Operating in the pharmaceutical industry requires superior data management and analytics. Pharmaceutical companies must manage large amounts of information regarding products, customers, prescriber behaviors and clinical trials. This data is the foundation for efforts in research and development, forecasting, and sales and marketing.

With increasing regulatory pressure, pharmaceutical companies must be able to access the right information at the right time. The industry can no longer afford to delay the implementation of next generation tools to assist them in managing the increasing amount of data they generate. The challenge is to successfully streamline and integrate the follow of data so that management has the information they need to make timely decisions.

Sebastien Lefebvre, Head of Architecture R&D at AstraZeneca and speaker at the upcoming Pharma Data Analytics Conference, July 10-11, 2013 in Philadelphia, PA, answered a series of questions provided by marcus evans.

Sebastien Lefebvre, Head of Architecture R&D at AstraZeneca, offered insight into the advancement of big data efforts within the pharmaceutical industry in a recent exclusive on-on-one interview with marcus evans.

Read the full interview now…

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TIAA-CREF, a leading insurance and financial services organization, chose IBM InfoSphere software to provide timely customer data—which enhanced reporting and improved processing speed by almost 10 times.

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Vestas, a leader in modern energy, chose IBM big data technologies—including InfoSphere BigInsights—to help it pinpoint the optimal location for wind turbines to maximize power generation and reduce energy costs. The solution reduced response time for wind forecasting information by approximately 97 percent—from weeks to hours—to help cut development time.

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FRS Consulting’s Fred Sobotka talks with Wikibon’s Jeff Kelly about maximizing DB2 performance and finding new ways to glean insights from data without investing in expensive new systems.

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While these trends and their associated models of engaging in business are becoming clear, we have two significant problems that need to be understood:

1. There is a lot of data of different types available for consumption by business today. The data is produced or generated at furious pace and is commonly called big data.
2. Not all big data problems are solvable by strategies discussed so far.

This brings us to the critical question: How does big data impact innovation?

To understand this, let us look at the history of innovation. We have been delivering innovations since the beginning of time; however, until we discovered electronics and deployed the first generation of computers, the innovations were happening at a relatively slow pace. The ability to compute started speeding the innovation across industry verticals at a startling pace. However, the volume of data required for innovating constantly challenged us, as most of the data was not inside the computing environment in a structured format.

This problem was expensive to solve until recently. There are now technologies to process large volumes of data on commodity platforms for a fraction of the cost compared to the original costs. Platforms designed and built for handling scalability problems for search engines and social media platforms now provide the computing and storage platform for creating the enterprise compute and processing platform for large and multi-format, multi-structured datasets. The extensibility of this platform into the enterprise data repository is the game-changer for many enterprises. Now you can access all the data needed for making informed business decisions, and this creates fertile ground for innovation of new business models and identification of “blue ocean” opportunities within the enterprise.

The availability of data provides the empowerment to start creating business scenarios and replay the outcomes using the data and the underlying infrastructure. Multiple scenarios called as experiments will provide outcomes; and by creating the near perfect experiment, you can predict the closest outcome to what your business expects. This experiment will allow you to create the right segmentation strategy for your customer, the right market for your product or the right cross-sell strategy for your call center. The biggest transformation that can be brought to bear is the overall approach of the business to its prospects or customers. Instead of asking the “lifetime value of the customer” or “the profitability segment of the prospect,” the question has shifted to “What is the value of me (the business) to the customer or prospect.” This type of introspection has provided the business with opportunities to adapt to different types of customers, offer personalized levels of marketing, and services and has directly increased the revenue from such an engagement.

There is a lot of complexity in this process that definitely is not trivial. However, if the business wants to increase its profitability and remain in business, they are forced to transform their thinking and behavior. This can be implemented in the most effective manner with the right set of insights and metrics, which can be provided by using big data platforms, all the data needed for such purposes, a collaboration platform and a robust set of analytical models.

As you can see from this article, we are just scratching the surface when it comes to implementing and monetizing big data. This is just the beginning, and the possibilities are infinite. Big data definitely empowers innovation and provides a scalable platform to create multiple successful strategies from one statistical model or one experiment. At the end of the day, the biggest risk is not doing anything.

