Business Information Systems Learning

Learning Platforms - Shifts in the AI age - Part 1

noreply@blogger.com (Raji Gogulapati) — Wed, 10 Jun 2026 22:20:50 +0000

Check out the interactive map here at the url provided.

https://golden-cobbler-048ce9.netlify.app/

Platform Business Models and Strategy - a few knowledge sources

noreply@blogger.com (Raji Gogulapati) — Thu, 31 Oct 2019 22:05:00 +0000

Several authors and business strategists continue to provide insights, interpreting the emerging web-based business models. This note is not a complete treatise on the various themes taken up by the authors whose remarkable contributions continue to enhance the growing literature around digital platforms. The purpose is to introduce a few authors and their work to enhance the student and novice practitioner's knowledge in the area of digital platforms and ecosystems.

Alexandar Oswalder's "Business Model Generation", A handbook for visionaries, game-changers, and challengers stands out to convey the essentials in a simple visual format with a supporting online community. For starting on the journey of news on new business models, this happened to be a good book at the time of publication. Most of the models discussed are no longer a mystery even for the novice now. Yet, it falls in the must-read category not only for its appeal visually but also for the essential content nailed down early on. The building blocks for business model generation are still the standard, notes on patterns observed at the time continue to take on incumbents as challengers. For design, while the book offers a method to start, there are more options to consider – books and authors who focus on design thinking and methodologies.

Business Model Navigator by Oliver Gassman et al, is a must-read for both academics and practitioners. The focus is on the underlying themes and patterns of business models behind successful companies who stand against all odds. The authors emphasize how Business model innovation is the key to avoid drastic failures and ensure survival. Each of the patterns described comes with historical origins of the pattern establishing the time- tested nature, how it came to limelight in recent years, when and how to apply it followed by questions to ponder. This is a very systematic way of presenting business model knowledge with examples. It is a go-to toolkit to refer to for beginners and matured thinkers to navigate and prepare for tough calls. The other parts form the glue to the knowledge about business models -on how to drive and implement very useful to the business professional. The book is rich with templates, checklists, for initiation (analyzing your ecosystem), ideation (adapting patterns), and integration (shaping the business model).

Amrit Tiwana's Platform Ecosystems Aligning Architecture, Governance, and Strategy is a textbook- style with comprehensive treatment to the three areas. The principles and concepts are covered with such precision, the reader will keep referring back to the book for any vocabulary related to Platforms. The book is rich with several meaningful diagrams and figures for anyone attempting to go in-depth for practice and theory. Prof Tiwana addresses the traps that incumbents – both individuals and businesses can fall into with prior enterprise systems and their life Optionscycles.

The Business of Platforms is the latest book by Annabelle Gawer, Michael Cusumano, and David Yoffie. They command expertise among the academics and practitioners with their papers and earlier books where they treat the subject of platforms with the richness of product platforms of the Tech industry – from the mainframe and Microsoft days. The Business of platforms is a journey they took on with both a thorough understanding of the governing principles and concepts along with words of wisdom and caution on the traps and pitfalls of the complex environments of platforms. Their work is always supported by extensive research data and multi-faceted industry consulting experience. Differentiating innovative and transaction platforms, managerial decision challenges to combat with platform leaders,

Sangeet Chaudary's Platform Scale is a bold venture by a young entrepreneur who placed himself in the forefront of platform thinkers and thought leaders in the digital age. I followed Sangeet's blog mostly out of curiosity about how the young author weaves his thoughts to write a blog on a complex subject. Later, as one of the book reviewers, I have observed what it takes for a new author to form a hub of his own, enable direct and indirect network effects by the new audience of his blog in the Internet platform age. I also noticed the partnerships the author took time to grow and nurture to form a platform and an ecosystem of his own. Exposing myself to this venture reminded me of all the characteristics of the web as a network in structure as well as architecture one has to understand for conducting one's own business. Chaudary's book is an amalgam of current trends, rich with examples, simple to read and grasp the complex environment. Failures and risks come with experience – the author's work lets startups venture into the new green pastures of platforms with their basics in place along with a lot of enthusiasm.

Much of the platform growth lies in its careful orchestration aligning and not losing sight of the core principles and properties of the web as a network. The authors of today have it simple for starters, yet much more to manage to come to limelight. Chaudary is well known in the circles of platforms enthusiasts as well as followers of the platform world literature. His work recently is more focused on the regulatory and governance issues at the level of policymaking for nations in the area of platforms. The world economic forum reports and the United Nations Development programs rely on the ease of building platforms for their producers and consumers of the developing world. Platforms are becoming symbolic in presenting themselves as representatives of cultures and the souls of nations.

The edX course on platforms strategy is based on the book Platform Revolution by the Initiatives on Digital Economy (IDE) researchers at MIT Geoffrey Parker, Marshall Van Alstyne, and Sangeet Chaudary of the Media Labs at MIT. Geoffrey Parker and Marshall Van Alstyne are well-known IDE researchers, teachers, and contributors to the platform world and more. Their presence is felt by anyone venturing into the area of complex digital systems and platforms through their talks, articles, and courses they teach in Boston. They have helped initiate and grow the visibility of the digital platform summits at MIT annually. As an aside, major universities in the USA have executive programs and courses on platform strategies fitting into their respective curricula.

Matchmakers by David Evans makes reading and grasping the insights easier. I would put this book as part of the package for the beginner reader along with the above- mentioned books with an emphasis on market place/ transactional platforms. But the pursuits begin after the course/ or reading these books. They seem to cover quite a lot of the fundamentals, yet one comes away with the urge that-- a lot is missing too. For sure, they are material for introductory courses on Platforms. Also, the perspective is mostly from the consumer side, not much for incumbent platform leadership.

Simone Cicero of Platformdesigntoolkit is an active blogger and platform designer from Europe on platforms. His extensive efforts are focused on the design of platform ecosystems, societal as well as geographical ecosystems. He certainly seems to take the platform world to unlimited boundaries along with much- grounded design tips with his regular workshops and courses around Europe and North America. His work on explaining market networks, innovation, and digital value chains through his extensive blog posts has brought clarity to the digital design thinking and tools for the platform ecosystems world. Cicero's platform design team continues to provide valuable insights and patterns for platform design on their website in addition to their courses and webinars.

Linked by Barabasi makes Network Science research easy to grasp and fun to read. Carefully written by a scientist on the properties of networks highlighting both the seriousness and understanding networks. As a student of computer science, understanding layered architectures and networks has been a part of my academic pursuits. I started reading Linked with the confidence that I knew a bit about networks already. I would think Linked is the best way to reach the semi-educated Business and Computer science students and practitioners as the first book to read about the power of the networks. Without the grasp of hubs and communities, there is not much anyone can venture into the world of platforms.

Michael Wade's Digital Vortex

Velcro hooking is a catch-all used to organize around the architectures of the web. Everything is a link away – appearing to be strong and versatile, yet so fragile. Maneuvering through the links and entanglements of the web is not an easy feat for the frail.
When you decide to get to the digital train of thought, the train would have left the station. The path to digital is neither a line nor is it circular. Getting in and out of the train, at several stations requires buying new tickets and embracing new destinations faster, being aware of the arrival time of the destination. Michael Wade aptly used the term vortex. Vortex reminds us of nature's whirlpools explains Wade where everything is drawn to the center. The digital center of a Digital vortex is where the maximum amount of disruption takes place. While the theme is not about being drained in or engulfed by changes, but to get a grasp of the extent of disruption, navigate along with the chaos, collisions, and combinations that happen to lead to both convergence as well as emergence.

IMD Wade's article on strategies for responding to digital disruption around four themes - harvest, retreat, disrupt, and occupy. This skeletal content is expanded as a chapter in the author's book "Digital Vortex" with case studies, examples and questions for leaders. Emphasis is on the Occupy strategy seeing value vacancies as contested market opportunities. The author's LinkedIn article @https://lnkd.in/gcjUTTB is a good read on this.

Mark Skilton's Building the digital enterprise covers the essentials of thinking digital.
The 25 case studies cross-cutting all industries, the well worked out diagrams and notes give pointers to embark the journey of starting anywhere with a clear compass for digital. In the chapter on techniques for building effective digital business models, useful approaches are provided with the digital workspace model notation that Skilton developed. Skilton discusses the evolution of capabilities and strategies in industry contexts. Exploring mechanisms to derive value with new thinking is the challenge that Skilton takes on with an extensive discussion on industry-wide examples.
A sequel to this book is his work on Building digital ecosystems. I wrote a review of Mark Skilton's Building digital ecosystems architectures here

Here are a few URLs to online courses related to platforms and Digital business ecosystems:

https://www.coursera.org/learn/bcg-uva-darden-digital-transformation

https://www.coursera.org/learn/digital-competition-financial-services

A few related and relevant courses to understand the theories behind digital models in depth.

An Introduction to complexity course bridges the discoveries and history of sciences to computing. It is a relevant course to envision the digital capabilities, providing the background to what it is about going digital in any industry. Melanie Mitchell's book titled Complexity, a guided tour is a sincere effort to bring the several core principles behind complex systems in easy to navigate and confront the world of complexity.

https://www.complexityexplorer.org/courses/27-introduction-to-complexity-summer-2015

A many model thinker is both wise and practical to set his compass to confront and solve problems of a gigantic scale with unique and robust tools that meet the purpose. Scott Page's "The Model Thinker" provides the theoretical background introducing the models behind complex systems. When taking up complex challenges, systemic and complexity thinking becomes a necessity. Prof Page gives a solid explanation of why we need many models in his introduction to model thinking. To achieve a basis for realistic decisions in the world of business and policy, to be comprehensive in making statements. As models cover a broad spectrum of disciplines, they are useful to predict and act on complex phenomena.

https://www.coursera.org/learn/model-thinking

Research web sites:

SSRN

ResearchGate

MIT CISR

World wide initiatives, symposiums, and summits are beginning to happen in the platforms and ecosystems world. Stay tuned as the theories and practices evolve.

A few fundamentals on types of platforms and ecosystems

noreply@blogger.com (Raji Gogulapati) — Thu, 26 Sep 2019 20:18:00 +0000

Ecosystem concept captures the idea of the coming together of firms with shared interests and goals to provide customers with a solution. Business platform ecosystems establish inter-firm relationships with the links for products and the surrounding services or complements. They usually take on the shape of a hub and spoke model of a network. The platform hub (owner, leader, sponsor) is at the center with the array of firms connected via technologies. Few of the members of this model of the ecosystem take on the governance aspects for the members of the ecosystem. Firms in the ecosystem deal with complementarities (essential core and the peripherals for example) in a fashion that seamlessly interoperate. For a short read on the paradigm shifts from a business ecosystem to a digital business system, I suggest reading the research article here.

There are two types of platforms and a few in between. There is scope for innovation on one type of platforms. On the innovative platforms, the base provides the infrastructure to create value thru complementary products by third parties. Google Android, Force.com falls in this category. The Android ecosystem is enabled by developments around the APIs. When engineering and technology building is primary to the culture of the business, the platform definition is explained by centering the infrastructure layer.

The other type of platforms is the transaction type of platforms. Here, there is an exchange of goods and services as in Amazon marketplace, people interactions as on Facebook. Salesforce, Apple, SAP fall in the in-between category where the emphasis is on a combination of product and platform. For a more complete treatise on the platform business models, read the section in chapter one “Platform thinking” in the book Business of Platforms.

Two-sided platforms and multi-sided platforms are yet another way of viewing platforms based on how exchanges happen on the platforms. In a two-sided platform, the exchange happens between two parties – customers and providers. In a multisided platform, providers cater to groups of interdependent consumers. Network effects come into play to grow the platform value.

Characteristics of Ecosystems

Ecosystems definitions highlight the design thinking of purpose behind the formation of hubs and links in a network. When the interactions of partners or complementarities are dominant in the design, the definition takes on as you find in this article here.

Modularity with loosely coupled components and self-organizing/ optimizing capabilities are the dominant characteristics of ecosystems according to the definition of ecosystems here.

Modularity enables the conditions for the complementors of the ecosystem to co-ordinate and emerge with ease creating sub-ecosystems around the hubs. Samsung and Motorola thrive on the Google Android hub. The relationships are unique, governed by standardized roles and rules. The extent of coordination and co-specialization needed among the members of the ecosystems determines how the complementors of the ecosystem are aligned for production and consumption. Open-source software Android, and Nike wearable devices are both ecosystems – the underlying mission is for production in one case, consumption in the other.

These characteristics in the definition of a digital business ecosystem show the open relationships of partners in an ecosystem with changing roles based on the demand. Ideally, the links of relationships are subject to change giving the ecosystem capabilities to organize itself over time releasing from the tensions of authoritative hubs. This is a fundamental shift from the traditional supply-side “pipeline” of resources to meet the customer’s demands.

Dimensions to Ecosystems:

Several dimensions to ecosystems come into play for ensuring engagement and interactions, building relationships with the partners of the ecosystems.

The degree of openness is valuable to determine the strategy for the platform/ ecosystem owner and stakeholders. With many moving parts, openness comes in many flavors. The levels affected can vary from determining choices at the architecture, marketing, standards compliance, IP management, and governance for managing boundary resources. The practices impact the roles and relationships of the various participants of the ecosystems.

For a grasp on how the various dimensions such as the degree of openness, engaging and collaborating with participants, tie strengths and relationships among the participants, read here.

For understanding the glaring differences of the traditional and ecosystem/ platform models and interpretations for management practices, read here.

Capturing Value:

It should be clear where the main or blended value is rooted in the business model. Cost, Experience, Platform values are often thought when it comes to considering value. Each of these value types is dependent on the type of business model adopted. Successful businesses adopt a variety of value deriving models combining with their business models.

Low cost/ No cost provisions are a norm when the business model enables producing one or many with no cost differences. Often, the customer owns a huge chunk of the product with very low or cost-free.

Customer experience has often been associated with retail – chasing the customer all through the journey. An entire customer relationship management system is often associated with ensuring customer empowerment, recommendation systems, and personalization of products and services.

Platform value-driven business models are quite varied. Communities are often the channel for distribution, communication or executing for a specific cause. Think Twitter, Facebook, Reddit here. Connecting and enabling group/ peer to peer dynamics is the strength of Digital market places. Transactional value is exchanged for buyers, sellers for a specific purpose in a market place. Airbnb, Etsy are good examples of digital market places. Ecosystems enable inter-connected firms to co-specialize, co-market, or co-create by leveraging resources. Think Google Android and Apple iOS for ecosystem value. Data orchestrators operate with real-time data to obtain analytics and recognize patterns for decision making. Think companies like GE, Cisco here.

Strategies for thriving with new value creation, discarding the old.

Ben Thompson’s Aggregation theory throws light on how disruptive business models take the internet advantage while designing for value. Fundamental shifts in capturing value have happened in the internet age. This is due to the ability to modularize components of the system of suppliers and buyers resources to provide consumers the best experience. Distribution of digital goods has found a free medium through the internet. There is direct access to the large numbers of consumers for personalized experience and value delivery. The value is delivered free via the internet by aggregating the modularized resources to the consumers.

For competitive advantage, think how your business models can seize value, fill the existing gaps in value delivery and keep it for a slightly longer time. Sounds harsh while diving deep on digital disruptors when considering competitive strategy. DBT's Prof Michael Wade defines value vampires and value vacancies. Value vampires squeeze and drain the value out of the incumbents. Filling value vacancies to exploit the digital opportunities sound kinder than the vampires. Beware incumbents on the aggressive pursuits of the disruptors who wipe away traces of the incumbents to the past! For more on this, read the post here.

Conclusions:

There are plenty of resources to define and interpret what goes into platforms and ecosystems design thinking. Organizing the thoughts and workings of your business units and stakeholders is a major part of the understanding behind ecosystems/ platforms. No one’s views can be neglected, not a single point of view can be insisted. The journey is experimental and focused on the short and long term strategy for winning and meeting the customer expectations of your business.

Platforms & Ecosystems Metrics - An Overview

noreply@blogger.com (Raji Gogulapati) — Thu, 19 Sep 2019 17:55:00 +0000

Platform ecosystems metrics are discussed by researchers and authors broadly from two angles

- - evolution of platform ecosystem

- - economics of platforms

Their contributions are based on their interests, backgrounds, and advisory experiences

When it comes to mainstream practitioner perspective, there is much to grasp, tradeoff, along with an eye on the effects of interdependencies. Design and Engineering of platforms processes are not yet standardized. This means, to churn out platform development efforts as easily as other business systems entrusting the in house IT departments to fit in their agendas of the enterprise architects do not seem to be popular in the immediate future. Perhaps, it will never be because of the nature of platform development.

Platform Ecosystems' author Amrit Tiwana and Platform Revolution authors Parker et al, have contributed extensively from these two angles.

Tiwana’s emphasis is on the shift in thinking necessary for those familiar with the software engineering methodologies for the business software systems. He clarifies the differences behind evolution metrics and implementation performance. For the business software architects, the term evolution can be seen as functionality/ feature expanding of platforms. Not only this, how well the system is fit for the future for long term survival and winning. While discussing evolutionary metrics, the corrective approaches and review backward is not the emphasis which is central to the themes of software systems maintenance.

Parker et al highlight the important metrics are those that quantify the success of the platform in “fostering sustainable repetition of desirable interactions” with a goal to generate positive network effects and creation of enormous value for everyone involved, including users, sponsors, and managers of the platform. The discussions on metrics from this angle does not mean that system implementation metrics from the engineering standpoint, the business process efficiencies, and system-level enhancements are not important – they are assumed. Everything discussed by the authors is on TOP of existing understanding of systems development thinking. More caution must be exercised due to the complexity as well as the mistakes made by chasing the wrong metrics can result in undesirable outcomes at a rapid rate.

The emerging role of ecosystem architect who caters to the needs of platform development beyond the system architects – whether business or technical. The system architect and the business architect bridged the gaps and laid the foundation for the various business software systems we see in operation today.

The article Emerging Role of Ecosystems architect by James Hedges and Andrei Furda stands out in my readings on the role of the ecosystems architect in designing platform ecosystems. The article contributes to an extensive literature survey on ecosystems discussions – approaching the ecosystems from a customer and technology perspective. The authors of this article emphasize on what constitutes interaction success throughout the various stages of the platform. They laid out a table of metrics for platform/ ecosystem combining Tiwana’s metrics for evolution (resilience, scalability, and composibility in short term, structure, platform synergy and plasticity in the medium term, and envelopment, durability, and mutation in the long term. Parker et al metrics focus on interaction success and reflect the reality of the business from the user’s perspective. They track evolutionary stages of the platform. These include “liquidity, matching quality and trust during the start-up phase, size of portions of the user base, lifetime value of producers/ consumers and sales conversion rate during the growth phase that can drive innovation and identify strategic threats from competitors during the maturity phase. Having a handle on the interdependencies of metrics requires in-depth understanding and tracking in the changing contexts and environments.

