Organization ABC has deployed top-tier enterprise software. It has hired an army of expensive consultants who advise that people should follow specific business practices designed to maximize data quality.

Contrast ABC with organization XYZ. The latter’s management never upgraded its mainframe, bought “modern” apps and, to be frank, some of its business processes are antiquated.

Based on this information, which organization manages its data better?

You’d probably guess ABC, right? Why? The answer can be found in Daniel Kahneman’s new book Thinking, Fast and Slow (affiliate link). He writes about how the human brain is broken into two systems. From the book’s Amazon page:

System 1 is fast, intuitive, and emotional; System 2 is slower, more deliberative, and more logical. Kahneman exposes the extraordinary capabilities—and also the faults and biases—of fast thinking, and reveals the pervasive influence of intuitive impressions on our thoughts and behavior. The impact of loss aversion and overconfidence on corporate strategies, the difficulties of predicting what will make us happy in the future, the challenges of properly framing risks at work and at home, the profound effect of cognitive biases on everything from playing the stock market to planning the next vacation—each of these can be understood only by knowing how the two systems work together to shape our judgments and decisions.

When you read the start of this post, you were invoking System 1.

Kahneman has taken some flak from academics because he has ostensibly simplified years of research. Pay them no heed. Few people are going to read books written like dense theses rife with citations.

This notion of two systems is essential in understanding how we interpret–or fail to interpret data. In Chapter 19 of the book, he writes about how intelligence on 9/11 gathered a few months before that awful day was not reported directly to George W. Bush. Rather, that information went to Condoleeza Rice, then National Security Advisor.

Of course, hindsight is 20/20. It’s easy to point fingers because we know now what we didn’t know then. But how often is that the case?

False Causality

Systems 1 dominates most of the time, fueled by our need to understand the world as quickly as possible. Case in point: We like simple stories with tactical, repeatable instructions. If I only do these ten things, then my company will be the next Wal-Mart or Apple. Books like The Halo Effect point out the facile nature of most management texts.

(Side note: I am not being hypocritical here. One of the things of which I am most proud in my most recent book, The Age of the Platform: How Amazon, Apple, Facebook, and Google Have Redefined Business, is that I don’t provide a ten-point plan on how to be the next Google. I’m just not that smart. In fact, if launched today, I’d argue that these four companies wouldn’t be the companies they are right now. Luck and timing are huge.)

Are companies successful because their CEO practices certain management techniques? Or is the chain reversed? Ultimately, this is impossible to tell absent some experiment.

Simon Says

Many organizations mistakenly follow a me-too approach to data management. That is, they model their data, buy applications, and/or follow “best practices” like they were scripture because “successful” companies are doing the same. But successful data management is more art than science; there are only necessary conditions. Those looking for recipes are probably going to be disappointed with the results.

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Have you successfully used or applied MIKE2.0 concepts  in a business or educational setting? This could include using any of our open source methodologies, how-to guides, open source solutions, supporting assets, or blog advice in an effort to improve information management. 

If so, please share your experience with us in the comment section below, or email us at mike2@openmethodology.org. Community respondents will have a chance to be featured as a case study in future MIKE2.0 knowledge publications, so this is a great opportunity for exposure and to help make an impact improving enterprise information management across the globe.

A New Era

Fast forward ten years. Those three companies are shells of their former selves. Screen names like TennisFan_69 have given way to real names at companies that understand the importance of validating user identifies. While forgeries are nearly possible to completely prevent, current tech bellwethers like Twitter, Google (via Plus), LinkedIn, and Facebook make great efforts to ensure that people are who they claim to be. (By extension, sites that use tools like Facebook Connect benefit from these authentication steps.)

The point is that millions of people can effectively manage their own identities, their own data, much better than a centralized entity or a customer service department.

This is one end of the spectrum: the democratization of data. As Clive Thompson writes in Wired, we’ve seen this era of increased transparency play out among our very eyes over the last five years, although sites like eBay and Amazon have long enabled this type of data self-service. Thanks to Google, it’s harder than ever to pretend that you’re someone else. Ask Scott Adams–or at least one of his pseudonyms.

Let’s switch gears.

