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Barry Devlin

As one of the founders of data warehousing back in the mid-1980s, a question I increasingly ask myself over 25 years later is: Are our prior architectural and design decisions still relevant in the light of today's business needs and technological advances? I'll pose this and related questions in this blog as I see industry announcements and changes in way businesses make decisions. I'd love to hear your answers and, indeed, questions in the same vein.

About the author >

Dr. Barry Devlin is among the foremost authorities in the world on business insight and data warehousing. He was responsible for the definition of IBM's data warehouse architecture in the mid '80s and authored the first paper on the topic in the IBM Systems Journal in 1988. He is a widely respected consultant and lecturer on this and related topics, and author of the comprehensive book Data Warehouse: From Architecture to Implementation.

Barry's interest today covers the wider field of a fully integrated business, covering informational, operational and collaborative environments and, in particular, how to present the end user with an holistic experience of the business through IT. These aims, and a growing conviction that the original data warehouse architecture struggles to meet modern business needs for near real-time business intelligence (BI) and support for big data, drove Barry’s latest book, Business unIntelligence: Insight and Innovation Beyond Analytics, now available in print and eBook editions.

Barry has worked in the IT industry for more than 30 years, mainly as a Distinguished Engineer for IBM in Dublin, Ireland. He is now founder and principal of 9sight Consulting, specializing in the human, organizational and IT implications and design of deep business insight solutions.

Editor's Note: Find more articles and resources in Barry's BeyeNETWORK Expert Channel and blog. Be sure to visit today!

Operational analytics is making headlines in 2013. But why is it important? And why is it more likely to succeed now than in the mid-2000s, when it was called operational BI or the mid-1990s when it surfaced as the operational data store (ODS)? 
First, let's define the term. My definition, from two recent white papers (April 2012 and May 2013) is: "Operational analytics is the process of developing optimal or realistic recommendations for real-time, operational decisions based on insights derived through the application of statistical models and analysis against existing and/or simulated future data, and applying these recommendations in real-time interactions." While the language is clearly analytical in tone, the bottom line of the desired business impact is much the same as definitions we've seen in the pact for the ODS and operational BI: real-time or near real-time decisions embedded into the operational processes of the business. 

Anybody who has heard me speak in the 1990s or early 2000s will know that I was not a big fan of the ODS. So, what has changed? In short, two things: (1) businesses are more advanced in their BI programs and (2) technology has advanced to the stage where it can support the need for real-time operational-informational integration. 

BI Evolution.jpg
The evolution of BI can be traced on two fronts shown in the accompanying figure: the behaviors driving business users and the responses required of IT providers. As this evolution proceeds apace, business demands increasing flexibility in what can be done with the data and increasing timeliness in its provision. In Phase I, largely fixed reports are generated perhaps on a weekly schedule from data that IT deem appropriate and furnish in advance. Such reporting is entirely backward looking, describing selected aspects of business performance. Today, few businesses remain in this phase because of its now limited return on investment; most have already moved to Phase II. 

This second phase is characterized by an increasing awareness of the breadth of information available collectively across the wider business and an emerging ability to use information to predict future outcomes. In this phase, IT is highly focused on integrating data from the multiple sources of operational data throughout the company. This is the traditional BI environment, supported by a data warehouse infrastructure. The majority of businesses today are at Phase II in their journey and leaders are beginning to make the transition to Phase III. 

Phase III marks a major step change in decision making support for most organizations. On the business side, the need moves from largely ad hoc, reactive and management driven to a process view, allowing the outcome of predictive analysis to be applied directly, and often in real time, to the business operations. This is the essence of the behavior called operational analytics. In this stage, IT must become highly adaptive in order to anticipate emerging business needs for information. Such a change requires a shift in thinking from separate operational and informational systems to a combined operational-informational environment. This is where the action is today. This is where return on investment for leading businesses is now to be found. And, simply put, this is why operational analytics is making headlines today--many businesses are ready for it; the leaders are already implementing it. 

This leads us to the second contention: that technology has advanced sufficiently to support the need. There are many ways that recent advances in technology can be combined to do this. In the white papers referenced above, one shows how two complementary technologies, IBM DB2 for z/OS and Netezza, can be integrated to meet the requirements. The other shows how the introduction of columnar technology and other performance improvements in DB2 Advanced Enterprise Edition can meet these same needs. Other vendors are improving their offerings in similar directions. 

So, to paraphrase the "Six Million Dollar Man": we have the business waiting. We have the technology. We have the capability to build this... But, wait. There is one more hurdle. Most existing IT architectures strictly separate operational and informational systems based on a data warehouse approach dating back to the mid-1980s. This split is a serious impediment to building this new environment that demands a tight feedback loop between the two environments. Analyses in the informational environment must be transferred instantly into the operational environment to take immediate effect. Outcomes of actions in the operational systems must be copied directly to the informational systems to tune the models there. These requirements are difficult to satisfy in the current architecture; they demand a new approach. This is beginning to emerge, but is by no means widespread yet. I'll be discussing this topic further over the coming weeks.

Posted May 13, 2013 8:41 AM
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