I now turn to Network metrics. A sample of the interrelated areas for metrics can be seen in this blog article – “16 ways to measure network effects” broadly categorized as – acquisition, competitors, engagement, marketplace, and economics related metrics. Diving deeper – the metrics turn out to be 16 of them. In your respective business contexts – there may be many more.

Conclusion:

It is clear that treading the waters of platform/ ecosystems metrics is about tracking complex metrics characterized by evolution, dynamics, and uncertainty. Watchful strategy, governance, and adherence to the discipline of tracking what is working and what is not is the way forward for metrics.

The promise of technologies, Big data, Analytics, and AI algorithms is to provide with tools that can far surpass human intuition and inference. This promise is both an asset and a challenge to what lies ahead for platform thinking and decision making on metrics that matter.

References:

http://platformed.info/platform-metrics/

Three among the many short term/ startup phase, interaction focused, user perspective metrics to be considered by an ecosystem architect - Liquidity, Quality of Matching, and Building Trust. Sangeet Choudary's @https://lnkd.in/gH_b2EX

https://thenextweb.com/entrepreneur/2013/05/04/how-to-win-with-marketplaces-the-three-success-factors/

https://www.researchgate.net/publication/324942808_The_Emerging_Role_of_the_Ecosystems_Architect

platformmetricsare dynamic and evolutionary by nature. Software and market places come closest to the journey with platforms. Still, if one goes by focusing only on traditional enterprise software engineering metrics - the software ecosystems/ platform engineering metrics are quite different. Similar issues if the focus is only on market place perspectives. Here is a read on metrics for network effects - to grasp the range and coverage on just one area - network effects, categorizing them broadly "acquisition, competitors, engagement, the marketplace, and economics-related" metrics. @https://lnkd.in/gkb83Rz

https://a16z.com/2018/12/13/16-metrics-network-effects/

https://medium.com/apis-and-digital-transformation/using-metrics-to-measure-api-driven-ecosystem-value-c0d334bad69b

https://innovationmanagement.se/2016/09/29/metrics-open-innovation-ecosystems/

https://www.researchgate.net/publication/267271540_KPIs_for_Software_Ecosystems_A_Systematic_Mapping_Study

https://www.slashdata.co/blog/a-game-of-ecosystems-measuring-ecosystem-performance

https://www.hellosign.com/blog/evaluate-digital-platforms

https://drive.google.com/file/d/0B36EfI3U2bSMbkdsSmFMbVY3WDg/view

https://www.pointillist.com/blog/uses-of-artificial-intelligence-to-boost-customer-experience-measurement/

Additional Readings:

https://www.researchgate.net/publication/310791758_New_Digital_Metrics_in_Marketing_A_Comparative_Study_on_Social_Media_Use

https://www.researchgate.net/publication/319485746_Are_You_Using_the_Right_Mobile_Advertising_Metrics_How_Relevant_Mobile_Measures_Change_the_Cross-Platform_Advertising_Equation

https://www.gartner.com/smarterwithgartner/how-to-measure-digital-transformation-progress/

https://knowledge.insead.edu/blog/insead-blog/digital-businesses-the-metrics-that-really-matter-6731

Designing boundary resources for platform ecosystems - An overview

noreply@blogger.com (Raji Gogulapati) — Sat, 24 Aug 2019 18:33:00 +0000

From the ancient times of emperors and empires, protecting geographic borders to secure them inside, and opening paths to connect with the outside have been the fundamental methods to both establish and continue to enjoy monopoly and expansion of power. There were alliances and disputes which were witnessed by the public. There were punishments and rewards which became lessons of history to the future. These were perhaps not the only motives. There were social, cultural, political, and economic reasons behind the several strategies implementing these primary goals. With power and a holistic vision, they strived to build empires that lasted the tests of time.

In the current trends of digital platform ownership and ecosystem building, platform leaders face similar situations to guard their ownership as well as play hard to meet their expansion goals along with winning the competition. Observing the modern platform monopolies, friendly dictatorship approaches seems to work for platform owners. In the world of digital platforms, transparency is a characteristic of platform ownership, even if not declared as open. There is also an ease of maneuvering through the challenges practicing and playing time tested theoretical approaches originating from game theories as well as applying multi-modal thinking. It becomes necessary for the leaders as well as participants to handle boundary resources with a command on the dynamics of the platform ecosystem in both the areas of platform competition moves and innovation initiatives. Whether the platform is in startup stages incubating from an incumbent business or starting on its own also matters.

For incumbent businesses, recognizing that platform thinking differs from creating more of the same or optimize for better results thinking is the first step. Next comes connecting the life cycle stages of platform building to both strategic and innovation initiatives for inside and outside the platform with a grasp of the consequences and effects on actors associated with the platform. Having a command on the core concepts and guiding principles behind the platform ecosystems evolution is a must to prepare for the complexities that arise every step of the life cycle stages of platforms.

For this write-up, I make use of the work of several researchers in the above-mentioned areas. Significant are Annaballe Gawer's work on what drives shifts in platform boundaries, A Research Framework and Synthesis of Game-Theoretic Studies in the area of platform competition by Angel Salazar, Amrit Tiwana's work on the core concepts and guiding principles for Platforms and Ecosystems.

Platform envelopment, multihoming, and forking are most commonly connected to the boundary resources management in the commonly seen writings on Platforms. Platform envelopment is associated with co-operation among platform leaders and their complementors entering into target space with bundling strategies to leverage resources for a common user base. Envelopment promotes expanding from within for economies of scope as well as power over competitors. Forking, on the other hand, is an exploitative strategy. The host platform is exploited from its core competencies to spin off a new competing platform risking the user base as well as the significant resources and assets. Forking is enabled by openness in digital platforms. Google's Android operating system is an example of an open digital platform and has been subject to forking by competing platforms like Amazon ( as in the case of Android operating system forked by Amazon's Fire OS). Multi-homing is a side effect of compatibility as well as forking. This strategy is found common among app developers who can gain from building their apps on multi-platform scenarios. Reaching a consensus, balancing the app developers independence with the integration methods adopted by the platform is a matter of design for platform evolution to avoid the "see saw" problem. Modularity of the components of the platform determine how far the "Humpty Dumpty" design principle is violated - with the whole platform system falling apart if a component is missing or lost a part of its functionality.

Any professional studying platforms can easily grasp the above concepts. It is also relatively easy to figure the motivations behind certain competitive, co-operative, and non-co-operative moves. Broadly they can be categorized under harnessing their power with strong fence building methods along with seeming openness. Knowing that power or effectiveness or simply preserving their unique identity, may be the goal of consolidating boundary resources, it is easy to figure the individual models and strategic games that contribute towards winning. The challenge is to figure out the appropriate models and strategic games played in steps and stages by platform leaders to place themselves in a consistently winning position. For sure, not everything is fair play for everyone experiencing the effects of the platform directly or indirectly. The pricing, subsidies, bundling are all subject to change and asymmetries. Looking for patterns may be as challenging to find the needle in the haystack if not delved in-depth getting over the myths of independent product evaluations in independent markets in platform contexts.

One of the keys to this challenge is to get a grasp of the core concepts and guiding principles behind platform ecosystem building, evolution, and emergence. This challenge is about trying to grasp the influencing capabilities similar to nature's ecosystem building that happened over centuries of time. But digital platforms can show the ecosystem effects faster than in the real world businesses, responding to feedback mechanisms. This can be viewed as an advantage at least to establish some basic patterns of platform owners moves although not efficiently counteract or predict well in advance.

Strategic games are appealing as the outcomes can be rated and are based on interaction encounters among rational players. Divide and conquer, weakening the opponent and strengthening the ally are strategic games that appear to be rooted in common sense. Designing pricing and freemiums, which side to subsidize, how to figure the way when trying to share ownership and control and many such issues are quite complex. Cooperative and competitive games handling the cross-network externalities requires estimating and considering effects on cross-side participants and consumer issues. How to look for win-win potential, how to back out or compete require strategic thinking at a large scale.

There are deliberately played power challenges and games based on time tested models to counter those effects. Figuring which challenge to counter by applying the appropriate strategy, considering the several key issues that can be dormant requires applying methods that work in the context. The common ground for relying on mathematically and experimentally supported models is that they are measurable and simulated. With time, technologies forming the infrastructures change, the standards by which a platform effectiveness are raised resulting in the red queen effect (trying to race also appears static and standing). There is a tendency to settle for an equlibrium state towards the middle. Many governance mechanisms come into play under the umbrella of Goldilocks rule. As players increase, challenges for platforms increase exponentially. The winners are those who have built the capabilities investing their best in many facets.

References:

Platform Ecosystems, Aligning Architecture, Governance, and Strategy

By Amrit Tiwana

(Chapter 2, Core concepts and Principles,

Chapter 10, Evolving a platform)

Exploiting and Defending Open Digital Platforms with Boundary Resources: Android’s Five Platform Forks

URL: https://pubsonline.informs.org/doi/abs/10.1287/isre.2018.0786

Intra-platform Envelopment: The Coopetitive Dynamics between the Platform Owner and Platform Complementors

URL: https://www.semanticscholar.org/paper/Intra-platform-Envelopment%3A-The-Coopetitive-between/0dc3ad6dd8fa88b99ff696cc41475040415d9620

https://hbswk.hbs.edu/item/platform-envelopment

Friends or Foes? Examining Platform Owners’ Entry into Complementors’ Spaces

URL: https://onlinelibrary.wiley.com/doi/epdf/10.1111/jems.12303

What Drives Shifts in Platform Boundaries: An Organizational Perspective

Annabelle Gawer

https://medium.com/regen-network/cooperative-games-and-the-ecological-data-economy-df993338cdff

Platform Competition: A Research Framework and Synthesis of Game-Theoretic Studies

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2565337

The origins of the modern ecosystem:

https://medium.com/on-the-horizon/the-origins-of-the-modern-ecosystem-69b2eb96d309

Explaining Platforms

noreply@blogger.com (Raji Gogulapati) — Sat, 17 Aug 2019 00:47:00 +0000

Platform means different interpretations in different contexts. Adding the digital context is part of addressing the questions that arise on what platforms mean for businesses. While beginning to figure out what platforms mean in the context of the business, several challenges arise. Practitioners come up with at least a couple of perspectives for drafting platform strategy for business – A business model perspective and technology infrastructure perspective.

News on platforms goes with mega-success stories and impact at the global level. Such stories inspire aspiring business professionals to take on the journey in whatever capacity they think feasible. The impact of the rapid development of technologies that support the platforms dreams increases the tendencies to have ambitious goals around the new approaches to growth, achieve scale, and find new avenues for value creation.

In established businesses, the challenges are two-fold to the professionals involved in making a plan for embarking the platform journey. First, the leaders may not be well prepared to lead people with the initial plethora of questions that arise. For this, the leaders must understand that expertise and research in this area is evolving. There are many experimental approaches which can prove a success/ failure in their specific context. Second, the challenge for the aspiring professional is not to be overwhelmed on the abundant success stories as well as the lack of models to fit their business context.

Look for unifying frameworks while paying attention to the glaring and subtle differences. Get a grasp on the fit for the context. A social platform can be a Facebook as well as YouTube or Twitter – completely different business models. A development platform like Android operating system, Salesforce along with its market place is quite different with its traditional counterparts of open-source Linux or Windows Operating systems. Both Uber and Airbnb are market place platforms bringing new options to consumers affecting their day to day lifestyles, but their community-building approaches are totally different. What is common among these is all these platforms have an “Interaction first” theme around their design. They are connecting producers and consumers in new ways using digital capabilities with little initial investment. They are plug and play business models with highly modular artifacts enabling the quick evolution and scale with less hierarchical intervention.

In Sangeet Choudary’s platform stack , there are three layers to explain platform – the infrastructure, market place, and data. Each of the platforms we hear about has invested in at least one of these layers differentiating themselves with new and often disruptive designs to create value. Some practitioners explain platforms around their journey towards building platforms from their current business models and content distribution mechanisms. This approach is more common with the incumbents who are facing the trends of platform businesses.

The disruption is clear when we hear the need for regulation when the impact is on the health of the societies. Caution with governance mechanisms is required to avoid competitor’s exploitation in the name of competitive advantage. Governance and regulation must be meaningful for the platforms. Platform designs take advantage of the presence of competition. There are several instances of how traditional boundaries are crossed in the virtual, digital world of platforms – often discovering traps and pitfalls leading to better orchestration of the boundary resources as a remedial strategy.

Significant academic and market research contributed to design choices and frameworks around the basic questions on design and strategies for platforms. The fundamentals of design frameworks include topics about architecture, value creation, governance mechanisms, and competition. For starters, read here. The authors bring to the table several easily available resources, mostly research articles to chart out the design issues around an established design framework. A crucial requirement is to get the sponsorship preparing for distributed leadership. Everything on the web is public and open, backed with steps toward building trust and reliability in the digital networks. What works on the ground is traditional. Much needs to be thought ahead on reputation building on the web.

There are a few significant principles around networks that successful platform makers achieve. These principles are the DNA of the platform makers themes in everything they do whether it is carefully evolving the designs or preparing for the emergence of their platforms. For thinking in networks, read Linked carefully. On the author’s website, there are downloadable chapters too. This book helps understand how to envision the seemingly random phenomena around major events that happen due to the nature of the web-based frameworks. The impact of web-based networks is borderless, spread across societies and businesses. Complexity leading to chaos is an inherent property of large networks if not managed and strategized well. For many professionals, careers and contributions seem to take the “red queen effect”. To avoid stagnation and cautiously embark the journey, the new rules of the business games can be understood with an eye on the happenings on networks around.

Platforms and ecosystems are often associated with the digitization initiatives in the company. The Digital is often associated with all the newly evolving technologies in the Internet era and also their convergence – Social, mobile, cloud models built around networks using big data, AI, machine learning, collaborative applications. Disruption and transformation are the big themes around digital. Going Digital includes preparation and building capabilities for the internet age. incumbent businesses must be clear about the impact of the newly evolving technologies and business models on their business value proposition to make the right decisions at every step of the journey.

Finding a place in the more complete ecosystem dynamics is associated with their business models and several early decisions. We will discuss a few fundamentals about ecosystems in the write up on Ecosystems

Web 1.0, Web 2.0, and what followed

noreply@blogger.com (Raji Gogulapati) — Thu, 15 Aug 2019 20:23:00 +0000

Two and half decades since the arrival of world wide web, most businesses are continuously experiencing the profound impact of new, digital web entrants. The new may not have become equals or counterparts to the old, but their positioning in the web-based business world seems to be quite unique and challenging. Notice the business models behind them that make this kind of entry possible. It would be a grave mistake to view the new, similar, yet thriving businesses in your industry and try to become one without undergoing the experiments and strategy exercises in your own context. Appearances from far can be deceptive.

Popular business models described by experts since the early web days support the above statements. Some of you reading this post may be recalling your own journey with the expert insights and the interpretation of the business models.

I think it was in the early 2000s, Prof Michael Rappa in his open course, Managing the Digital Enterprise, consolidated the new digital business models, coming to light by then. I guess at this time, E-commerce and Internet commerce did not make much of a difference yet in people's lives. Web-based businesses seemed to provide a channel for transactions with a focus on presence and reaching out using the web as another window for the business to serve customers from a distance.

Prof Rappa stated that internet commerce is sure to give rise to new business models. Amazon made it to his business models list as a Brokerage model and was described as a hosting service, a virtual mall, or a market place. Amazon also appeared as a virtual merchant in the merchant model. PayPal and eBay appeared as transaction and auction brokers respectively.

In the Advertising model, Google took the place of both query-based paid placement and content-targeted advertising. Craigslist was discussed as an easy to list (listing service) alternative to existing yellow pages/ penny saver/ even newspaper advertising.

Apple iTunes Music Store duly took its place as a Bit vendor under the merchant model category.

Wikipedia (open content) and Open source (software development) made it to the community models. Flickr was part of the social network service model. Netflix was the rising subscription service model.

In the university classrooms, online courses started out as replacements to distance education, scaling up their existing offerings. MIT open courseware became the pioneer in opening up world-class educational content reaching out to millions of aspiring students and life- long learners across the globe. Online learning systems resembled the uploaded versions of on-ground classroom-based learning. The e-mail was getting popular with people outside the business intranet mail systems.

Tim Oreilly came up with the article, what is web 2.0 along with comparisons of web 1.0 and web 2.0 businesses. The article is more than comparisons. He brought clarity to some powerful concepts, a few core principles and practices around web 2.0 technologies and business models, strategically positioned as a platform, gravitating towards some core competencies "forming a veritable solar system, global brain (with the mental chatter as the blogosphere)". Next, he mentioned "hyperlinking is the foundation of the web". The lesson from collective intelligence is that “network effects from user contributions are the key to market dominance in the network era”.

Talking about blogging, he highlighted the importance of the RSS technology that is significantly behind the database-backed web/ “incremental web” / “live web”. This technology allows for subscribing to changing the content (syndication) giving way to the weblog, not just a webpage. The fundamental framework of web 2.0 (see web 2.0 meme map) is listed in this article – covering the data-centric nature (raising debates on who owns the data, the new types of databases), end of the software release cycle (leading to software as a service, perpetual beta), loosely coupled lightweight programming models (think user participation), software adaptable on multiple devices, anytime, anywhere user interaction model and leveraging long tail, a global phenomenon (masses contributing with their own content) , adoption of RIA (rich internet applications) with dynamic content. These principles backed by the Web 2.0 based technologies were shaping the new web, which have now become a common practice in modern enterprises.

Enterprise 2.0 by Andrew McAfee emphasized the value of Emergent Social Software Platforms along with varying tie strengths among people. While listing all the benefits of making use of the collaborative, social, aspects of software (a big shift from web 1.0 to web 2.0) McAfee highlighted self -organization of the social networks shaped by its participants with the ability to build large communities without a central authority or guidance. The self-organization ability is paving the way to large business ecosystems in today’s digital world. Several of nature’s laws are penetrating into large enterprises requiring better and relevant ways of managing the ecosystems. Dion Hinchcliffe made significant contributions to the writing on the web and business models. My favorites are all of his diagrams with such clarity, enhancing the visual appeal, not missing the detail.

Several researchers and practitioners continue to predict and write about the evolving web trying to fit into a few paradigms (semantic web, mobile web, cognitive web, web 3.0 and even web 4.0), compared with the previous (?) more popular trend. This approach may lead to an understanding of a few, specific, characteristics of the web and drivers that enable those characteristics, but they can only hit the self- imposed barriers and limitations of the larger view of discussions sooner or later.