Remember the bottom line: For innovation to occur and thrive in a business, people are the biggest success factors, both from an executive and business user perspective. As you read this article and start thinking in this direction, remember the actions and outcomes need to be small, incremental steps. If not planned appropriately, it is easy to go overboard and attempt—unsuccessfully—to boil the ocean.

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If you’ve read the recent columns on starting an information governance journey by focusing on business glossary, data lifecycle, and data protection approaches, you may think we’ve exhausted the possibilities. But this month we’ll look at another governance starting point chosen by a number of organizations—managing master data.

Master data is the high-value, core information used to support critical business processes across the enterprise. It is information about customers, suppliers, partners, products, and other entities that lie at the heart of every key business process.

With that definition in mind, it should come as no surprise that managing this critical data is a top organizational priority, since it clearly supports business objectives like reducing costs, improving customer service, and driving revenue growth.

So what is master data management (MDM)? It is an approach that combines:

• Disciplines that provide a consistent understanding of master data entities and their relationships
• Technologies that provide mechanisms for consistent use of master data across the organization as prescribed by governance policies
• IT practices and processes designed to accommodate, control, and manage changes in master data assets

MDM incorporates structured and unstructured information from across the enterprise to create a complete profile of a customer or other entity. MDM can be applied to all high-value information used across the organization, including customers, suppliers, partners, products, materials, chart of accounts, location, and employees.

One phone call: $100M in lost business

A business partner of a large technology firm contacted the firm’s order center to buy a laptop for his daughter. Since his company drove USD100M in business annually for the firm, he planned to take advantage of the employee discount based on the business partner agreement. Unfortunately, the customer service representative on the phone could not locate this individual in the company’s systems and refused to give him the discount.

One simple phone call brought the lack of a single, accurate view of customers to the attention of the CEO of the technology firm. The business partner called to announce that he would not give the firm any more business because the customer service representatives couldn’t locate him in their systems.

This call got attention because of the scope of the loss to the company. But day after day, as a result of smaller, less visible decisions, many organizations are experiencing the same and greater levels of lost business because of the lack of management of master data.

Situations like this have convinced many organizations that the payoff from effective MDM is well worth the investment required to put the appropriate processes and tools into place.

Managing master data in a big data world

Organizations struggling to make effective use of big data are likely to have an MDM problem. Information about customers that is buried within big data needs to be linked to existing customer information that is already captured in traditional structured applications. Unless it is linked to master data, new customer or product information extracted from big data ends up being yet another disconnected silo of data.

MDM’s well-defined approach augments the value of big data by giving it structure and meaning. MDM tools can match products with traits derived from web comments and social network postings. MDM also gives context to machine-derived data—such as the data provided by mobile devices about the times and places consumers buy specific products.

MDM helps improve the 360-degree customer view by enhancing master data with unstructured content. For example, Facebook postings may tell a hotel chain that a high proportion of its business guests have too many children for standard reward rooms. The hotel can then respond by providing reward privileges on larger suites for these high-value customers, potentially developing closer relationships and enhancing loyalty.

Getting started with MDM

All companies in a 2012 study by Aberdeen Consulting wanted their business records to be more than 90 percent complete and accurate. They wanted to have correct information in all essential and also in most supplemental fields. According to the study, “The companies that chose not to invest in data quality and MDM tools reported the actual state of their data as only 70 percent accurate, a far cry from their goal and 12 percent worse than companies with governance tools.” ¹

How can an organization get started down the path toward improving information quality and managing master data? While defining best practices and enlisting executive support, the organization should select tools to make the job easier. For example, IBM® InfoSphere® Master Data Management (InfoSphere MDM) software includes both transaction-oriented MDM and collaborative authoring and workflow capabilities to handle all domains, implementation styles, and use cases across various industries. An MDM-powered application toolkit creates governance applications through pre-built blueprints and widgets, and pre-built data models can accelerate deployment. InfoSphere Information Server pulls together all the right data from across the enterprise and addresses data quality concerns before the information becomes part of the single, trusted view.

If a single view of key data supports the top business priorities of your organization, then MDM may well be the perfect starting point for an information governance initiative. If you have taken this path within your organization, what have been the key challenges and the key rewards? Please share your thoughts in the comments!