Contrast the “all hands on deck approach to data management” with what many small business owners have to face. Their data tends to be extremely accurate because so few hands are touching it. While far from perfect, at least errors tend to be consistently made. Rarely in my experience are 20 different people at a company entering hours, invoices, or purchase orders in 20 different ways. Mistakes can typically be rectified in a relatively short period of time after someone understands what was done.

To sum, when millions of people touch the data, the result tends to be the same: reasonably good data.

The Middle

The problem for most organizations lies somewhere in between these two extremes. When 50 or 200 or 1,000 people touch the data, things often go awry (absent some type of data quality tool, culture of data governance, routine audits, and the like). Data is often, incomplete, inaccurate, dated, and/or duplicated.

Employees in big companies rarely make errors in consistent ways–and business rules of enterprise applications can only do so much. Yes, I can prevent someone from adding an employee with the same social number, but does the busy data entry clerk really care about data integrity when making minimum wage?

Adding to the mess is the fact that too often organizations fail to appropriately train employees. On-the-job training is, at least in my experience, sadly the norm.

Simon Says

You may allow vendors, customers, and even employees to manage their own information–or at least some of it. Of course, you can restrict access to editable data to only employees who have been properly trained and understand the consequences of their actions–and inactions.

Perhaps most important, however, understand that “the middle” represents a danger zone, a potential netherworld in which your data faces serious risk of being compromised.

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Introduction to MIKE2

Check out our latest solutions and wiki articles!

MIKE2.0 has a number of open source solution and wiki offerings that provide helpful guidance for information management professionals.

MIKE2.0 Solutions

MIKE2.0 Solutions address specific information management problems:

   Core Solution Offerings
Composite Solution Offerings
Business Solution Offerings
Product Solution Offerings
SAFE Architecture
Governance Model
Open Source Solution Offerings   New Articles!

• Is There Return on Investment of Information Assets
• The Dimensions of Cloud Computing
• Review of Data Virtualization
• Master Data Management Solution Offering/MDM Challenge
   

We hope you find these offerings of benefit and welcome any suggestions you may have to improve them.

Sincerely,

MIKE2.0 Community

A look at the site reveals a number of fascinating data contests, including one that offers $3 million (USD) for identifying patients who will be admitted to a hospital within the next year, using historical claims data. For a look at the data, data dictionary, and the like, click here.

How, you ask, can an organization such a large prize? It’s actually not that hard to understand. From the site:

More than 71 million individuals in the United States are admitted to hospitals each year, according to the latest survey from the American Hospital Association. Studies have concluded that in 2006 well over $30 billion was spent on unnecessary hospital admissions. Is there a better way? Can we identify earlier those most at risk and ensure they get the treatment they need? The Heritage Provider Network (HPN) believes that the answer is “yes”.

Do the math. $3 million is one-hundredth of one percent of $30 billion. One could even argue that the prize for that kind of savings should be ten times higher than what is currently offered, but the current bounty is clearly not holding people back. At the time I wrote this post, 734 teams or individuals or companies had entered to win the $3 million prize.

Mutually Beneficial

Why are so many people competing? To quote Gordon Gekko, “It’s all about bucks, kid.” $3 million is clearly a great deal of incentive–and that doesn’t include the invariable PR benefit of winning the prize.

In a way, the mere fact that this type of project has to be outsourced is, quite frankly, sad. Think about it. With more than $1 trillion wasted on healthcare in the United States, even moving the needle a little bit can result in massive savings. Yet, clearly something isn’t working.

Healthcare is just one of many industries has become complacent and utterly incapable of fixing its own problems. (Of course, there are many others, as Jeff Jarvis’ wonderful book What Would Google Do? [affiliate link] points out.)

Simon Says

This is the beauty of the Internet. It has brought with it increased transparency, opportunity, and tools. No longer do people and organizations need to sit idly by on the sidelines as opportunities are squandered and poor practices are ossified. No, creative and/or frustrated folks can take their data or their causes online and circumvent traditional gatekeepers.

Now, no one is saying that developing this type of predictive algorithm is easy. It can’t be. But that’s a far cry from impossible. Perhaps the current level of waste is simply an example of a market failure.

In any event, the Kaggle example demonstrates how poorly many–if not most–large organizations treat the topic of information management. Maybe if organizations awarded major bonuses to individuals, teams, and departments for (one could argue) doing their jobs, they wouldn’t have to go elsewhere.