It is getting clear that there is no stopping to the continuous revolution the web is weaving. Yet, some of the trends seem to be taking back seat compared to some others. Interpreting the trends has become a relevant and serious practice to predict which trend would overtake the other.

Convergence, disruption, and innovation to create a parallel virtual world have become the norm with the backing of ever-growing technologies, business models, and strategies. The major themes of technology-based innovations are around Artificial Intelligence, Big Data, Analytics, Internet of things, and Cloud computing. The business models in the limelight are around social networks and web-based platforms. Platform strategies are framed from perspectives from engineering and economics by researchers and practitioners. To try to contain the web world in a few themes is something similar to containing the magic of the genie world figuring who is touched and not yet touched by the magic wand and what they became after...

Mary Meeker’s internet trends, IDC report (s), World Economic Forums Reports on Platforms and Ecosystems, and writings of the several, world-wide consulting firms have become resource libraries to get a handle on the business practices on the web. The web trends are giving way to clear vision of aligning with the omniscient, omnipresent web for each and every country, business, and the active, individual participants forming several universes.
Forming hubs and communities has become a goal for anyone associated with the trends of the web in the all-pervading social media and networks. This could manifest as an attention-drawing, self-promoting scheme for an individual who can ride the tide the social activities demand. Social media activities require time and skills to become the genuine master of the new and trendy social media skills that the web era demands. This trend can also be viewed as a positive force for those who would not have voiced their opinions and make a presence for themselves along with sharing their views. Still, more importantly, trying to name a trend and getting on the bandwagon is not the approach for serious business practitioners.

Full of paradoxes, the web has come a long way, far from the times when Nicholas Carr’s IT doesn’t matter(2003) debates on the Information technologies were causing concerns. The programmers and professionals of the early Y2K’s may have disappeared into the oblivion or morphed to creations of their own making, but the web world has not been stopping for anyone.

There will be a follow-up post to highlight the significant contributions of authors and researchers in bringing clarity to the ever-growing business models of the web.

References:

https://www.researchgate.net/publication/37408327_Internet_Business_Models_and_Strategies

https://pdfs.semanticscholar.org/1355/ef6bbbf82d6ab31ba1c3a02c6ac70439c4f5.pdf

https://www.flickr.com/photos/36521959321@N01/44349798

https://www.oreilly.com/pub/a/web2/archive/what-is-web-20.html

https://dionhinchcliffe.com/category/the-web/web-2-0/

https://conferences.oreilly.com/web2summit/web2009/public/schedule/detail/10194

http://radar.oreilly.com/2007/10/todays-web-30-nonsense-blogsto.html

https://www.forbes.com/sites/juttasteiner/2018/10/26/what-the-heck-is-web-3-0-anyway/#176eb2086614

https://hbr.org/2015/06/a-better-way-to-map-brand-strategy

https://hbr.org/2007/11/mapping-your-competitive-position

A few references on web 3.0 and web 4.0 – based on practices observed.

https://pdfs.semanticscholar.org/73dc/041c26998ee182c214a3dfa428ec491fc14c.pdf

https://www.researchgate.net/publication/324537592_Web_Technologies_From_Web_20_To_Web_40

https://pdfs.semanticscholar.org/8cb3/93c3229e8f288febfa4dac12a0f6298efb93.pdf

https://www.linkedin.com/pulse/20140324055730-14091619-effects-of-web-3-0-in-the-new-digital-world/

https://medium.com/fabric-ventures/which-new-business-models-will-be-unleashed-by-web-3-0-4e67c17dbd10

https://www.researchgate.net/publication/259875366_E-Commerce_Business_Models_in_the_Context_of_Web_30_Paradigm

Co-opetition - Cooperate and still compete

noreply@blogger.com (Raji Gogulapati) — Wed, 22 May 2019 19:50:00 +0000

Much has been talked about cooperation and communication in the internet era in schools, colleges, and workplaces. Many professionals recognize that there are several differences in how students are taught about collaborating in teams in the classroom, how employees of a firm operate in teams. The dilemmas about how a business operates in day to day operations as well as how strategic decisions are made in the interests of stakeholders (internal and external) are not left out from debates either.

Collaboration has limited scope for business activities, especially when faced with specific challenges (both internal and external) at the organization level. Collaboration is a concept and a form of synchronous and asynchronous engagement at multiple levels, ranging from countries and firms to people observed in all areas of work both large and small scale, since ancient times. The objectives of engaging in work to achieve a common goal and collective outcomes reveal characteristics of collaboration that include nature of ties, timeless and transferrable skills of people, processes and more recently, use of evolving technologies catering to the nature of work and context.

Gaining competitive advantage is also limited in scope when faced with challenges of context-related innovation to grow from the current to the future. Probing into decades of evolution of how organizations and firms view competition deriving advantage reveals how competitive strategies have evolved starting from Porter’s competitive advantage theories. While finding value and differentiation among products to compete is focused on external environments, developing core competencies being process-oriented has been the strengths of firms focusing internally. The inside view of competition brings in seriousness and reflection oriented methods to deliver superior approaches to maintain the health of the organization.

Collaboration and competition separately have limited scope for the context of business activities and strategies when faced with dilemmas of the interest of the stakeholders.

A part of the answer to challenges that incumbent businesses on where and how to find the next growth space (and a bunch of products as a result) are in creating and forming competing teams for future growth strategies from within first. There are theoretical frameworks that back this argument, see “complex network coopetition strategies”.

Co-opetition is not about collaboration or competition perspective but a balanced combination of both perspectives to find solutions. And yes, it is common for large enterprises to view their internal business units as competing for business units. Why not be truly competitive while cooperating partly and competing effectively?

Keeping the teams separately does not get them anywhere because of their clear differences. Merging them will not get instant solutions either. On the other hand, competing with ideas to get grants for research and development projects is quite common in business and academic organizations. This approach indicates a healthy competition from inside to get the best out.

First, care must be taken to form the challenge teams for growth strategies with a mix of talent in a large organization. I recommend hungry talent, those aspiring to become new architects of the future organization. This includes talent from research and development, engineers and developers, users, marketing, and strategists, and other stakeholders. They must think beyond their current stakeholder mix too and redefine their new possible stakeholders - starting with the new organization talent from within.

Next part of the answer lies in the lessons from the Netflix grand prize challenge that happened around 2008/ 2009 by putting forward their current knowledge base and asking outsiders to figure out the best recommendation algorithm. A large enterprise has a tremendous amount of scattered talent pool. They need not go outside but must be willing to share knowledge for enabling competition within their newly formed challenge teams. The teams must be able to interact on areas carefully crafted to let innovation happen with freedom, yet keep the competitive spirit and motivation to participate.

Netflix grand prize case/ discussion is valuable for the way the teams were managed by adopting rules and conditions for “free revealing and knowledge brokering" that are quite unprecedented in firms. Discouraging opportunistic behaviors and managing the teams through the life of the various phases to progress from inventing to innovating, demonstrating value is not easy. Research on Netflix grand prize team management case is available extensively - starting with the famous NY times article for light reading. Many team collaboration researchers have contributed to insights and scenarios on forming collaborative and still competing communities.

Yes, such teams can sound redundant when talent is viewed as individuals contributing to uniqueness. Because these teams are from within, working on similar projects appears a waste of resource initially. But the ideal mix creates the syndication at micro, meso, and macro-level for the dimensions of knowledge and economic value creation. In fact - such strategies must be viewed from multiple dimensions of value creation at the various levels and layers in organizations. In the article on co-opetition strategy (read here), the researchers demonstrate “matrix representation of coopetition for value creation”. This includes partial cooperation as well as competition from within and outside firms.

Netflix is not the only one that experimented in such scenarios of competition and collaboration among communities. Such co-opetition existed from ages in industrial districts of Italy in traditional settings. During the ‘80s, the Japanese car manufacturers could beat US competitors with such strategies. The LCD TV market has been dominated by the Sony Samsung resource-based strategic alliances, yet competing for customers head-on.

The ICT standards happen to bring together many competing firms' approaches towards hardware, software and communication technologies. The alliances among airlines is a very common example for models of co-opetition for efficiency and resource utilization. For many such examples, and a detailed case study on how Amazon consistently uses coopetition based business models - in its marketplace, web services, and content platform (kindle), read here.

Creating and capturing value based on co-opetition business models is the theme behind these endeavors. Understanding which areas of business practices and models benefit by collaboration and competition is the foundation for the drivers of co-opetition models. Inter-firm relationships and networks can be oriented towards a “positive-sum game”, win-win situations, and positive network externalities.

References:

https://www.researchgate.net/publication/238333593_Competitive_Advantage_Revisited_Michael_Porter_on_Strategy_and_Competitiveness

https://www.nytimes.com/2009/09/22/technology/internet/22netflix.html

https://hbr.org/2014/02/use-co-opetition-to-build-new-lines-of-revenue

https://repository.upenn.edu/cgi/viewcontent.cgi?article=1128&context=marketing_papers

https://pdfs.semanticscholar.org/0dc3/ad6dd8fa88b99ff696cc41475040415d9620.pdf

https://www.hbs.edu/faculty/Publication%20Files/platform_entry_review_07-2018_08bece62-de69-4e46-846a-b8c0233bb95e.pdf

https://sloanreview.mit.edu/article/unleashing-innovation-with-collaboration-platforms/

https://www.researchgate.net/publication/228215995_Economic_Networks

http://ilp.mit.edu/media/news_articles/smr/2015/56417.pdf

https://www.bcg.com/publications/2019/emerging-art-ecosystem-management.aspx

https://www.researchgate.net/publication/236343099_Coopetition_Strategy_Theory_Experiments_and_Cases

https://www.researchgate.net/publication/259505436_Coopetition-Based_Business_Models_The_Case_of_Amazoncom

Challenges of Balancing Hierarchy and Modularity in Platform ecosystems

noreply@blogger.com (Raji Gogulapati) — Thu, 11 Apr 2019 17:59:00 +0000

Hierarchy and modularity are the structural, organizing artifacts behind networks. Their impact is manifested as hubs and communities on the Internet. Understanding how they are formed, their characteristics, what factors facilitate and inhibit their growth helps knowing how technologies evolved and in turn how they shaped industries and organizations so far. Balancing the two seemingly paradoxical models has always been a challenge to any business organization and the ecosystem they take part.

The fabric of the organization of the Internet network is a cluster of nodes which is modular by construction at the base level. Modularity is measured by clustering coefficients which tells how interlinked the neighbors of nodes are. The distribution of the clustering helps understand the extent of modularity.

Highly connected nodes form hubs. Connected nodes are the connectors whose strength is measured by the number of links connecting them. This is a property of the structure of a network.

The property of modularity appears often in design discussions – whether it is computer systems design or a departmental functional design in business. The purpose is to shift the responsibility and ownership of independent actions without the intervention of the parental system owning the module. Most systems are built with the purpose of a few central themes and modules to start with. Complexity grows with building around the core, meeting the needs with scale and functionality.

A complex system - whether it is computer systems architecture or an organization of a social movement, or an operating business organization is part of an engineered structure of principles behind hierarchies and modularities. The fabric of the operating networks consists of several components operating independently as well as being facilitated by hierarchical structures and systems of people, processes, and technologies.

Several models explain that hierarchy or formation of hubs happen when clusters of nodes are linked to nest or are held together creating hierarchical clustering of modules - a generic property of real networks as well as the world wide web. For example, Granovetter’s model explains how people’s threshold values affect joining a hub for a social movement. It is difficult to predict which social movement takes off or which network will be successful in matching, segregating systems. It is not just the individual threshold values but also the group’s distribution of thresholds that matter. Airbnb and Uber are different platform businesses with their underlying operating network very different. Airbnb faced the complexity of getting people to list although there were many interested in renting. Uber and Lyft face a different kind network shifts due to the localities and people’s segregating patterns.

Observing how any complex system evolved over time gives a handle on what next for a complex system. Computer architectures - both hardware and software evolved due to inbuilt modularity. Mainframe Computers such as the IBM 360 started as hardware systems with successful attempts to separate software functionalities from the hardware. This enabled the software to run on several machines, giving way to software evolution and several devices allocated for specific functionalities. Modular approaches enabled partitioning tasks resulting in parallel computing, coordinating the system-level decisions.

While evolution is a natural and a healthy way of growing a system, the value derived from the new designs and approaches has their merits leading to the emergence of systems that cannot be envisioned from the older, evolved systems. The tradeoffs of performance, risks, dependencies increase with complexity. They are compensated by the values offered by the innovations leading to economic feasibilities.

Modularity as a basis for strong and weak ties:

The rise of the voice of the individual with the web 2.0 modular artifacts such as blogs and the collaborative networking among communities with wikis have led to various ties – both weak and strong. The small and low-level interactions collectively pave the way to stronger, self- organized communities of employees and customers inside and outside the enterprise when self-organization is viewed as a benefit rather than a risk for the enterprise. Traditional organizations are known for their hierarchical models of authority, division of labor and a foundation for a stable environment for the people. These areas of strengths need not turn to become weaknesses when the balance of central, hierarchical models and independent modular systems operating for the benefit of the work that needs to be done.

Strengths of Hierarchy and modularity - their manifestations in shaping technologies and organizations

The operating system governance, architectural decisions, and several centrally controlled system configuration management decisions are fostered by hierarchical models in computer systems. For example, traditional mainframe computer operating systems architectures evolved over time. They are the foundational ground for several capacity planning tools, automated operations tools, recovery, storage and backup, performance monitoring, job scheduling and many more - the list is enormous.

Most important points to notice are twofold.

No matter how far the distributed computing systems have evolved, there is always a place for the mainframe systems. Today’s cloud computing endeavors include Big data and machine learning which run on massively parallel computer systems and servers. When the component applications of operating systems matured enough to become independent, they formed the basis for new technologies and industries. The storage and work-related applications such as data warehousing, groupware, workflow, and document management systems evolved into systems capable to survive and thrive as industries. Data management systems capabilities have grown exponentially to analyze and provide deeper insights and augment human capabilities.

Impact to organizations:

The impact of technologies on organizations is not only shaping the way people do their work but also shaping the structure of organizations – both the technology industries and those that use them. This impact is attributed to the several paradigm shifts in the various types of technologies produced over time ranging from the vertically oriented hardware, operating systems, database and application layers of the computer systems to the horizontal PC DOS operating systems and the applications produced around it. These led to the client-server, enterprise architectures (bridging the client-server with a middle layer), followed by the web 2.0 and web 3.0 technologies oriented towards including the commons, by building and spreading the communities. Adjusting themselves to the modularity and hierarchies that technologies demanded is fundamental to the enterprise organizational structures of the internet era.

The current trends of digital network businesses indicate how modularizing is impacting their expansion as well as profits. They have been successful in building components suitable and aligned with the internet era. Apart from leveraging the internet era advantages of digital modularized distribution channels, the internet era born-digital platforms have become "aggregators" of the modularized supply-side resources. The internet platforms are primarily aggregators of modular components shifting the value from pre-internet era integrating approaches. How they modularized suitable to their context is discussed in detail in a Stratechery post here.

Best of both worlds for people and organizations:

I came across the word “hyperarchy” in the article by Khan, M., & Azmi, F. (2005). The authors discussed the WWW as a hyperarchy which is an “essential element of information culture” with flexible work arrangements that arise as a consequence. They warned that the organizational changes are lagging behind the turbulent age everywhere in the world. A decade and a half later, this is still a fact.

Discussing the various features of hyperarchy, the authors recommended for “Velcro organizations” which are fluid and cohesive enough to organize and flexible enough to delink for work on demand. The authors quoted Bower JL, 2003 with the analogy of how players organize for Soccer and Rugby. As in Rugby, playing Velcro hooking requires agility and strong skill set to act on demand. Rugby is not a position game like soccer.

A large group of business models of platforms is essentially "plug and play" models of organizations providing the infrastructure for producers to create and interact with consumers on top of the platform. For example, YouTube is the media platform with producers of video content plugging into the platform to create content. Consumers check in to watch the video content. Medium is the publishing platform where writers write their articles, readers subscribe to Medium to read.

Exploring the potential of several possible organizational models suitable for growing in the webspace is a continuous effort for both small and large businesses in the digital age.

References:

Linked by Barabasi, chapters

The Fourth link - pages 40 - 54

Small world

The After link - pages 227 - 238

Hierarchies and communities

https://hbr.org/2019/01/why-some-platforms-thrive-and-others-dont

Bower JL, 2003. “Building the Velcro Organisation: Creating Value Through Integration and Maintaining Organisation-wide Efficiency”, Ivey Business Journal, November/December, pp 1–10

https://iveybusinessjournal.com/publication/building-the-velcro-organization-creating-value-through-integration-and-maintaining-organization-wide-efficiency/

Baldwin, C. Y. (2019) “Platform Systems vs. Step Processes—The Value of Options and the Power of Modularity,” HBS Working Paper (January 2019).

Baldwin, C. Y. (2018) “Introducing Open Platforms and Business Ecosystems,” HBS

Working Paper (October 2018).

https://www.researchgate.net/publication/256040694_Organization_Design_for_Business_Ecosystems/stats

https://www.gartner.com/binaries/content/assets/events/keywords/catalyst/catus8/2017_planning_guide_for_data_analytics.pdf

Khan, M., & Azmi, F. (2005). Reinventing Business Organisations: The Information Culture Framework. Singapore Management Review, 27(2), 37-62.

https://platformed.info/platform-stack/

https://stratechery.com/2015/aggregation-theory/

Self organization in Digital Business Ecosystems - Long tail distributions, fluctuations

noreply@blogger.com (Raji Gogulapati) — Thu, 14 Mar 2019 17:34:00 +0000

Extremely fragmented imbalances in the order of the systems participating in the ecosystems can result in long tail distributions.

The evolving YouTube long tail distributions tell the story of both desirable and not so desirable consequences of the patterns observed in the long tail.

New entrants can get significant attention (distribution of the pattern near the long tail), more popular stars get even more popular (rich get richer) as a result of network effects of people’s (fans) decision making. It is easy to observe while listening to music or watching an entertainment channel, YouTube directs you towards more of the kind, getting you more.

Initial popularity does not mean the popularity remains effective. Favorite musicians or movie stars (with their popularity at their peak) usually remain in people’s list at most a decade (just my observation on how tastes change with more exposure) - that too sharing and enjoying their most popular hits. Meanwhile, someone else takes the spotlight and the show goes on. There are always stars waiting to be recognized among the niche players. This tendency leads to fragmentation over time leading to long tail distribution patterns.

In politics, unforeseen driving forces towards extremism and radicalization can happen. The Atlantic article on YouTube extremism highlights the autoplay feature of YouTube going a step above recommendations - towards promoting inflammatory content.