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From all reviews I’ve heard, the IIUG conference team really outdid themselves this year. This year’s annual event was the best yet, led off by a keynote speech from Bob Picciano, IBM’s new General Manager of Information Management Software and Big Data. Bob kicked off the conference by telling the crowd about IBM’s concentration on cloud, mobile, and big data—and how these areas will be leading concerns for the information management group and IBM as a whole during the coming years. I have known Bob for over 10 years, and I really appreciate him flying across the country to be there for the Informix faithful at this event. Thank you to Bob—and of course to Patty for arranging his trip. Bob has also written an open letter to the Informix community, and you can read it on the IIUG web site.

The conference took place over three days and included more than 100 sessions on Informix, Genero, IBM, and much more (and don’t forget the food and the parties!). Jerry Keesee, Director of Informix Worldwide Development, delivered another keynote where he discussed the new Informix 12.10 release. This new release was also covered in several sessions—many of which are available for replay at the IIUG site in the member area.

This year, we tried something new that seems to have been a big success: moving the half-day educational tutorials to the end of the conference and including them in the price for all paid attendees. We hope to continue this next year. Also, returning this year after a few years off the schedule, we held a final “Ask the Experts” general session. This session was preceded by ice cream sundaes, so as attendees enjoyed their ice cream, questions were flying to the Informix architecture team. It might have been the best IIUG conference closing ever!

Looking ahead to 2014, the search is on to move the conference to a new location, hopefully to an East Coast city. The search is just starting now, but we are looking at two potential dates—the week of April 27 or May 4—so mark your calendar and keep watching this column for updates. We’ve held the conference in the central US and on the West Coast, so now it is time to hit the East Coast. By holding it in a city with a major airport hub, we also hope to help make travel easier for attendees from South America and Europe. Check back here and the IIUG web site for more information—or go to iiug2014.org, which will be available as soon as the conference details have been finalized.

On a related note, did you know that there is a Development Edition of IBM® Informix®? This is an all-features-enabled version of Informix Dynamic Server, but with a 20-process limit. This edition lets users learn and develop with Informix, but it should not be used in a production environment.

Want to give Informix 12.10 a road test? Download the latest version, as well as the latest version of Open Admin Tool (OAT), from the members area of the IIUG web site. Simply log in and click the “Software Downloads” button. (The 2013 conference presentations, as well as previous ones, are available by clicking the Conference Downloads button.)

Remember: this is fully functional software courtesy of IBM for IIUG members, but do not use it in a production environment or to replace Informix in your work environment. It is simply a way to learn what is in the new release, so please respect that.

A whole host of thanks are definitely in order before I close this column. Thank you to the outstanding IIUG 2013 conference team, all the IBMers who assisted in the production of this event, and all of our excellent speakers—without each of you, this event would not have happened. Finally, a big thank you to the entire Informix development and support team!

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Hadoop can store big data and unlock answers to key questions. IBM® InfoSphere® BigInsights™ is built on open source Hadoop and extends it with advanced analytic tools and other capabilities. InfoSphere BigInsights helps organizations of all sizes efficiently manage the vast amounts of data that consumers and businesses create every day. At its core, Hadoop is a Distributed Computing Environment that manages the execution of distributed jobs and tasks on a cluster. As with any Distributed Computing Environment, the Hadoop software needs to provide facilities for resource management, scheduling, remote execution, and exception handling. Although Hadoop provides basic capabilities in these areas, IBM Platform Computing has been working on these problems and perfecting them for 20 years.

This IBM Redpaper™ publication describes the integration of IBM Platform Symphony® 5.2 and IBM InfoSphere BigInsights 1.4 in an IBM System x® cluster. IBM Platform Symphony is a low-latency scheduling solution that supports true multitenancy and sophisticated workload management capabilities.

Download the Redpaper now…

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As an information management professional, you’re already familiar with the ghastly consequences of data loss.

Here’s some perspective from onlinebackupgeeks.com: 50 percent of businesses that suffer data losses for 10-plus days file for bankruptcy immediately, and a whopping 93 percent file bankruptcy within one year. Scary, isn’t it?

The top reasons for data loss include hardware failure, human error, software corruption and theft. And if you think it can’t happen to you at your company, read on for the sad tales of data loss and backup failure that plagued such heavy-hitters as NASA, T-Mobile and Microsoft. Or take a look at how “Toy Story 2″ was deleted off the company’s servers.

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