Then again, maybe more of them should.

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The CIO role is one that really appeared during the 1990s in response to the increasing profile of Information Technology (IT) in organisations.  Previously, the person in charge of IT was usually called an Information Technology, Information Services or Computer Services Director.  The creation of the “chief” title shows the world how critical IT is in the modern enterprise.

The problem is that the IT world is changing in a way that we haven’t seen in over a decade.  I argue that the first decade of the century was one in which little actually changed in the use of IT in business.  When I take this position in discussions I’m often met with many who disagree.

My argument is illustrated by comparing a typical professional worker’s desk in 2000 to the same desk in 2010.  The year 2000 desk probably had a laptop, telephone that was likely (or soon to be) based on IP telephony making them highly mobile.  It is also very likely that the business applications were little different to those we know today using Microsoft Windows, an ERP such as SAP for business operations and Microsoft Office for email and documents.  Sitting beside the computer was a mobile phone.  The 2010 picture is very similar.

Interestingly the comparison between the 1990 desk and the one at the turn of the century is much more dramatic.  The 1990 desk was very likely to have no computer, a terminal or if there was a PC it would have been deskbound.  The telephone was absolutely fixed to a point using a traditional PABX and there would not have been a mobile phone.  Any software being used would look very different to the products we commonly use today and likely to be bespoke and certainly character rather than graphics based.

This is a trend that we have seen before.  The 1970s saw massive change, while the 1980s was really a decade of consolidation.  While much happened in both the 1980s and the last decade, both were really dominated by a need to simplify and consolidate the role of IT in business.  As a result, we have been lulled into a view of the CIO which needs to focus first and foremost on the discipline of managing a complex but predictable portfolio of systems and projects.

Already in this new decade we’ve seen a move away from a single operating system (Windows) and end-user platform to a wide range of consumer-driven options ranging from tablets to mobile phones.   Similarly we’ve seeing a move away from enterprise servers to a much richer suite of options utilising cloud services.  Just as importantly, we’re seeing the long-predicted “internet of things” coming to life with embedded computing in everything from trucks to shopping trolleys.

No aspect of business value chains has been untouched by these changes.  The line between consumer technology and business systems has blurred to the point where customers expect to directly access their transaction data as it appears inside back-office applications.  This level of integration challenges the role of the CIO that has evolved during the last decade as being focused on business systems.  In this new world, the CIO is responsible for technology and information that is used by the businesses external stakeholders (including customers and suppliers).  This new CIO is as responsible for earning revenue as any divisional general manager.

The challenges will be substantial.  The CIO cannot hold onto the same number of staff that has been traditionally needed to run the infrastructure of the enterprise and still expect to take on revenue earning responsibilities.  The CIO is also going to need to lead large-scale innovation across the organisation with the goal of creating new products and finding new uses for the information that is the lifeblood of the enterprise.  This new CIO role doesn’t just serve the business, it also shapes it.

If CIOs don’t take on these challenges, then technology will be distributed across business divisions and an opportunity will be lost to innovate while maintaining the discipline that modern IT has fought to implement over the last thirty years.  This is a battle that is worth having.

Helena Hamilton is currently the Manager of Enterprise Information Management at Deloitte and has over 10 years of professional working experience in technology and consulting. She is an Information Management specialist with recent client experience in information governance, data quality assessment, business intelligence and enterprise reporting solutions. Her industry expertise includes public sector banking and financial services, pharmaceuticals and consumer business.

Connect with Helena.

From my perspective, the vast majority of large organizations has not embraced MDM quickly enough. Based upon my consulting experience, the MDM penetration rate isn’t anywhere near where it should be. Overwhelmingly, the pros exceed the benefits for the vast majority of mature organizations.

Why?