In firm ecosystem distributions - long tail fluctuations can result in catastrophes, and volatility increases - leading to undesirable results.

Take the popular Amazon’s role in retail. It is clearly spreading its wings cashing on the core digital distribution frameworks it has been operating the book selling business for the convenience of the customer. While entering retail continued to look like a phenomenon expected of an digital venture, establishing stores on ground does not sound the same. This aspect is leading to fragmentation in the long tail of retail stores on ground. Similarly delivering fresh groceries and cooked food is very different from delivering other retail items. This is another case of fragmentation in the long tail distribution patterns for order.

The logic behind the long tail distributions:

Long tail distributions can be a result of manifestation of power law. It requires verification because not all long tail distributions represent power laws.

Questions arise on the models behind the long tail resulting in power law. In the case of YouTube example discussed above, the preferential attachment model is in play.

The sand pile and forest fire models account for the volatility and catastrophes that can be caused by Power law distributions due to massive expansions of businesses.

What does the understanding of models to do with self organization for ecosystems?

When an entire forest will catch fire on lightening is determined by a critical point or threshold parameters. This point is difficult to predict. However, the parameters causing the criticality can be understood to plan better. For example, it is a fact that the density of trees in a forest determine the critical state to self organize - by burning down. But business ecosystems can do better. They can determine these critical points caused by interdependencies and interconnections and avoid reaching those thresholds causing failures and catastrophes.

About order and disorder, entropy:

More disorder or fragmentation in the long tail is growing uncertainty. It is a measure showing maximum entropy. Grasping the models behind lead to better choices and justification of the order that prevails reaching equilibrium or the disorder that leads to even more fragmentation.

Conclusion:

Getting customers of retail or consumers of content to long tail sounds desirable for both the businesses and customers, but the consequences are not so desirable for politics, super stars or retail stores alike. Inflammatory content can have adverse affects shifting the order and the health of the society. Too much customer convenience centered, widely expanding business can be creating the ground for catastrophic results for the industry.

Controlling and handling fluctuations balancing from disorder and chaotic changes is not easy. Perhaps statutory warnings as in the case of cigarette smoking is injurious to health, the consumer of inflammatory content must be warned about where his desires might lead to. Consumers can re-think their options for gratification and encourage on ground local markets.

References:

https://www.theatlantic.com/politics/archive/2018/03/youtube-extremism-and-the-long-tail/555350/

https://trends.cmf-fmc.ca/the-long-tail-in-2018-niches-meet-streaming-and-social-media/

https://medium.com/@michaeltauberg/power-law-in-popular-media-7d7efef3fb7c

https://www.wired.com/2004/10/tail/

Books:

Linked by Barabasi

Chapter The Seventh Link: Rich get richer

The Model thinker By Scott E Page - chapters 6 and 12

Chapter 6: Power law distributions, Long tails

Chapter 12: Entropy: modeling uncertainty

Networks, Crowds, and Markets: Reasoning about a highly connected world

by David Easley and Jon Kleinberg

Chapter 18:

Self-Organization in Digital Business Ecosystems - Interpretations

noreply@blogger.com (Raji Gogulapati) — Wed, 06 Feb 2019 18:16:00 +0000

The goal of self-organization concept is to bring order from an existing state to a more desired state. Self-organization in computer systems has several interpretations - as many as the systems built and contexts used. An often used interpretation is configuring systems to achieve optimal performance in the desired state. As the world of computer systems is expanding rapidly towards pervasive, complex, digital business ecosystems, there is a necessity to interpret what self-organization means for the several layers of computer systems (vertical) which in turn form networks of (horizontal and vertical, complex) systems. I will attempt to highlight the use of just two terms - uncertainty and feedback in the context of self-organization of such complex systems as these terms are often associated with self-organization. Several interpretations, in turn, come into play with this terminology for different layers ranging from the network, infrastructure layers to the user layer of computer systems. The varied interpretations with just the two terms associated with self-organization for ecosystems suggest that building digital business ecosystems combining traditional settings with new digital business models require clarity and attention to detail.

Uncertainty is experienced with surprise and little information revealed on the disorder. The distribution of patterns, timeliness, and complexity of the associated variables are some of the causes of the uncertainty affecting the desired state of the system. Designing a matching system whether it is for renters and owners, or of males and females for a dating event can result in quite uncertain outcomes depending on the several associated factors. More difficult factors are how to get people on board when there are no apparent incentives or pull to draw them to use the system. Once you get people on board, what about handling the details of the actors involved? The cause of uncertainty for the matching system could be all the factors or just a factor for the success or failure of the event. The key to resolving uncertainties is to turn to clear assumptions on what can go wrong. Statistical reasoning can be the rescue here. Again, just using the best available statistical tool and a data scientist to interpret the data with the tool is not sufficient. Expert knowledge to understand and predict the nature of uncertainty with the model of distribution patterns is the first step. This needs to be contextually combined with the feedback data. Both the positive and negative implications need to be well thought of.

Some ecosystems or networks are so complex (a city is a very complex network with several computer systems) that questions arise if they can be self-organized. Going deeper into the mathematical reasoning for modeling city congestion, there are mathematical functions like Lyapunav functions to prove that it is possible. "A Lyapunav function maps configuration at time t into the real numbers and satisfies two assumptions. First, if the transition function is not at equilibrium, the value of the Lyapunav function falls by a fixed amount. And second, the Lyapunav function has a minimum value. If both assumptions hold. then the dynamical system must attain an equilibrium" - Page (see References on Model Thinker below). Page discusses how cities self-organize with a "city activities model" in a very clear and readable way. Each individual or a corner shop does contribute to the overall congestions. Adjusting their schedules to meet less traffic results in reaching equilibrium for themselves and reduce the overall city congestion.

Whether the context is a modestly big network like a supply chain network or understanding the effect of a person’s portfolio of modest investments, feedback mechanisms help analyze the system dynamics that come into play. Here, the key is to pay attention to feedback from the system - positive, negative, combined effects of positive and negative resulting predator-prey scenarios. These feedbacks are challenging not just because the scale of the network growth brings in threats from competing forces. The challenge exists even with the combined direct and indirect effects of a couple of positive and a single negative feedbacks as they are beyond human reasoning to predict. Such effects reveal with constructing flows with boxes and arrows and developing models applying mathematical reasoning.

So, how is self-organization defined and designed for the growing number of business platforms and ecosystems? How is it achieved? Take the context, steps, and models needed to achieve an order of the various layers of the system seriously. The space covered by the internet based complex adaptive system requires expertise at various layers and levels - starting from a unique market creation to identifying hundreds of variables affecting these various layers and continuous experimentation with the effects. It is already becoming clear to practitioners and researchers in the field of platforms that nature-inspired patterns for self-organization and self-adaptation can be the source for effective digital ecosystem designs. And that may just be the beginning.

The limitations of current software engineering (inspired by systems engineering) approach for self-organization with configuration management and feedback control loops are that these mechanisms work well in systems whose size and scope is suitable for developers (human intervention) to manage. Web-based business ecosystems are interconnected firms with abilities to co-create, collaborate and possibly no specific hierarchical management and much more. Ecosystems are clearly out of scope for the traditional methods of software configuration used in software engineering practices for information systems either for predictability or managing large scale effects of uncertainty. A few of the often mentioned topics on platforms or ecosystems use nature-inspired protocols for attaining an equilibrium, dealing with competition or monitoring behaviors and creating patterns of organization.

The context for swarm effects is clear in the mass movements with people coming together naturally in a specific location or around an idea for a specific purpose. These effects are observed in nature in bees or other insects. This idea is put to use in developing new transportation ecosystems as relevant as creating a macro-situation map for routing drivers with situational awareness. Tracking human behaviors in an organization in response to a transformational change is a related context.

Viral growth effects are often associated as mechanisms to cause desired marketing effects in ecosystems. The design of the protocols causing the viral growth effects is inspired by nature - how gossip spreads or epidemics spread in a very open, adaptable, and creative way from host to host forming a network with the gossiping people thriving on topics that interest them or spreading the disease.
Leaving environmental trails or traces to gather information on the go and form patterns to create a massive, collectively co-ordinated effect is the mechanism of stigmergy. Stigmergy is demonstrated by ants, termites (social insects), bees, forming colonies, birds flocking and flying together. This phenomenon is evident in Multi-agent model simulations in video games

The challenge is to come up with mechanisms paying attention to the deeper implications of the organic approaches taken by firms for the life cycles of the member firms of the ecosystems. Perhaps nature's models of self-organized communities which allow for co-existence in nature's ecosystems offer better solutions. Understanding the models behind the phenomenon observed in the markets of platforms and ecosystems provide better reasoning and power to analyze, plan or predict for action.

References:

Classical Papers - Principles of the self-organizing system

E:CO Special Double Issue Vol. 6 Nos. 1-2 2004 pp. 102-126

Industries, Ecosystems, Platforms, and Architectures: Rethinking our Strategy Constructs at the Aggregate Level

https://www.researchgate.net/publication/291372335_Ecosystem_Platform_and_Industry_Architecture_Research_Re-focusing_the_Agenda

Key Affordances of Platform-as-a-Service: Self-Organization and Continuous Feedback (Researchgate)

https://www.cio.com/article/3193073/artificial-intelligence/why-is-stigmergy-a-good-platform-for-swarm-intelligence.html

https://www.huffingtonpost.com/entry/understanding-cryptocurrencies-with-stigmergy_us_5a387452e4b0578d1beb7243

The Model thinker by Scott E. Page

Chapter 12: Entropy - Modeling uncertainty

Chapter 18: Systems Dynamics Models

Chapter 19: Threshold models with feedbacks

Chapter 26: Models of learning

Chapter 16: Lyapunov Functions and Equilibrium

(Self-organization - New York and Disney world).

https://www.ineteconomics.org/uploads/papers/WP_92-Frydman-et-al-KUH.pdf

Comments on the article: Introduction - Platforms and Infrastructures in the Digital Age

noreply@blogger.com (Raji Gogulapati) — Thu, 22 Nov 2018 22:31:00 +0000

Comments on the article:

Introduction—Platforms and Infrastructures in the Digital Age

(Link to the article: https://pubsonline.informs.org/doi/10.1287/isre.2018.0794)

A valuable contribution introducing research areas on Platforms in the context of digitization. The authors’ “concomitant” view of platforms from diverse disciplines is a must-read for anyone who is looking for fresh and new glimpses into the “whole” about the much-discussed social, economic, strategic, and architectural angles to platforms. The questions in Table 1 are thought-provoking –the paper addresses the need for further research thru these questions. These questions will need much debate and discussion in depth. Probing into the disciplines of management and mathematics, sociologies and economics may get to justified answers. The broader theme of these questions seems to be on the challenges of factors of time and boundaries on ecosystem infrastructure development and the constraints on interactions.

The discussion and argument on the view of platforms as layered, modular systems highlighting the extent of modularity (coupling) and layering observed in platforms enabling evolution and emergence along the boundaries is extensive in this article. The authors also relate modularity to functional partitioning leading to organizational design and the role of firms participating in the ecosystem. Tradeoffs of tightly coupled components with centralized governance versus loosely coupled components with decentralized governance on innovation efforts is another area of focus here.

The discussion of the advantage of multi-sided platforms over the digital supply chains is an exercise for management decisions. Note the example of Amazon offering strategies to switch to physical with the strengths of digital infrastructures. The authors argue the need for theoretical research addressing the paradoxical tensions between the monopoly and control of digital infrastructures and the “democratizing force” of digital platforms. The ground is covered on platform envelopment for developing business for third-party developers and understanding the tradeoffs faced by complement providers in multihoming. Blockchain infrastructures and platforms challenges for scaling are questioned.

The authors chose to discuss online labor platforms from the angle of need-based outsourcing mostly. They also mention them as the testing ground for human and machine intelligence experiments. The challenges of these platforms arise from the pricing a task as well as freelancer and employer relations. When it comes to policy implications the topics covered are value created by data, US efforts on network policies, the European and US take on data privacy, the EU lead over consumer data protection thru GDPR.

The customer journeys in the digital platform space are not taken up in this introductory article. Perhaps later. Social media networks, unfortunately, appear to be positioned negatively. This year, Facebook user data misuse by Cambridge Analytica has been public news.
The authors discussed social networks data highlighting the issues with ability/ inability to audit and control used after data collection. Fake news spread via Twitter databases is another concern.

The authors discuss the pros and cons of net neutrality suggesting new regulation speaking of platforms as networks with modular, layered architectures enabling data flows between a variety of devices and applications. Industry level challenges talked about in the article focus on regulatory issues and controversies to allow for entrepreneurship, curb anti-competitive behaviors.

Popular platform strategy books and courses start out discussing the failure of MySpace and the popularity of Facebook to get the message across about network effects, core interactions as value units along with other basic concepts such as personalization, feedback loops, reach about platforms. These topics are not touched upon in this article. Also, there are no in-depth discussions on the services angle at the SAAS, PAAS, IAAS levels.

The research on patterns of service platforms based on necessities of communities (such as the shared assets like time banks, one time services like TaskRabbit, elderly and medical services apps) is not mentioned. The services include social services, banking, and retail services, time banks, asset sharing, and rentals. Services are becoming more granular with expanding boundaries for those who are interacting in the platform ecosystem. The reach of an asset is permeating via different channels - using the web as the platform for service delivery directly to the consumer who makes a personal decision to participate in the ecosystem. The solution is a real-time pull, personalized solution using a mobile app. The interactions on these apps are real-time, personalized, and have a built-in feedback system. The article does not mention the growing trends of platforms offering shared assets and social services.

On the Artificial Intelligence trends, the authors mention the business processes automation occurring beyond human agents - among "self-organizing, intelligent machine agents". However, the limitations of deep learning neural networks, machine learning based on big data needed to be highlighted. The trends in cognitive sciences and how the learning theories about brain functionality would impact the self-organizing agents on platforms can be an effective addition to this series. For example, how would the free energy principle (Karl Friston) affect the self-organization of agents on platforms?

The article is a beginning of much-needed discussion and research on the complex systemic view of platforms. Looking forward to more of such work on platforms.

The papers chosen for the special issue of Information Systems Research on this discussion on Platforms along with many other relevant articles are the following:

Cennamo C, Ozalp H, Kretschmer T (2018) Platforms architecture

and quality trade-offs of multihoming complements. Inform. Systems

Res. 29(2):000–000

Foerderer J, Kude T, Mithas S, HeinzlA(2018) Does platform owner's

entry crowd out innovation? Evidence from Google photos.

Inform. Systems Res. 29(2):000–000.

Karhu K, Gustafsson R, Lyytinen K (2018) Exploiting and defending

open digital platforms with boundary resources: Android’s five

platform forks. Inform. Systems Res. 29(2):000–000

Lee HCB, Ba S, Li X, Stallaert J (2018) Salience bias in crowdsourcing

contests. Inform. Systems Res. 29(2):000–000.

Niculescu MF, Wu DJ, Xu L (2018) Strategic intellectual property

sharing: Competition on an open technology platform under

network effects. Inform. Systems Res. 29(2):000–000.

Rolland KN, Mathiassen L, Rai A (2018) Managing digital platforms

in user organizations: The interactions between digital options

and digital debt. Inform. Systems Res. 29(2):000–000.

User experience design thinking – an exercise

noreply@blogger.com (Raji Gogulapati) — Tue, 13 Nov 2018 03:54:00 +0000

Design thinking brings structure and a systematic process to envision options for a predictable and prescriptive journey with insights and recommendations on any endeavor for various stakeholders.

It works and is effective as there are established and popular design methods to capture the needs and translate them into actionable items.

In the book,101 Design Methods, Prof Vijay Kumar discusses example design projects and methods used to arrive at optimal design solutions.

I summarize below the themes and methods discussed in chapter 6 (Frame solutions) methods.

While sensing the needs can be the seed for new and innovative solutions, not all solutions are viable. A framework to connect the concepts, envision the value for the user and the provider is the foundation for the design to materialize into feasible action.

Changes in perceived value with possible new solutions must be understood. The effects of the changes on various stakeholders due to the new design and changed value must be clearly stated and communicated. The language for clearly communicating the intent to translate the insights to products or experiences is rooted in design thinking and methods.

Connecting Concepts - Morphological synthesis comes to rescue here. For example, under the theme of user experiences, several ideas or concepts can be connected to arrive at new solutions or systems.

The main concepts can be broken down into smaller components. Contextual interaction is a core concept in the journey of a tech savvy customer/ user for the business. Preparing for the journey, finding the customer, connecting with the customer in various avenues and following up to make the match for the needs of the customer can be the main categories in the user experience journey. These categories can be organized in the form of a table to start with. Several related concepts can be listed for each of the categories. Elaborate the solutions with descriptions to evaluate further.

Rate the concepts for evaluating further, show the value flow for stakeholders. Foresight scenarios are built to facilitate discussion and focus to the process.

The solutions can be arrived at combining the concepts from each of these categories balancing business and customer needs.

Prepare	Find	Connect	Follow up

Demonstrating value to stakeholders with the new solutions is the purpose behind this exercise.
Several such design thinking exercises can be taken up.

In order to explore the impact and convey the new insights for stakeholders I found the HBR article “Elements of Value” very useful.

Remember the Maslov model for the hierarchy of needs you studied in psychology in high school? In this article, the author adopts this basic model to today's businesses for fulfilling their customers' needs and desires. Know your customer enough that you address his needs rather than promote impossible to attain goals (and probably the business' goals). In the global business - the developing countries customers and their conceptual framework of benefits and risks are different from those of advanced countries. Businesses addressing several elements of value (services?) achieve loyal customers. Companies can strategically and gradually introduce services balancing the emotional and functional benefits to their consumers.

Prof Kumar shows how organizing the stakeholders as nodes in a network diagram, indicate how the elements of value flow among stakeholders.

Envision

On digital customer engagement:

The objective is to create user experiences, create value at every step, and compete on customer journeys.

Contextual interaction is one of the powerful concepts discussed in the article - "Competing on customer journeys".
As a business, you take your customers' journeys into your hands shaping their journeys and experiences at a very personal level. A tech savvy customer is already ahead of the journey, his insights about his purchasing power can overturn any business if not vigilant.

Several examples are discussed in this article. Find how Loreal goes a step further, beyond recommendations. It lets its customers try on makeup virtually.

Think about how proactive businesses are expanding their Apps - for example, Starwood hotels check-in experiences, Delta (any) airlines app - the travel management tool for the users are some of the examples.

The article goes further in discussing the customer journey management and experience management roles for an organization.

Here, I must mention a related, earlier HBR article - "The Truth about customer experience". In this article, the authors urge businesses to make efforts to address broader perspectives and root causes of a customer call by engaging in deeper quests by creating feedback loops of interactions. The quest depends on how far the business is willing to go on whether to invest in transformational customer experiences or to address some glaring problems on the outset.

References:

Kumar, Vijay. 101 Design Methods: A Structured Approach for driving innovation in your organization; John Wiley & Sons, Inc., Hoboken, New Jersey.

Almquist, E., Senior, J., & Bloch, N. (2016). The Elements of Value. (cover story). Harvard Business Review, 94(9),

46–92.

Rawson, A., Duncan, E., & Jones, C. (2013). The Truth about Customer Experience. Harvard Business

Review, 91(9), 90–98.

Edelman Marc, D. C. (2015). Competing on Customer Journeys. Harvard Business Review, 93(11), 88–7.

Designing value with Data - A quick top down view

noreply@blogger.com (Raji Gogulapati) — Sat, 01 Sep 2018 00:53:00 +0000

Value of a business with data: A top-down view.

The vision and company purpose must be such that it is difficult to mimic what the company stands for.

It is capable of creating a universe around itself.

It is independent of others for its survival and existence.

It manages its systems and ecosystems efficiently.

It is a fortress that stands on its own.

The sportswear and equipment company Nike speaks in data ecosystems language. On the surface, it appears like selling shoes and sportswear serving a wide range of customers keeping customer-centric focus like any other company. Observing closely, Nike’s products are geared towards its mission and purpose “to bring inspiration and innovation to every athlete in the world”. Nike’s ties with iPod, sports apps MOVE and Running, gear like sports watch and sports band, Nike+ training system speak of Nike not just a business, but a smart data ecosystem connected and listening to it customers. The feedback system speaks of the needs of the customers put to reality every day.

Therapeutic ecosystems are placing digital health in the hands of the patient and the physician. Both healthcare and non-healthcare systems are contributing to this revolution. Several personalized, patient care applications, tools for the physician are making healthcare more manageable on a massive scale. Caregiving has become more specialized, attending to the patient’s emotional, physical healing along with medical assistance with the help of applications built around the theme of service and assistance. Complying with HIPAA regulations, ecosystems with data, smartphones, sensors, registered users of these systems. Value is what the patients and healthcare providers do with the platform rather than the identity of the registered user. Partners in the ecosystem include leaders in health, tech, sports, fitness... Mango health for managing patient medical doses, Practicefusion.com for electronic health record platform for doctors and patients, mylifesoftware.com for dementia care providers by care providers are just the beginnings.

What makes these businesses ecosystems serving a higher purpose and stand out among their counterparts. This quest brings to the following questions:

How is the analytical apparatus different and unique to make the experiences special with data for customers?

To answer this, let's begin with a few basics heard often in experiments with analytics.

- Mass customization - leveraging smart data and internet of things, awareness, sense (intelligence), respond, and connection forms the basis (defining characteristics) for smart products. Power of configuration and connection, not profile updates and behavior patterns, addressing the issues that were not previously addressed and as required by the coach – Nike, Adidas. Adidas smart shoe 2.0 is a research project, and its MiCoach running app does not have a developer community I think.

- Becoming personal with data.
This is the market of the individual consumer. Participating and Socializing with the customer and his tribe (friends) for understanding the influencers/ proximate causes. To monitor and respond in real time (social). Vendor motivates, sponsors, and “pulls” customer with customer-centric marketing messages frequently

- Data helps create multiverses (contexts) for customer meeting expectations “in context needs”.
Context is explained along the dimensions of space, time, and matter. It explains the reality as of experience. Smartphone (location services) with Mobile apps have expanded the services in real time to the customer. More the dimensions added, more specific the services become. Knowing what is searched, the weather, time of the search, services change – say offer a hot coffee coupon on a cold evening as opposed to a cold drink on a hot day. The shift is towards becoming proactive and predictive to the customer wants (levels of maturity). From transactions to preferences, awareness, and participation.

- Ontology models

Classifying data objects based on their relationships with other data objects and not on their similarities is a simple form of ontology. Using resource description frameworks is a standard method to bring structure to unstructured data. It is not so simple as stated in the world of recognizing each object as unique, to classify objects in groups. Knowing the strengths and pitfalls of classifying data based on data objects types is part of this exercise, which is not yet perfected.

- Importance of Metadata
Several definitions prevail for Metadata - data about data. There are at least three features of Metadata - content related to the intrinsic knowledge, context for the extrinsic knowledge, and structure related to forming associations. Numerous applications of Metadata include preserving the data trails, versioning, rights management, and accessibility of digital resources.

- Data as a Service
Processing large amounts of data to publish on-demand information for real-time analytics and visualization is the purpose behind data as a service. Disease analysis, customer behavior models, tracking fraud and detection benefit from the patterns derived with time series analysis.

A few prime examples of organizations using data as a service are UPS with optimized travel routes correlating with the weather and traffic conditions, Amazon's data-driven recommendations based on customer personal support systems, Kayak’s flight price trend forecasts, Target’s predictive clickstream analytics to track customer purchase patterns to adjust pricing are a few examples

- Data democratized.
Simply said democratizing data is to enable access to data across tiers, unlocking from the IT department of an organization. Concerns include security risks, misrepresentation and integrity.

- Presentation and visualization.
Visualizing results of data mined enable grasping insights, derive patterns. It is about using technology tools to present data. What is interesting about interactive visualization is a change in parameters helps to visualize the effect of the changes. Gapminder tools, Tableau software tools have become popular since the beginning of the Internet years.

How does data from the current state move to the realms of artificial intelligence, machine learning, cognitive knowledge, and behavior design?

Learning with data and evidence, building the knowledge without the need for reprogramming is the foundation for cognitive computing. The techniques of machine learning involve all the cognitive functions in assimilating data from several possible sources, retain the memory, analyze, discover patterns, advise and continue to learn. IBM’s Watson based applications demonstrate the capabilities.

What are the data level contentions organizations face when moving to smart data initiatives?

- Governance – balancing the privacy of data and the usage – who owns the data. Aligning governance with strategy. How to deal with massive amounts of data? Who maintains the validity of data? For data products – the technical API is the most valuable – competitive advantage.

Understanding Twitter as a platform

noreply@blogger.com (Raji Gogulapati) — Thu, 23 Aug 2018 16:27:00 +0000

Understanding Twitter as a Platform

Since Twitter was launched in 2006, it has grown to be the second largest social media tool in many countries. It’s transformation to an event following, tracking, news media from a simple “what I had for lunch” messenger is phenomenal in just a few years. Twitter is an example of how a simple idea can evolve into something more capable with a potential to make a mark and position with time mainly by usage. It is even more astounding and paradoxical to turn into something that it was not originally intended for, into an archived data set adapted by the U. S. library of congress.

Functions and Preliminary Architecture:

Tracking and following is fundamental to the “dispatch and courier protocol” that Twitter is built on. It is a one to many communication system combining the functions of instant messaging with SMS texting with a status function of “what you are doing”. This makes it a microblogging service. Following friends and other users is the second function of Twitter. The third function is enabled by search, to be able to find information that is valuable to the user. This came about with a change of focus to “what is happening”.

A use case of Twitter is centered on what it takes to post a tweet, the pre and post conditions. An actor of Twitter use case does Login, post a tweet, search for people, search for words, follow trending topics and logs out.

With the preliminary services (even search got added later on), it is easy to imagine that Twitter had a front and back end services layers with a user interface to send and receive feedback. A simple database would allow for storage and access of tweet texts. As Twitter adoption grew among users, a middle layer, search engine, and a robust data management layer with in memory processing capabilities evolved from the basic architecture.

Quality of Services:

The architecture evolved to address the quality of services - performance, usability, maintainability. Relevance to ISO/ IEC 25010 quality model is an exercise that explains how the architecture and technologies resolved the quality of services considering the tradeoffs between maintainability and efficiency, maintainability and reliability. More servers for reliability, a variety of technologies for efficiency is the normal route with tradeoffs to maintainability.

Contribution of Early adopters/ User innovation:

The famous # hashtag to report news, @ mention marker to share information about url’s are examples of user innovations representing internet relay chat culture.

Design changes – Twittering by Cuckoo:

Twitter is designed as a centralized architecture and microblogging service to handle SMS text messages in a messaging platform. Twitter’s centralized architecture is prone to issues that arise with scale, availability, reliability, and performance bottlenecks. Twitter’s broadcasters (for example news media and celebrities) can be those with millions of followers responsible for the scaling issues that arise. Broadcasting is not the only supporting service from Twitter – the list ranges from individual profiles, social relations, those looking for connections.

Cuckoo is designed with a decentralized architecture to allow for reducing bandwidth costs, load balancing, remove single points of failure, and ensure availability. Organizing the users based on their usage of the system is the key behind the structured and unstructured overlays of users. Dedicated servers to store static information about users also serve as backup servers to ensure availability. Organizing according to relationships allows for load balancing. Normal users, friends and broadcasters form the social awareness framework in Cuckoo.

Twitter’s ecosystem – publicly available API’s, access points, Services:

Twitter’s primary offerings for access to data are via three APIs - streaming data, REST (Representational state transfer) API and search API. There is an assortment of Twitter data connection (access), collection and analysis tools that deserves to be called the ecosystem responsible for its growth. It’s wide range of services in varied contexts makes it an attractive research tool.

Streaming API connects the receiving host and Twitter for large volumes of data.

REST API follows a request response communication pattern. Connections are established the Twitter and the host on an as per request basis.

Search API relies on the REST to provide a real time index search to the tweets.

User Search:

With the help of a search engine layer, Twitter provides robust text search mechanisms for its users. This layer is added not only to speed up searches, but also to improve on the queuing system for more reliability

Recommendation services – Twitter WTF.

Goal of the “who to follow” recommendation service is to help users discover connections. At the core is an open source, in memory graph processing engine – Cassovary. Recommendation algorithm is based on SALSA. Twitter’s uniqueness with connections is that there is no need for establishment of a connection to receive messages. The WTF architecture is continuous ffort. It is based on a single server prone to scalability issues. Memory limitations are also affecting the graph processing engine. The Twitter team is considering s Real Graph that resides on Hadoop distributed file system as the next step for the WTF architecture.

TwitterEcho – A distributed focused crawler:

TwitterEcho is an open source, modular distributed crawler used to support new insights into social community research, crawling communities. Alternative is to license Gnip social media API (for unlimited access to data) which can be expensive and beyond reach for academic research.

Storm @Twitter:

Storm is a Twitter developed, real-time, distributed stream data processing engine behind the data management tasks important for Twitter services. Twitter team lays out the essential features of a service they designed, used by millions of users – scalable, resilient, extensible, efficient, easy to administer. Several data partitioning strategies come into play at Storm. Many data-driven decisions are powered by Storm at Twitter.

Twitter Zombie:

Twitter Zombie is a technical tool for data collection and analysis. It is designed to have scheduled, designed, and programmable searches between the Twitter data and the host using the search API. The rate of funneling queries for search, the complexity of the query, are some of the variables that determine the usage of Zombie.

TwitterMonitor:

Twitter Monitor offers trend detection over twitter stream. Detecting trends in real time and providing meaningful analytics is the goal of this system. It detects trends based on bursty keywords and analyzing them further for trends.

There are several systems that come to use with Twitter’s platform on a daily basis making it a lively and growing ecosystem. The above are just a few of the systems selected to demonstrate how fast a simple idea can grow if it is of interest to its users.

Conclusion:

Addressing scalability, reliability, availability, maintainability and performance bottlenecks that arise requires ongoing effort to adopt changes to architecture, experimenting, throwing away old, replacing by new.

The effort to keep the platform relevant to the vast user community is continuous. It is a process of reverse engineering as well as a “next to impossible” task to track and document the architecture and usage. In the web 2.0 world, this also requires agility and lightning speed (almost real-time) change management.

Services offered, adaptable architectures, and software evaluation methodologies (to build, buy or acquire, try open source) to choose the right technologies, packaged into useful products and platforms offer the recipe for success attracting users in multitude.

In many situations, services are easily adapted by changing the focus with modular distributed architectures of the services. For example, TwitterEcho used to crawl one community can be adapted to crawl yet another community adhering to limitations of Twitter. TwitterEcho also reminds the limitations to obtain complete access to data – an act of governance by the Platform owner.

Efforts to produce new algorithms for an intended use often become re-used and become meaningful resources for the organization. For example, the efforts of WTF recommendation system with the Real Graph has also found its use in search personalization and content discovery at Twitter.

References:

Published papers of Twitter engineers, researchers on the architecture and services.

The Seventh Sense: Power, Fortune, and Survival in the Age of Networks - A review

noreply@blogger.com (Raji Gogulapati) — Thu, 23 Aug 2018 16:07:00 +0000

If a country’s leaders singularly focus on the goals set by the founding fathers ignoring the nature of the age, resort to actions forgetting the mastery and guidance needed by visionaries, there can be dire consequences in the evolving new world order. The tension arises when leaders of nations resort to achieving short-term which could be their own term in office with goals based on popular beliefs and pleasing the electorate. Leaders and citizens have equal responsibilities to mend their ways recognizing the complexities of the times. This time is clearly for setting the political direction with policies for the digital age for every country. There is no space for politically outdated thinking. Old methods of fixing structural, geographical boundaries do not solve the behavioral impulses of the networked age. Leaders may be accustomed to dealing with complicated issues, but the complex is different from complicated. Why - several reasons. First, let's get to the book review.

Joshua Cooper Ramo’s “The Seventh Sense” theme is to highlight the importance of planning and preparing with long-term strategies using indisputable methods suitable for the connected age – before materializing them to action. Ramo warns the most powerful country of the industrial era - the USA to prepare once again like a master not ignoring the winds of change of the networked age. If connection brought only benefits, a gatekeeper (country) by nature with a system of values such as choice of democracy, freedom of thought and expression, and privacy has nothing much to worry about. But, the times are proving this belief is shaken already.

Ramo cites the history of an ancient country like China, how protection of their values is inbuilt in their political system, how their leaders take the guidance of their spiritual masters for their political moves. The country has a history of a mix of power and art, the author writes about its leaders. This approach suits the nature of the country’s problems and the culture it has imbibed over thousands of years. This vast country too had its handful of dilemmas when transitioning from the Mao regime to open its borders to trade and development, to stand firmly in a new age. Ramo resorts to both European and Chinese histories to bring to the table the main point that the dynamism of changing scene of international politics is based on the patterns of histories of nations and conditions that lead to war or peace. As a person of Indian origin, I can see this mix of dilemmas which prevailed, contributing to the prosperous, ancient India from my knowledge of ancient Indian history quite clearly. I will not divulge on how I lament when I learn how India too succumbed to lures of unsuitable choices time and again.

In the connected age, a great nation which laid the foundations for institutions such as the United Nations and a mission for globalization faces the consequences of allowing seamless penetration into a country’s markets, its technologies, and it's educational systems to serve the causes it stood for. Openness requires assessment of risks, threats along with understanding opportunities. If risks are not mitigated in a timely manner, the systems can be easily manipulated by unforeseen sources – giving way to extreme behaviors including terrorism. The mission of global order might be shaken from its roots if there is no established vision to realize it. Securing and strengthening oneself will result in cooperation and coevolution at a different level beyond the threats and risks of interdependencies. This is the timeless truth for a nation, a business, and an individual - face the immense gaps tracing the paradoxes which arise with the complexities and trends.

For the purpose of comparison, I see this book similar to Scott Snyder’s ”New world of wireless”. Snyder's book is a decade old and was written for the purpose of understanding the swarm effect, implications of mobility for businesses and peoples. The book had a lasting impression as a thoughtful work on possible future scenarios for different regions and industries on the effect of mobility. As a norm, expert analysts from the technology world try to bridge the gaps of communication to convey the potential of technologies for business decisions. Business leaders are advised not to leave their strategic decisions on technologies (for that matter on those areas that have a lasting impact) solely on a singular group to avoid bias and heuristics. The power vested in the leaders of a nation is being tested as the impact and implications on future scenarios as technologies are becoming pervasive more than ever before. The biggest threat is the networks themselves which leaders have to come to terms with.

Ramo asks serious questions throughout the three parts of the book. He sets out to expand on the thought process to reach conclusions resorting to examples from the past politics and history, present developments – social, technological, and political systems in a masterful yet enmeshed way. The enmeshment of the discussions makes sense to the very nature of the complex problems with no easy solutions. Every nation must feel its own pulse and pave its own path to prosperity in this networked age, whether leading or being led, whether being gated or gating. His narrative is spread in three parts – first to establish the importance to understand the deeper ambitions, culture, and the soul of the countries, their politics, and their actions to participate in the networked world. Threats can arise from the evolution of the networks themselves in new ways. Envisioning the problems with new insights and seventh sense is necessary to lead and thrive here. The author visualizes a world tuned to AI and reflects the machine age where decisions will be mingled and relied on the intelligence of the machine. Yet, man represents the gatekeeper so far in spite of the superstitions and ignorance. To let machines gate-keep man for reasons not understood by him is a possibility to worry about. In order to get the best of rules and regimes, the key, age-old question is whether to "let kings who become philosophers rule or hope for philosophers to somehow become kings". Now, the author says that it is to decide on the ways the current gaps can be filled leading to a stable era is with the quality of citizenship, the balance of technological kingships along with wise leadership. Hope is to rely on the peoples at all levels, everywhere.

Ramo observes deeply on the nature of protests and the patterns of resistance around the world. Part II of the book starts with themes of rebellion around the world and fruitless resistance from political leaders in power. He explains the dynamism of power shifts in networks. He warns that slow reactions due to political inertia and a struggle to give up power have always been detrimental. The business of spiritual control is an example in many nations which struggle with the power of middlemen between man and his God. Speaking of middlemen, there are distributed, yet concentrations of data and network power with technologies as the facilitating middlemen in the platform companies GAFA. Science and technology advances with computing power are taking mankind to new horizons. Ignoring, ridiculing, resisting, and then accepting need not be an approach of the people in position for handling trends. The author emphasizes how strange things happen because of connections – “connection means systems take on new forms”. The conventional tools and available models don’t come of use in the face of crisis. Don't miss reading the chapter "MapReduce" for glimpses into those tinkerers, thinkers, and scientists who made the networks possible as we see them today.