In her post, Master Data Management: Does an effective solution exist?, Brenda Somich writes about many of the cultural, data, process, and other complexities that plague organizations that go down the MDM road. Her excellent post sparked a maelstrom of comments exploring the issue. Among them, Tony Nudd writes:

too many projects fail because the initial business outcomes were not clearly defined in a manner which could be specifically measured. One of the biggest issues with MDM is that there is the danger of trying to boil the ocean with an enormous program of MDM work, when projects should be broken down into manageable, measurable projects which address the pressing needs of the business. Like enterprise Business Process Management, MDM should grow organically throughout the organisation starting with a manageable first steps project to get the frameworks in place and for the Centre of Excellence to be established. From that initial project a program of projects should then be formulated and prioritised to address the needs of the business. As the projects come online, so the project teams build competency, the business are educated as to what is possible and the technology is proven.

In this sense, MDM resembles other IT and IM initiatives. Beset by amorphous or overly ambitious goals, many organizations embark upon MDM with a “big bang” or “boil the ocean” approach, as Nudd points out. Those expecting MDM to immediately solve longstanding and prickly problems are probably going to be disappointed. What’s more, they then may well dangerously and prematurely dismiss MDM as an important concept.

Agile MDM

Perhaps the often unrealistic expectations of MDM projects can in part explain why many organizations are loathe to even give it a try. (However, let’s not forget declining IT budgets, shrinking headcounts, and risk-averse CIOs.)

But fewer financial and human resources doesn’t necessarily have to mean that new MDM projects are completely off the table, right? In fact, because of the rising popularity of Agile software development and deployment methods, I wonder if organizations have had more success eschewing Waterfall approaches. I can’t say that I’ve personally worked on Agile MDM projects, but it seems to make a great deal of sense.

And I’m not the only person who things so. Information management expert Scott W. Ambler writes:

If you’re going to adopt an MDM strategy within your organization, it should at least be an agile one.  Many organizations struggle when it comes to MDM, typically because they adopt a traditional, command-and-control strategy.  Your MDM efforts can in fact be very agile and streamlined if you choose to.

Ambler echoes Nudd’s comments about the organic nature of successful IM projects.

Simon Says

Many organizations struggle with Agile methods because they are time-driven, not requirement-driven. Determine in advance if yours is ready for an Agile MDM approach before undertaking this type of project. Cultural and/or execution problems may well poison the MDM and Agile wells.

Also, understand that even an successful MDM project–however implemented–is no elixir. Those expecting MDM to completely eradicate fundamental process, people, system integration, and political issues are bound to be disappointed.

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For us marketing folks, January is typically the time we square away to audit our marketing efforts from the previous year in preparation for future strategies. And the one big question that should be on everyone’s mind is, how much did those leads really cost me? 

The good news is, the numbers should be pretty painless to figure out. With all the wonderful analytic and marketing intelligence provided by companies such as Google, Coremetrics, Optify, Marketo, Raven, etc, there’s no shortage of data to analyze and come up with that magic number that tells you, “was this effort really worth it?” And with the rise of online and social media marketing in the past few years, the cost to acquire good leads has reduced dramatically to the point that any company, regardless of size or budget, can easily compete in the digital marketplace.

So what’s your magic number? In order to determine this, you need to define what you consider a lead to be. Is it someone who fills out a form on your website? Is it someone who clicks on an email campaign? How do you know when your leads are qualified, unqualified, hot or cold? The answer to these questions will differ for every business, but should be defined before calculating the cost of lead acquisition. Once you’ve figured out how you define a lead, you’ll want to find out the number of total leads you acquired and break them down by marketing channel activity.

Next, you’ll want to determine the cost of your marketing channel activities. In the world of online marketing, activities will generally fall into one of 5 campaign categories: Email, Social, SEO, Paid Search or Events/Webinars. The cost of activities for each channel will generally be a combination of time plus resources.

Lastly, to determine the average cost to acquire a lead, you’ll want to divide the total cost of marketing channel activities by the total number of leads acquired from that channel. For those visual folks out there, the equation looks similar to this:

Average cost to acquire lead = Cost of activity time + resources / total leads acquired

Calculate that and tada! That is your magic number (aka the average cost of lead acquisition for that channel). If you want to know the cost of lead acquisition for all channels combined or your marketing department in general, add the total cost for each channel and divide by the total number of leads you received.

Knowing how much value you derrived from your marketing efforts is paramount in developing your marketing plan. It will enable you to determine which activities generate the highest return on your effort (and help you boost the bottom line). Sure it takes some time to figure out, but hey.. they don’t call it crunching because it’s easy!