Contrary to the narrative that the established may be losing their grip on the systems of the world, tremendous opportunities exist for the masters to orchestrate the loosened threads to weave a new story of mastery. Time is ripe to find mindful ways to lead the seemingly waiting, yet eager to grab the low hanging fruit with hunger and enthusiasm all around the geography of the globe. At times, the author seems to get circumlocutory on the power of networks. Ramo understands his wide range of audience than I do - he satisfies the need for breadth at a level that only a master of history, philosophy, politics, and technology can cover - everything merging with the themes of network science, computing, as well as hacking and fuzzing. But the purpose of the book is not the science itself – it is how leaders can sway the people dooming their nations’ future and foundations for security and prosperity by lack of clarity on the power and potential of the network themes and science. Popular and massive educational efforts to make every citizen of the world be clear of the choices and vulnerabilities with networks is crucial. None of the topics of computing is just for the enthusiasts and hobbyists of the '70s or the '80s. There is a clear tendency towards obsession which everyone has to guard themselves more than ever before.

It is important to preserve the soul of every country with methods suitable for the age. What seemed an asset is becoming a liability - the openness, the generosity, and the innocuous ways of perception are put to test every day.

Needless to say, the needs of the hour are so many, to summarize it is the mastery of design that can save, not a mere engineering effort in every sphere of life. No country’s leaders or its citizens can spare time for luring themselves with the masks of manipulations without paying attention to where they are driving themselves. To err is human - it is this human tendency we have to guard against the power of new machines and old politics in the network world.

Data management in entertainment industry

noreply@blogger.com (Raji Gogulapati) — Tue, 17 Jul 2018 20:44:00 +0000

Just finished reading the book "Streaming, Sharing, Stealing, Big Data and the future of entertainment" by Michael D Smith And Rahul Telang

The pillars for the strength of the entertainment industry have always been - creativity in content, content presentation in new and attractive ways, adapt pricing mechanisms for reach. The industry was always customer centric and depended on best practices around them.

Yet, the established firms (Hollywood) appear to have missed out on a major area - data collection to the finest detail that affects everything - understanding the customer, dealing with the competition, staying ahead and winning. All these areas are not new to running a business known to be established.

New ways of content creation and management, data-driven connections and decision making, intelligent and smart ways to reach and retain the customer are enough to create wars and win in this industry. There is much to learn about online retail from Amazon, data management from streaming companies such as Netflix, targeted advertising from Google for the established industry majors.

Telang and Smith did a remarkable job in their book highlighting the challenges for the established and how the challengers like Netflix, Amazon, Apple, and other platforms are superseding them. The majors have much to focus on their organizational structures and culture of decision making to play catchup.

My focus here is the chapter Pride and Prejudice from this book. In a nutshell, the chapter covers the complex topics of big data management and digital marketing for a digital store in an exploratory and readable fashion.

Some of the details discussed are about data collection in terms of
- collecting personalized data about customer's behaviors for metrics as well as experimenting further to seek new opportunities for marketing the 4 Ps (product, place, promotion, price) and connecting customers to content.
- embrace a culture of data-driven decision making that includes predictive analytics.

Assuming great content is created, data plays a powerful role in connecting with customers, launching the product in effective ways. Think subscription services and to bundle here. Diverse, multi-channel consumption with a handle over price elasticity (how price change affect sales?) is a strength of the internet offerings. Timing, deployment models are yet other factors that come into play for pricing. Allow for better targeted and profitable ad campaigns. Targeted ads can accommodate instantaneous responses to address changes in demand using data and machine learning techniques. Discover and predict changes to present the portfolio of popular titles to the front of virtual stores.

Netflix is an exemplary digital platform. It matches creative content directly to viewers with the help of data. I found the following research articles very useful in understanding how Netflix is able to make streaming its strength.

Dreier, T. (2018). Netflix Uses Data to Drive Creativity, and It's Terrifying Hollywood. Streaming Media, 15(4), 6-7.
(In this article, the author explores the topic of big data management at Netflix with Prof Michael D Smith - who authored the book along with Telang).

Fernández-Manzano, E., Neira, E., & Clares-Gavilán, J. (2016). DATA MANAGEMENT IN AUDIOVISUAL BUSINESS: NETFLIX AS A CASE STUDY. El Profesional De La Información, 25(4), 568-576. doi:10.3145/epi.2016.jul.06

LaPorte, N. (2017). Can Netflix Slay the Mouse?. Fast Company, (220), 27-30.

NEWMAN, J. (2017). Apple TV gets cozy with Amazon Prime, so where does that leave Netflix?. Pcworld, 35(7), 50-54.

Joon Suk, H., Youngjoon, C., & Kyu Tae, K. (2017). Specificity and Commitment: UX approach to Netflix. Journal Of Korean Society For Internet Information, 18(6), 127-136. doi:10.7472/jksii.2017.18.6
(www.jics.or.kr/journals/jics/digital-library/manuscript/file/.../JICS-2017-18-6-014.pdf)

The

What is Systemic thinking and why is it important?

noreply@blogger.com (Raji Gogulapati) — Sat, 07 Apr 2018 20:25:00 +0000

Globalization and the technology revolution have become the catalysts and the means to research the offerings on the leadership and management education and practices in the world. Global trends in higher education for the convenience of student advocate and promote simplicity of the principles behind complex systems at work. This can lead to the dangerous possibility of the students losing focus applying the seemingly simple theoretical concepts in real life and work situations which reflect the universal systems which are much larger and complex than they confronted in the classroom.

With the advent of the web, it is clear that the path to educating the masses is by allowing easy access to knowledge resources and adopting learning tools and technologies for more reach and effectiveness in learning.

Yet, questions arise as to why certain individuals and institutions consistently perform beyond expectations and make decisions that reflect disciplined vigilance and timely action? What could be the methods that work to fill the gap between ordinary and extra ordinary performers in growing, complex organizations? What could be the practices, tool set that these performers adopt in unforeseen, uncertain situations making decisions, solving problems where no amount of prior experience is of use. How can higher education help with the practices and thinking that individuals can be trained and educated in the context under discussion?

Learning to adapt and survive is necessary for everyone. There is no denial of the basic survival learning principles. The fast changing world is doing away with old practices of the industrial world that no longer work as we enter the new digital economies preparing for the future. The ever changing dynamic systems of the world require entrepreneurial education where the individual learns, unlearns and relearns at a rapid pace. The individual should be capable of learning for achieving excellence and utilizing one’s fullest potential. This requires a different toolset from the tools for survival and adaptable learning to do the situational jobs in the new digital economies.

It is recognized that collaboration, team work, openness that promotes discussions and debates to assimilate various perspectives need to take center stage in the educational systems everywhere in the world. As for the thinking processes, students are trained to think and apply critical and analytical thinking processes to solve existing, well defined and structured problems.

Systems thinking is a unique tool that stands out among the rest of the approaches and processes adopted to promote higher learning that leads to exemplary leadership practices. Systemic thinking gifts the individual with the capacity to unlearn and relearn at a rapid pace to handle unforeseen complex situation irrespective of the magnitude of the challenge. The guiding principle behind systems thinking is the universal thinking required to be able to see the whole as well as the parts simultaneously for achieving the needed balance in the decisions made.

We can overcome the limitations inherent in the thinking and practices adopted by those in decision making authority by applying systemic thinking tools and techniques.

Learning and knowledge need not be confined to the world’s top schools and scholars. If the systemic thinking practices are understood and unleashed to ordinary schools and colleges, putting knowledge to effective use will become a common practice amongst everyone who attends college education.

Insights on the leadership practices for decision making to sustain business competitive advantage with varying emphasis depending on the organization culture are crucial in the context of global, complex, uncertain and dynamic social and organizational systems.

In order to educate the majority of students who are not exposed directly or indirectly to management experience and practices in public and private sector industries, new experiments and approaches for education are necessary on a continuous basis.

We must be clear on the competence of the application of systemic thinking principles in the leadership paradigm. This is a crucial factor for educators as well as organizational leaders in order to direct efforts towards developing new applications and approaches.

We will interpret the definition, scope and context of systemic thinking based on the viewpoints of scholars of past and present. We will understand and evaluate contemporary knowledge environments and organizational thought leadership practices for decision making with systemic thinking.

Dawidowicz, P. (2012) elaborates on the concept of “hard” and “soft” systems thinking described by Checkland (1981). The essential difference between the two types of systems thinking is how each type of thinking is used depending on the problems to be solved. The problems and the related factors that are solved applying hard systems thinking are simpler, predictable and well defined. Researchers indicate that the soft systems thinking is applied when the “nature of systems, relationships of its parts are less defined” and the “problem involves conflicting or multiple perspectives that must be reconciled or understood”. Soft systems thinking often requires “analysis, synthesis and evaluation of factors and actions at multiple levels of interaction among potentially multiple related systems all of which can be in flux” requiring high order thinking and is a “high level taxonomic skill”.

Dawidowicz, P. (2012) claims that the “functioning of social and organizational systems remains a matter of some debate” and they work based on “balance and feedback loops” with limited control over the achievement of balance as well as an understanding of the feedback loops. They state that “debate over the nature of systems thinking itself has also proven heated”.

Skaržauskienė, A. (2008) recognizes the need to view organizations as systems in the current times of uncertainty and changes as “organizational problem is actions that do not correspond to the whole” attributing the root cause to management problems. Sensing and responding to situations orienting the management and leadership practices to the dynamic nature and structure of the systems are the key guiding principles to address these problems. Transformational measures and decisions can be taken only when leadership practices are not only inspirational seeking vision but also demonstrate the ability to plan “the whole politics, strategy and structure of the organization”.

There are enormous studies and resources on systems thinking concepts and methodologies from ancient to current times. From these studies, it is possible that systems thinking can be viewed as a “magic pill or mantra” to relieve organizations and leaders from the burden of the complexities of decision making in today’s world. However, it appears to be similar to any other system life cycle methodology. Although this thinking has the potential to address some of the conceptual problems of today’s complex systems, there is no certainty in achieving desired outcomes. The perception and awareness for confronting the complex problems and situations with this powerful thought process paves the path to redefining, reviewing and reflecting towards achieving the desired outcomes.

Skaržauskienė, A. (2008) discuss the essence of systems thinking suggesting to view it as interrelations and processes of changes. The authors compare systems thinking with traditional thinking in detail. They explain that systems thinking skills comprise “dynamic, system-as-cause, forest, operational, closed-loop, quantitative and scientific thinking” based on Richmond, B (1997).

It is crucial to assimilate and distill the learning and lessons from ancient and contemporary scholars about systemic thinking. New experiments with education cannot afford to forget the tradition and the roots of learning.

Evolution of the Systems Perspective

Systems thinking has similarities to the ideas behind the high-order, evolved consciousness, big picture, divine plan thinking. This thinking has its roots in ancient philosophies and civilizations of the world. Contemporary systems authors recognize this aspect in their studies. Using systems thinking compared to linear thinking, we can understand and recognize broader, large and small scale perspectives on the problems at hand. Systems thinking offers the potential to achieve the harmony and balance necessary for decision making affecting long-term, aligning with nature, and complexities of the systems.

Studies conducted on the evolution of human consciousness suggests that the systems thinking is closely aligned with the highly refined conscious, evolved behaviors of human beings.

Jere Lazanski, T. (2010) “presents Mayan calendar systems to contemporary systems

thinking principles and explains the answer to the Ackoff’s judgment on four hundred years of analytical thinking as the dominant mode of society”.

Nair, D., & Gopal, R. (2011) remind the readers about Dr. Deming of Columbia school of business and Peter Senge’s elaborate discussions about Systems Thinking in his Book “The Fifth Discipline”. The authors advocate Dr. Deming’s teachings about management education that promotes system thinking that utilizes people’s original thinking, motivation, curiosity and joy to learn rather than “survival and adaptive” learning which is temporary and transient.

Elements of Systemic Thinking

There are abundant resources for defining and describing the elements of systems thinking, viewpoints on leadership and organizations applying the principles. From these similar yet varied interpretations, it is clear that awareness of the unlimited possibilities for the paradoxical practices is based on the underlying nature of conscious perception and philosophy of systems.

Jere Lazanski, T. (2010) presents five principles of systems thinking viz., big picture, long and short term perspectives, measurable and no measurable data, dynamic, complex, interdependent and being part of the whole.

Skaržauskienė, A. (2008) describes the systems thinking principles comprising of “openness, holism, interdependency, feedback, purposefulness, multidimensionality, equifinality and counter-intuitiveness” and that the methodology is based on the R L Ackoff’s tradition “enriched” with contributions for several years. The author highlights the viewpoints of S G Haines (1998) that “system thinking is a platform for designing business architecture”. In addition the author explains that “principles and methods lose their value if they are used separately from a paradigm”. Applying the systems thinking principles to leadership practices to achieve operational competence and viewing organizations as systems is the premise for this.

References:

Dawidowicz, P. (2012). The Person on the Street's Understanding of Systems Thinking. Systems Research & Behavioral Science, 29(1), 2-13. doi:10.1002/sres.1094

Jere Lazanski, T. (2010). Systems Thinking: Ancient Maya's Evolution of Consciousness and Contemporary Systems Thinking. AIP Conference Proceedings, 1303(1), 289-296. doi:10.1063/1.3527166

Skaržauskienė, A. (2009). SYSTEMS THINKING AS A COMPETENCE IN THE LEADERSHIP PARADIGM. Management Theory & Studies For Rural Business & Infrastructure Development, 16(1), 97-105.

Skaržauskienė, A. (2008). Theoretical Insights to Leadership Based on Systems Thinking Principles. Management Of Organizations: Systematic Research, (48), 105-120.

Chun, M. S., Sohn, K., Arling, P., & Granados, N. (2009). APPLYING SYSTEMS THINKING TO KNOWLEDGE MANAGEMENT SYSTEMS: THE CASE OF PRATT-WHITNEY ROCKETDYNE. Journal Of Information Technology Case & Application Research, 11(3), 43-67.

Nair, D., & Gopal, R. (2011). Application of Systems Thinking to Management Education in India. Indian Journal Of Higher Education, 2(2), 21-25.

M. Kljajić, Significance of simulation and systems approach methodology in development of complex systems. V: Symposium on Engineering and Management of IT-based organizational systems, Baden-Baden, Germany, 2008.

Engineering and management of IT-based organizational systems: a system approach. Tecumseh, Ontario: The International Institute for Advanced Studies in Systems Research and Cybernetics, 2008, pp. 34-38 (to be deleted, not valid, secondary reference from the

R. Ackoff, Ackoff's Best: His Classic Writings on Management. New York: Wiley& Sons. 1999.

Checkland P. 1981. Systems Thinking, Systems Practice.

Wiley: Hoboken, NJ.

Collaboration - Operational definition

noreply@blogger.com (Raji Gogulapati) — Mon, 19 Mar 2018 19:00:00 +0000

Collaboration is a concept and a form of synchronous and asynchronous engagement at multiple levels, ranging from countries and firms to people observed in all areas of work both large and small scale, since ancient times. The objectives of engaging in work to achieve a common goal and collective outcomes reveal characteristics of collaboration that include nature of ties, timeless and transferrable skills of people, processes and more recently, use of technologies, irrespective of the nature of work and context.

Conoley, J., & Conoley, C. (2010) discuss the promise and characteristics of collaboration for adult relationships for energetic and creative team work. The viewpoints are based on Fredrickson’s (2002) “Broaden-And-Build” theory on fueling positive emotions.

Fredrickson’s theory include the following essential characteristics of collaboration.

Focus on strengths to pursue goals.

Eliminate perceptions that deter progress and interfere with support.

Commit to promote positive or at least neutral frameworks with adaptive explanations.

Leaders commit to promote creative, cohesive teams, and enable continuous growth for win win/ bidirectional success.

In stressful situations, provide individuals with necessary resources and emotional support.

Ask for help when needed, be grateful for the help received.

Notice the needs of others.

Exploring the domains, contexts, and environments ranging from inside and outside firms and businesses to individuals helps understand the essential characteristics of collaboration. In the context of grand challenges human beings come together to collaborate for meeting life’s essentials by nature. Scientific research and technology development requires deeper focus of experts and specialists. The expertise is unique and special for a variety of systemic issues that arise during the research. Discuss, debate, integrate, and assimilate are some of the themes of collaboration that arise.

Here are some examples:

Context: Grand challenges
Theme: Collaborating from everywhere voluntarily for a higher purpose.
Japan earthquake, Psunami. Virtual teams (Netflix experiment with collective intelligence, diversity)

Context – Collaborative Research groups
Theme – highly focused individuals integrate and assimilate for systemic issues.

Context – Practitioner and academic administration partnership

Theme – establish a learning environment, impart education.

Context – Software development

Key themes:
Open Source Development – Community of volunteers developing software source code.

Agile software development methodology - Extreme programming or pair programming

Collaboration in the context of software development – A few details.

Software development aka programming is not limited to writing code given specifications. For complex technology development (for example web based technology development, such as Amazon recommendation system) there are several creative areas and opportunities to explore for optimal decision making. The software under development needs to meet both functional and several quality of service level agreements to cater to. There is legacy code to transform and integrate with. The application code should function under several test conditions ranging from a unit level functionality, to multiple conditions such as scaling and performing reliably for a number of users. The software thus developed is unique for the system that it is intended to function.

In practice, prototypes are generated by senior level engineers which undergo transformation on a continuous basis to meet the requirements. Several modules of functionality are taken up by individuals creatively working in small to large team environments. Code inspections and review process involve members of the teams who meet on a regular basis to discuss and debate on the validity of the code being written. Writing code is a result of the above thought process, which requires proficiency and skill in the language being used. It takes maturity, proficiency and experience to write code in the language as well as in the context for complex software.

Decisions for progressing on which route to take while developing software is a crucial area that benefits with collaboration of competent individuals who understand the nuances of the requirements and tradeoffs to be made. Roles of a creative team require the cognitive abilities of a detective, explorer, artist, engineer, judge, producer, provoker, think tank, facilitator, and manager. Together, the creative team engages in the learning, organization and direction of the team, and creation of the product or process (Crawford, B., & de la Barra, C. (2008)).

Collaborative team work is emphasized in Extreme programming. The team engages in programming, testing, interacting with the customer as well as consultant, project management for tracking and monitoring the tangible and intangible products developed. There are more indirect roles that include senior management to sponsor the project work. The priorities and emphasis of each of the roles vary depending on the phases of the project life cycle which includes iteration and response to feedback.

Extreme programming practices in the software development are said to improve the satisfaction and confidence levels of the paired programming members of the team. Evidence suggests that pairing people may not always result as the better of the best individual performances. It is noteworthy to consider the net positive effects and benefits that result in working with satisfaction and confidence with pairs performing in par with the individual best performers (Balijepally, V., Mahapatra, R., Nerur, S., & Price, K. H. (2009)).

Adapting to collaboration at workplace:

Assignment:

Come up with the context, themes for collaboration at your work place. For this, consider the following questions.

How is collaboration incorporated in the context of the work flow at present?

What are your suggestions to improve the nature of collaboration?

References:

Conoley, J., & Conoley, C. (2010). Why Does Collaboration Work? Linking Positive Psychology and Collaboration. Journal Of Educational & Psychological Consultation, 20(1), 75-82.

Balijepally, V., Mahapatra, R., Nerur, S., & Price, K. H. (2009). ARE TWO HEADS BETTER THAN ONE FOR SOFTWARE DEVELOPMENT? THE PRODUCTIVITY PARADOX OF PAIR PROGRAMMING. MIS Quarterly, 33(1), 91-118.

Crawford, B., & de la Barra, C. (2008). Does eXtreme Programming support Collaborative Creativity? International Multiconference Of Engineers & Computer Scientists 2008, 1026-1032

Fredrickson, B. L., & Joiner, T. (2002). Positive emotions trigger upward spirals

toward emotional well-being. Psychological Science, 13, 172–175.

Platform Strategy for Business - MOOCS course on edX

noreply@blogger.com (Raji Gogulapati) — Mon, 05 Mar 2018 19:29:00 +0000

I took the course – Platform Strategy for Business (audit option, followed the discussions regularly and did all the homework!) the first time it was offered on edX. This post is not for advertising for the course, but I must mention that I was very satisfied and motivated by the syllabus (for a 6 week introductory course) and content delivery.

The course draws attention to the topics largely from the work of the teachers – Platform Revolution: How networked markets are tranforming the economy and how to make them work for you. Platform revolution is a thorough introductory work on all the major topics necessary to learn about the economic shifts caused by platforms. The other book that comes close to this book is MatchMakers: The new economics of Multi-sided platforms.

The class engaged the students with carefully chosen weekly readings, short videos, discussion questions, and quizzes. The peer review and feedback mechanisms are commendable for a MOOCS course. This course motivates anyone preparing to work with some high order thinking, academic, and theoretical foundations on not only the basics of platforms, but also on serious topics such as design and development issues and paradoxes, launching strategies, pricing models, special topics on freemiums and openness, competitive strategies and traps for failure. I believe that learning begins after taking a course. There is so much to understand about platforms and their role in shaping the future of firms. This course is a pre-requisite for gaining insights and some of the necessary tools on serious topics on platforms.

A few perspectives on AI from a couple of popular authors

noreply@blogger.com (Raji Gogulapati) — Wed, 06 Dec 2017 21:18:00 +0000

Thomas Friedman has dedicated a chapter appropriately titled Turning AI into IA, to the topics on Artificial Intelligence in his work Thank you for being late

Friedman discusses work in America, the high wage benefits the average worker derived for several decades in the industrial era. What was learned in school was relevant and training on the job was sufficient in the pre-internet era for the average worker to enjoy steady employment. In the current times of accelerated changes in many fronts, Friedman states - "say good bye to all that" insisting the needed skills for good jobs (even average) are going up.

In this broad context, he analyzes how the machines augment human activities with examples like an intelligent milking machine in a farm. He rips apart the job of a cow milker with the routine part and the skilled part. But that is only the part of the job itself. He further analyzes the competition to the cow milker as a profession from several angles in the global world.

The discussion on Artificial Intelligence (AI) is practical. It is pictured first as Intelligent Assistance (IA). AT&T's pioneering models for life-long learning endeavors with MOOCs and gaming platforms for its workforce take up a significant portion of this chapter.

Next, he takes the reader on tour of Intelligent Assistants and Intelligent Algorithms. Friedman paints the big picture to the most commonly heard about topics about the AI world centering the discussion around knowledge worker, learning to upgrade skills continuously in a fast cycle approach, by using technologies to meet the demands.

The primary purpose of technologies is to better human lives along with meeting their daily needs. In this context, Khan Academy, Launchcode.org, LearnUp.com find their place in the chapter. He reminds how the human, soft, never changing skills are even more important to conduct themselves in fast cycles of change - a wake-up call for the theme of discussions on changing technologies.

In the Second Machine Age authors, Andrew McAfee and Erik Brynjolfsson discuss the fascinating miracles achieved by innovations in Artificial Intelligence aiding the visually impaired by bringing aspects of sight to the blind, restoring hearing to the deaf. IBM's Watson is already saving lives not just improving them but pioneering AI aided diagnoses. The chapter focuses on the influence of useful AI and the connected digital network of people benefiting from the innovations.

The role of people, their ability to continuously add and refine the stock of existing knowledge, and improve the innovative offerings, is getting easier with the advances the mobile, smart phone technologies. The people all around the world are privileged to be meaningful contributors to the knowledge creation and innovation.

The authors have come up with their next book, Machine, Platform, Crowd.

The new book has an entire section devoted to the Mind and Machine. It is clear that machines are an integral part of the human world, how humans can re-fit themselves is being debated and drawing the attention of many.

Digital ecosystem view - how to begin, Mark Skilton's book - Building digital ecosystem architectures

noreply@blogger.com (Raji Gogulapati) — Mon, 20 Nov 2017 02:53:00 +0000

There is no contention on the view that digitization is a broad, complex topic. Much data and pieces of information keeps growing leaving the tough questions behind.

Where and how do enterprises begin? How are the initiatives planned with a conceptual view?

Mark Skilton's book - Building digital ecosystem architectures introduces the must know concepts meant to satisfy academics and professionals deeply involved with the questions. It is not for those without a background in systems of systems thinking and experience with enterprise level digitization applications challenges. If you try reading it fast, it sounds unreadable. Set aside time for it, underline key points, take notes, and enjoy some in-depth discussion as the book will certainly address some of your deeper questions on digitization as well as developing an ecosystem architectural view.

There are two main parts of the book. In Part I discussion centers on the Architecture in the era of Digital Ecosystems. Part II is about designing the Digital Enterprise.

In summary, Skilton thoroughly discusses the following and more in this book.

1. Understand the big picture.
There are several routes to digitize. Have a handle on the emerging, digital oriented enterprise architectural themes of thinking for digitization in your context. The author chose to discuss about 10 topics of themes including platforms at a conceptual level with examples.

2. Identify the design patterns or models and emerging architectures understanding the ecosystem view for the context.

3. Follow the trends of the various technological systems that fit the context. Think immersive experiences as a progress from technology pervasiveness.

4. Design with a fluid modeling approach by experimenting and testing several possible alternatives and endless possibilities to design.

5. Grasp how the concept of virtual environments is based on physical work spaces for an elaborate thinking on digital capabilities and spaces. This thinking is not restricted to the information systems and social aspects of digital ecosystems.

6. Follow how social, business and knowledge context drives the meaning and purpose.

7. Observe how the human machine boundaries are changing with digitization.

8. Take note of rapid software development practices in addition to building complex enterprise applications with the fourth and fifth generation, and data analytical software development techniques towards digital convergence.

9. Keep up with the rate of change to make sense of the new opportunities as well as threats.

10. Follow the several digital design practices and solutions using enterprise architecture models examples along with case studies. These include modular, scalable, multi-sided platforms. Recognize the extent of ecosystems that can be considered understanding the concepts of clustering and grouping for the services and processes.

Interactions in the Sharing Economy

noreply@blogger.com (Raji Gogulapati) — Tue, 07 Nov 2017 18:46:00 +0000

Interactions/ Services in the Sharing Economy:

The digital consumer platforms are laying the foundations for a leveling ground for trade and transactions where both haves and have nots benefit from services in all walks of life as never before. In this decade, significant changes are happening in the way services are envisioned and delivered in every part of the world. The consumer platform businesses and the sharing ecosystems have initiated this trend. The fluctuating financial conditions and uncertain employment affecting the mindset of the middle-income group consumers of the developed countries are catalysts to this trend. There is a significant difference in their affordability for owning one-time use goods. Corporations and businesses have not been able to keep their promises of steady employment due to complexities that arise with changing global trade and national taxation policies. As a result, they have become larger consumers and suppliers of short and long term services.

Sharing assets is not new for people or businesses; the underlying motivation could be as varied as purely economic or purely humanitarian. What has made this prevalent, is the convenience of availability of services, and people being able to locate the services through the mobile web platforms that are just a touch away – the app on their tablet or smartphone. Considering the history of web platforms, eBay probably comes first for connecting consumers and producers. So, what is new about sharing or peer production? The web as the hub (digital platforms) coupled with smartphone apps (call them cloud services) has become a trend and is no longer limited or contained for the wide variety of daily needs of consumers of all walks of life. The young are attracted to convenience; the regulators recognize the outdated laws and regulations, those in the middle are observing services expand from the trading spare time (Time Banks such as Greater Boston Time Trade Circle (TTC)) to accommodations away from home such as Airbnb.

Consumer platform businesses are mostly web platforms, serving communities in different walks of life. Outwardly, all the services are catering to the needs of the consumers and appear to be similar. There are subtle differences though. Consider the often heard Airbnb and Uber platforms. Home owners and travelers (Airbnb) have come together starting a new trend of finding avenues to stay away from home. Drivers or vehicle owners, and riders (Uber) have come together starting a new trend of finding quick rides at any time in the busy streets of cities everywhere, finding convenient and affordable transportation. The service ecosystems of the sharing economy distinguish themselves from the rest of the services with their unique characteristics. These services are from individuals or businesses who utilize their not frequently used assets or services for a fee or provide for free.

Therefore, an Uber driver, driving his car for a living, is not a participant in the sharing economy if viewed as someone co-partnering with riders on the Uber platform. His car is not just parked for days, becoming an asset which can be rented otherwise to make some money. An Uber driver uses his car to drive and make a living in this context. But a vehicle owner who may be using his vehicle as part of Turo (formerly RelayRides) is clearly participating in the sharing economy. The car would be just parked in an airport or on the road side, if not part of the Turo system. One can argue if a shared Uber (one car with several independent riders) is a participant in the sharing economy or not. In this context, every seat in the Uber driver’s car is an asset and is under-utilized if there are not enough riders. Services around sharing ecosystems can be analyzed further to differentiate. What about Uber drivers who earn their basic living in some other jobs? While the definitions of the economies of sharing and collaboration are still being debated and understood, the sharing ecosystems are making their way into the mainstream of all sections of societies. New business models are evolving as the communities of the sharing economy are sharing under-utilized assets and are challenging the established and traditional businesses and regulations that govern them.

The motivation behind these services is economic, social, technological, as well as environmental. Compared to hiring a regular maid service, calling a one- time helper is more economical and easier. The “help user” and “help taker” can be co-partners in the system (for example, TaskRabbit). They can still enjoy the luxury of hiring services with just some trust on the people that are contributing to these services. Compared to leaving the car in a parking garage at an airport, Turo (formerly RelayRides) helps to rent those cars to be of service minimizing the economic burden on the traveler – the one traveling in/out.

Some of the services add new capabilities to existing businesses. The old and new are teaming up to deliver niche services for the convenience of the customer. For example, Walgreens’ flu medicines are delivered home by TaskRabbit during the flu season, a home service that is reliable for the needy who is sick at home. TaskRabbit is a participant in the sharing economy where people with skills or time, help out each other in errands that are hard to get on a temporary basis. Anyone with some spare change can enjoy services that they can’t afford on a permanent basis. Walgreens is an established business. Taking advantage of a sharing economy participant – TaskRabbit (the worker him/herself is the asset making himself available), Walgreens added the service of delivery of the flu medicine.

The consumer platform businesses are not limited to physical goods business services. They include social services, banking and retail services, time banks, asset sharing and rentals. Services are becoming more granular with expanding boundaries for those who are interacting in the platform ecosystem. The reach of an asset is permeating via different channels - using the web as the platform for service delivery directly to the consumer who makes a personal decision to participate in the ecosystem. The solution is a real-time pull, personalized solution using a mobile app. The interactions on these apps are real-time, personalized, and have a built-in feedback system.

Dubois, E., Schor, J., & Carfagna, L. (2014) elaborate on the time banking concept in their article highlighting the members’ opinions on the motivation behind their social services. They clarify that the gaps they are filling and the services they render are not possible by the current global or capitalistic ways of running businesses and marketplaces. There is a local need for collaborative sharing, and they are doing that voluntarily. Time is the currency with equal value to all skills where the assets are social relationships in time banking.

The mobile app services tap into the power of mobility, user-centeredness, personalization, context awareness, sensing, embedding technologies such as search engines, GPS, RFID, NFC chips, device interaction, and many possible unique mobile web platform capabilities in addition to integrating with the traditional media. Mobile Apps don’t require software license; they connect people and business processes to wherever you are. It does not matter where the worker is; the technology comes in handy to manage the workflow connecting with colleagues, customers, and suppliers.

In the case of business services, the business processes are undergoing closer scrutiny to attain efficient access to service delivery, promote assets (tangible and intangible), and filling the gaps from both outsiders and insiders of the industry. Adjustments and re-thinking for conducting business are required to value the trends. Businesses will benefit paying attention to the “weak signals” of the acts of collaboration taking different shapes and sizes by the societies of the world. GOVINDARAJAN, V. (2016) calls the idea of paying attention to the weak signals as “Planned Opportunism.” He explains “The idea starts with recognizing that the future is unpredictable, shaped by nonlinear changes and chance events—the “opportunism” part. How you as a leader respond to is the “planned” part. Planned opportunism requires sensitivity to weak signals.” A much broader view of the potential of the web for services needs to be taken to come up with models and standards to enable their larger role in the world societies and businesses.

For designing business services, delving deeper into the context of existing customers as well as listening to stories of new, potential customers of various cultures, and backgrounds from global settings is the first step. Empathy is about paying attention to the story with intense listening to the other. Demonstrating empathy comes by practicing to understand deeper beliefs about problems and solutions. Gathering information by interviewing the client places the problem solver/ analyst in the pre-planned questioning mode. Whereas, listening to the story told in open dialogue without judgment brings the underlying intentions and needs to the surface. The analyst can make sense of the problem by synthesizing the information collected and defining the problem. Next step would be to generate several ideas for potential solutions. Prototyping allows to quickly explore which ideas are worth pursuing to generate further conversations and to test possibilities with feedback.

For example, services in any housing community can include pet care services, tools for home repairs, listings to reach out for maintenance activities at a minimum. While an in-house service and maintenance unit in the housing complex can be expensive, sharing the assets (home improvement tools), services among the members of the community can’t be difficult. The services thrive when consumers don’t count everything as numbers. Deeper insights help build services to meet the needs of service seekers. Inquiring into the pet service seeker’s needs can unfold a different story altogether. It is possible the family may be in need of a one-time vacation gear as well. The rentals for vacation can then be offered at low prices to lure the service seeker into sharing a related item for the next seeker.

The scope of sharing and services around sharing is enormous. It is not just for those who are economically deprived. Anyone benefits from sharing. The millennial consumers of designer fashions prefer to rent clothing, more so than their previous generations. Chic by choice, Rent the Runway business models cater to the needs of the consumers who seek expensive, designer brand clothing for onetime events, with a willingness to rent rather than own to keep up with the fast pace of fashion trends.

Student communities and college administration can come together to provide more avenues for making the student life in dorms even more friendly with attention to detail. While reputed schools like Harvard are known for their generous approach catering to the needs of students, it is not all that difficult for every school to step up. For example, any student juggling with time management, desires shared Uber and TaskRabbit services either free or with a minimal fee. Student fairs to share the rarely used assets of senior students with juniors is another area to explore. Linking services with the student registration process instead of just leaving it to the student to handle it all would bring a welcome change to the not so friendly campuses.

Owning assets does not make sense for many who would prefer to participate in the sharing ecosystems and benefit by them. The savvy web generation is beginning to enjoy the benefits of sharing. The web is enabling their freedom of choice with a fraction of the expenditure without missing the privileges of sought after assets and services.

References

Amit Sheth, Kunal Verma, and Karthik Gomadam. 2006. Semantics to energize the full services spectrum. Commun. ACM 49, 7 (July 2006), 55-61. DOI=10.1145/1139922.1139949 http://doi.acm.org/10.1145/1139922.1139949

Andrikopoulos, V., Binz, T., Leymann, F., & Strauch, S. (2013). How to adapt applications for the Cloud environment. Computing, 95(6), 493-535. doi:10.1007/s00607-012-0248-2

Banking in the Backwaters. (2010). Business Today, 19(11), 48-50.

Botsman, R. (2014). Sharing's Not Just for Start-Ups. Harvard Business Review, 92(9), 23-25.

Cook, S. (2008). THE CONTRIBUTION REVOLUTION. (cover story). Harvard Business Review, 86(10), 60-69.

Cusumano, M. A. (2015). How Traditional Firms Must Compete in the Sharing Economy. Communications Of The ACM, 58(1), 32-34. doi:10.1145/2688487

Dubois, E., Schor, J., & Carfagna, L. (2014). CONNECTED CONSUMPTION: A SHARING ECONOMY TAKES HOLD. Rotman Management, 50-55.

Geron, T. (2013). The Share Economy. (cover story). Forbes, 191(2), 58.

Gobble, M. M. (2015). Regulating Innovation in the New Economy. Research Technology Management, 58(2), 62-64. doi:10.5437/08956308X5802005

GOVINDARAJAN, V. (2016). Planned Opportunism. Harvard Business Review, 94(5), 54-61

HENWOOD, D. (2015). WHAT THE "SHARING ECONOMY" TAKES. (Cover story). Nation, 300(7), 12-15.

https://www.fastcompany.com/3046119/defining-the-sharing-economy-what-is-collaborative-consumption-and-what-isnt

http://www.wired.co.uk/article/filipa-neto-chic-by-choice-fashion-shopping

http://knowledge.wharton.upenn.edu/article/the-next-phase-for-the-sharing-economy/

http://www.mirror-project.eu

http://mylifesoftware.com

http://pwc.blogs.com/press_room/2014/08/five-key-sharing-economy-sectors-could-generate-9-billion-of-uk-revenues-by-2025.html

Lehrer, E., & Moylan, A. (2014). Embracing the Peer-Production Economy. National Affairs, 2151-63.

Malhotra, A., & Van Alstyne, M. (2014). The Dark Side of the Sharing Economy and How to Lighten It. Communications Of The ACM, 57(11), 24-27.

MANEY, K. (2014). YOU JUST GOT UBER-ED!. Newsweek Global, 163(19), 52-53.

Nafaâ Jabeur, Sherali Zeadally, and Biju Sayed. 2013. Mobile social networking applications. Commun. ACM 56, 3 (March 2013), 71-79. DOI=10.1145/2428556.2428573 http://doi.acm.org/10.1145/2428556.2428573

Roland T. Rust and Carol Miu. 2006. What academic research tells us about service. Commun. ACM 49, 7 (July 2006), 49-54. DOI=10.1145/1139922.1139948 http://doi.acm.org/10.1145/1139922.1139948

Villano, M. (2014). The sharing economy 2.0. (cover story). Entrepreneur, 42(12), 56-58.

Zingale, N. C. (2013). The phenomenology of sharing: social media networking, asserting, and telling. Journal Of Public Affairs (14723891), 13(3), 288-297. doi:10.1002/pa.1468

External Collaboration - Ecosystems

noreply@blogger.com (Raji Gogulapati) — Wed, 25 Oct 2017 15:05:00 +0000

User contributions, crowdsourcing, and ecosystem development are some of the several labels for external collaboration. The connected world of industries and individuals have taken the platforms to become hubs of communities where producers and consumers interact more than ever before jointly creating new business models of collaboration. These consumer platforms operate with their foundations closer to social communities of customers whether they are public, industry specific, or a business created. These communities organized as platforms are setting the stage for new ways of designing interactive customer experiences in scenarios with behavioral complexity and relational control. In this article, the terms ecosystems and platforms are interchangeable although ecosystems encompass platforms.

External collaboration is getting more sophisticated leading and following the customer (s) with technologies. Technologies are in place at every touchpoint of the journey following him/ her as a part of the system of life experiences. Ongoing, innovative, and experimental endeavors to fine tune the experience is expected to bring value with a higher impact on the interactions. The effort requires in depth understanding of the context, personal preferences, coming into play to provide a unique, unified, adaptive, proactive support system to the customer. The customer’s positive and negative feedback trail has much to offer for making these systems even more impactful.

In the last decade and a half, the information age has emphasized migration of an array of back- end, robust information systems to web based, global systems. The quest for utilizing the internet’s strength as a larger ecosystem embedded by systems of platforms is taking shape in the business world. Business models are harnessed to lure the new age global customer with experiences that are deeper in meaning, nurturing behaviors with every device that can augment human empathy.

Speaking of deeper and meaningful collaboration, Pink (2006) called this phenomenon attributing it to the dawn of “conceptual age.” He defines the conceptual age as “animated by a different form of thinking” with aptitudes of “high concept” and “high touch.” He further clarifies that high concept “involves the capacity to detect patterns and opportunities, to create artistic and emotional beauty, to craft a satisfying narrative, and to combine seemingly unrelated ideas into something new. High touch involves the ability to empathize with others, to understand the subtleties of human interaction, to find joy in one’s self and to elicit it in others, and to stretch beyond the quotidian in pursuit of purpose and meaning ”(pp. 51-52).

The current trends in producer consumer interaction platforms are fundamentally different from the business models witnessed in the various stages of internet evolution so far. They combine the strength of internet as a platform along with the applications of the conceptual age as defined by Pink (2006).

Starbucks, Nike, and Coursera, adhere to a few principles of collaboration in ways of teaming and aligning, fostering participatory networks for building and sustaining their ecosystems. They all have one thing in common – their founders and leaders had a grand vision for everything they took on – the mission statement and their guiding principles. Outwardly, Starbucks is a coffee shop, Nike is a sportswear and apparel company, Coursera.org is a learning organization where anyone can learn from a professor from a world class school.

Starbucks’ mission statement includes words like “premium purveyor of finest coffee in the world.” The fans are in millions. It is meeting the needs by interacting with its fans on their ideas generated and leveraged at MyStarbucksIdea.com. What is more interesting is to analyze how they address the collaboration challenges for filtering breakthrough ideas from the millions of ideas. At http://mystarbucksidea.force.com/, there is a whole world of ideas happening here on a product, experience, and involvement. There is a voting system for deciding on what is next at Starbucks listening to the fans.

Nike’s mission statement says – “bring inspiration and innovation to every athlete in the world” followed by the definition of an “athlete” (if you have a body, you are an athlete). Nike is an orchestrator of the estimated 98% of its production in Asian countries with diverse suppliers and products. Nike always focuses on its core competency and strengths around the athlete and its product design and R&D. The ecosystem reveals this. At http://www.nikefuellab.com/ Nike demonstrates its collaborative initiatives with the athletes with a “common language for movement” focusing on technologies and applications for measuring activity.

Coursera’s app store model for choosing courses on the website lives up to its mission – “provide universal access to the world’s best education.” Thousands of students gather and openly collaborate with faculty and facilitators from reputed institutions of the world in MOOCs settings. Together they are enhancing the learning experience for students worldwide.

Several models of external collaboration have taken shape in the always changing, rapidly innovating software industry. One of the most prominent models is the Open Source community model. The open source community continues to challenge the proprietary initiatives in the software industry. Linux operating system, adopted worldwide at a large scale on computer systems is the starting of the dominant cases of the marvels and miracles achieved by open source community. In 1991, thousands of volunteer software developers collaborated with a student, Linus Torvalds. He placed his software modules open for anyone willing to contribute and develop code under the GNU General public license. There is no stopping to this movement since then. The history of the evolution of the open source community model (https://en.wikipedia.org/wiki/Open-source_software_development) and several other team collaborations adopting various software development techniques are beyond the scope of this article. It is enough to say that open collaboration and knowledge sharing has been the backbone of the meteoric rise of the software industry. Today, at the Top Coder (topcoder.com), more than a million coders around the world come together for similar crowdsourcing initiatives. Google’s mobile phone operating system Android (based on Linux kernel) is yet another case of open source initiative by Google.

General Electric’s (GE) innovations in their core business are continuous since sixty or more years. There is a constant effort of scouting for external research and development in spite of it’s established idea generation factories and research labs globally. GE sponsors and collaborates research endeavors with universities and national labs for envisioning new fields of investment to maintain its leadership position in its core business. GE recognizes that not only following new principles, discovering them is also crucial to compete as a leader.

The grand challenges such as the one for Netflix to improve the movie recommendation algorithm is an example of crowdsourcing and team diversity and unorganized groups concepts. Every college student or a professional is aware of the concepts of team collaboration. However, the granularity of detail of the contributing factors is key to understanding success stories of team collaboration in various settings. It is not just talent, but also the emotional intelligence and agility of the groups of people working on innovative projects impact the results. Bringing together the best of people talent is not enough, combining their work in ways that only a group can achieve is the triumph of collaborative work. The individual does not lose, instead, shines with the spirit of the aligned group in such settings.

Managing trust and risks are inherent to any collaborative activity. When external resources are involved, the employee and outside collaborator have similar as well as different governance structures in an organization. While every organization has a window to the outside world with a website, has policies for public relations, and sales, initiatives for the external or open collaboration activities are different. The impact of an external collaborator on the innovative processes of the internal organization requires focus and attention to strategies for managing and developing the ecosystems.

Several known risks exist in external collaboration activities. Kannangara, S. N., & Uguccioni, P. (2013) identify at least eight types of dominant risks in the existing business ecosystem literature covering basics such as actors’ interdependence or complexities that are unique to collaboration when no one takes responsibility leading to uncertainty in results.

Any collaborative initiative presents dilemmas requiring thoughtful action. An understanding of game theory principles and prisoner’s dilemma in particular help with determining possible paths of action for predictive outcomes even in complex scenarios at large scale. Business communities present complex dilemmas to the stakeholders. When the stakeholders are in large numbers, the network effects can be very high with highly interactive and dynamic scenarios, causing negative ripple effects for everyone involved. A carefully designed governance structure can avoid such effects. Game theories such as the Prisoner’s dilemma theory can be used in scenarios of collaboration, price wars and catch 22 situations as instances of prisoner’s dilemma in platform business. This concept of the game, when extended to n players, results in thousands of experiments with scenarios for collective action and behaviors. For example, an understanding of the basis for the pricing wars that give rise to foundations for inter and internal firm partnerships for cooperation as well as rivalry requires experiments and simulations to help envision outcomes and prepare with measures of success mitigating risks. Architectures of participation involving stakeholders to connect, contribute, collaborate, and co-create are coming to shape the open organization where the network does most of the work. Cook, S. (2008) discusses “user contribution taxonomy” where both active and passive users contribute, whether it is providing expertise as in building Wikipedia encyclopedia, creativity via YouTube video sharing sites or provide behavioral data as in the case of Amazon recommendation systems. Removing barriers and obstacles is the first step in an organization to become more participatory.

Interest graphs bring relevance and context to handle information overload caused in such networks. The interest graph of the individual helps learn about his/ her activities beyond the realms of Facebook and Twitter. Machine learning or data mining algorithms accomplish this now for networked organizations for content curation. Several of the consumer collaboration trends suggest how the seemingly disparate ideas are filling the gaps people need the most. Competition, return on investment, making up the numbers of profitability can take the firms only so far. Although it appears that the challenges of collaboration on a large scale are designing for loss of control and handle the overload of information, the organization can prepare and reap the benefits. Companies can explore the feasibility by systematically analyzing of why crowdsourcing is useful and resolves their problem at hand, what they can crowdsource, and how they can approach the initiative.

References:

Kannangara, S. N., & Uguccioni, P. (2013). Risk Management in Crowdsourcing-Based Business Ecosystems. Technology Innovation Management Review, 32-38.

Cook, S. (2008). THE CONTRIBUTION REVOLUTION. (cover story). Harvard Business Review, 86(10), 60-69.

Pink, D. H. (2006). A whole new mind. New York, New York : Penguin Group.

Idelchik, M., & Kogan, S. (2012). GE's Open Collaboration Model. Research Technology Management, 55(4), 28-31. doi:10.5437/08956308X5504101

http://hbswk.hbs.edu/item/platforms-open-user-innovation-and-ecosystems-a-strategic-leadership-perspective

https://miter.mit.edu/articlehow-interest-graph-will-shape-future-web/

https://transformation.pwc.ch/site/templates/files/transforming-collaboration-with-social-tools_EN.pdf

http://relevance.com/blog/content-curation-and-the-interest-graph-delivering-context-to-the-consumer/

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2915213

https://dionhinchcliffe.com/2016/01/04/the-hardest-lesson-of-digital-transformation-designing-for-loss-of-control/

http://www.pwc.com/us/en/retail-consumer/publications/collaborating-innovation.html

http://about.nike.com/

http://about.nike.com/pages/doing-business-with-nike

http://www.huffingtonpost.com/darby-roach/behind-the-scenes-at-nike_b_818132.html

https://openforum.hbs.org/challenge/understand-digital-transformation-of-business/why-digital/nike-creating-a-digital-ecosystem-and-leaving-hardware-to-the-other-guys

https://www.coursera.org/about/

http://www.wired.com/2009/09/how-the-netflix-prize-was-won/

https://www.linux.com/news/category/open-source-practices

https://www.topcoder.com/

ABC’s of Big data and Machine learning in the AI Ecosystem:

noreply@blogger.com (Raji Gogulapati) — Sat, 07 Oct 2017 21:22:00 +0000

Computing machines are capable of holding an ocean of knowledge and also can recall relevant material useful to resolve dilemmas for competing in sports or provide expert advice for evidence based medicine at lightning speed. Machines have learned to sense, respond, debate, discuss, and interact with reasoning to achieve such extraordinary capabilities which have been the territory of only expert human beings so far.

The preceding statements are reflective of the vast variety and broad spectrum of applications with varied maturity levels and much promise. There are a few points to note when we come across Machine learning and Big Data connecting them to Artificial intelligence.

1. There is much to unlearn about information systems built when data, information storage, and access were at a premium.

It was necessary for engineers to follow computing models that required instructions for anticipating every possible scenario to develop information systems. Engineers were limited by programmable computing (a.k.a Von Neumann computing) to build systems that helped people both at work and in their personal lives. They still prevail for solving numerous business problems.
In the age of big data, the nature of computing is changing to fit the needs of more complex and uncertain scenarios. Computing has now begun to show the potential to adapt to humans naturally in every walk of life.

2. Nature of decision making and problem-solving systems of the industrial age emphasized the left brain thinking based on logic and analysis for the most part.

The left brain thinking became the foundation of the automation mechanisms built to aid human and organizational endeavors. Left brain thinking and the infrastructure in the analytics space encompass applications built around traditional databases. These applications evolved and contribute to Business intelligence, broadly covering data warehousing, online analytical processing, and meta-data repositories. While all of them use past data and experience for knowledge and discovery – there are differences. The exercise is beyond the scope of this notes. The one thing in common is that they use sorting and searching algorithms with sequences of instructions which lead to responses from the systems.

3. The potential of Big data and Machine learning is enabling to explore and build right brain systems which are taking shape as the AI ecosystem.

Smart, intelligent, cognitive systems support the Artificial intelligence (AI) ecosystem. Cognitive computing systems are developing human capabilities and interactions, and scale better than ever before. Machine learning, prescriptive and predictive analytics, deep learning, recommendation engines, evidence-based expertise, narrative science and much more comprise the Artificial intelligence (AI) ecosystem backed by Cognitive computing.

In the consumer and business space, the technologies of analytics, big data, cloud, and AI are converging to form the AI ecosystem. The AI systems are capable of deriving inferences and pursue the objectives with the data provided. The AI systems draw on the fundamentals of statistics and data mining algorithms with iterative, improved learning from real time data.

4. The AI ecosystem as heard in the business communities represents a higher maturity level of analytics compared to the descriptive, predictive, and prescriptive analytics practices.

Examining the types of questions asked and problems solved is a way to probe into the state of capabilities of the systems for the business. Tom Davenport brought up analytics maturity levels at the organization level more than a decade ago on how to take the first steps for exploring data based decision making in his work “Competing on Analytics.” He later discussed the three stages of Analytics maturity (Davenport, T. H. (2013)). He called the era of Business Intelligence as Analytics 1.0 (up to the mid-2000s with enterprise data warehousing capabilities and business intelligence software), Analytics 2.0 as the era of Big data (internet based social media and networks begin to amass data), Analytics 3.0 as the era of building analytical power into consumer products.

5. Machine learning is different. The talk about machine learning is around predictive or descriptive learning using big data applying statistical and mathematical models that scale.

Data mining models and applications come closest – except they use large data bases. Typically, data mining tasks are grouping people or objects based on selected characteristics, estimating for inferring or guessing reasonably, predicting expecting behaviors as a consequence of previous actions. The list goes on with more statistical models with deeper selective techniques for evaluating relationships and associations.

The ability to question determines the level of understanding to find solutions. With descriptive analytics, there is an understanding of the power of analytics for starters. Progressing to predictive and prescriptive analytics lets the business come up with frameworks to arrive at desired results experimenting with product mixes. Machine learning overlaps with the foundations allowing to scale and iterative collaboration with man and machine. The use cases of predictive analytics are varied – for example, figuring consumer behavioral patterns, forecasting sales, predicting failures in machinery or fraud. [Ref: Chapter 6, Hurwitz, J., Kaufman, M., & Bowles, A. (2015)].

Machine learning broadly encompasses supervised learning (through association, regression, classification) and unsupervised learning for pattern recognition – an engineering term for classifying. A super market chain or Amazon would prefer to know the customer’s next choice of items based on previous purchases. Customer too prefers to check out the cart faster. These recommendation engines and matching systems work with recognizing behavioral patterns obtained from the data. Recognizing patterns helps derive a response of likelihood of a behavior. Detecting frauds, stock market predictions with changing data and environment require learning and intelligence more than just depending on the system designed using a large database with traditional, structured queries. Applications for recognizing speech and faces, robotics, and biometrics use machine learning techniques. High-risk scenarios such as patient health management are yet another use case for advanced analytics combined with AI ecosystem.

6. A broad spectrum of business analytics applications is beginning to show up as machine learning applications.

Advanced analytics including data warehousing applications scenarios are on one side of the spectrum. One such category of questions is those that predict re-order levels for inventory management based on sales next quarter. Machine learning is helping when answering questions such as alerting city security when a large-scale crime scene is unfolding in real time or even predict one. Applications based on machine learning and a mix of advanced analytics get better iteration after iteration – as they learn from the previous happenings. On the other end of the spectrum are the applications that mimic the brain with intelligence that far surpasses known human expertise in medical or space sciences. The infrastructure is built on the principles of Neuro Science and Nano Technology to design cognitive chips to take up part of the tasks the human brains can accomplish (see Dharmendra Modha and SYNAPSE).

7. Watson, AlphaGo.

Any briefing about AI systems is not complete without mentioning the AI Super computers IBM’s Watson and Google DeepMind’s AlphaGo. There are more in the race – most heard are Microsoft’s Oxford and Baidu’s Minwa. The problems that Watson takes up have come a long way since the initial Jeopardy win in 2011. Watson is being trained to aid doctors in the area of evidence based medicine for treating diseases better. AlphaGo is no longer limited to winning the Go board game. They are image recognition engines with natural language processing powers participating actively in the AI ecosystem.

Conclusions:

The primary message is to focus on the ability to compute differently and use data uniquely to look beyond the horizons seeking the unexplored and understand subtleties. The structured and unstructured data from various sources is beginning to come out of the dark to shed light on the patterns, correlations to match data with deep expertise to answer tough questions for businesses. The broad and vast maturity levels of the analytics and big data capabilities is the guiding force for enabling AI ecosystem growth. To take the first step is to set the stage for a vision towards the organization’s power to tap into the potential of machine learning and big data. Cognitive systems with the power of big data and machine learning are to become the sources of new dynamic knowledge sources to think and act differently on information for businesses.

Note: This is a work in progress article for https://www.techcastglobal.com/

References:

Alpaydin, Ethem. Introduction to Machine Learning, edited by Ethem Alpaydin, MIT Press, 2014.

Davenport, T. H. (2013). Analytics 3.0. Harvard Business Review, 91(12), 64-72.

Kelly, III, John E., and Steve Hamm. Smart Machines: IBM's Watson and the Era of Cognitive Computing, Columbia University Press, 2013.
Hurwitz, J., Kaufman, M., & Bowles, A. (2015). Cognitive computing and big data analytics

Loshin, David. Business Intelligence, The Savvy Manager’s guide, Morgan Kauffman Publishers, 2003.

https://www.ibm.com/blogs/watson/category/cognitive-enterprise/

http://www-03.ibm.com/press/us/en/pressrelease/35251.wss

Cognitive computing

Dharmendra S. Modha, Rajagopal Ananthanarayanan, Steven K. Esser,

Anthony Ndirango, Anthony J. Sherbondy, Raghavendra Singh

August 2011 Communications of the ACM: Volume 54 Issue 8, August 2011

https://www.linkedin.com/pulse/ai-from-venus-machine-learning-mars-geoffrey-moore

http://www.bloomberg.com/news/articles/2015-03-16/what-the-french-know-about-drones-that-americans-don-t

https://hbr.org/2017/06/in-the-ai-age-being-smart-will-mean-something-completely-different

http://www.wsj.com/video/baidu-case-for-an-ai-ecosystem/D44A92A9-66BE-4892-B802-59051B06608B.html

http://www.businessinsider.com/alphabet-earnings-ai-ecosystem-2017-7