— Posted by guest blogger Carlos Serrano-Morales
There are strong indications that “Decision Management” is graduating out of the buzzword world into the standard reality of the design and implementation of systems that embed business logic. Technologies such as business rules management systems, predictive analytics, decision optimization, are now found to be combined in interesting ways to automate and manage business decisions for mission-critical applications. Experts such as Carole-Ann Matignon see the dawn of the decision management era just around the corner and my experience delivering systems that automate and improve decisions certainly supports this view. Analysts such as Yvonne Genovese (//blogs.gartner.com/andrew_white/2009/08/10/what-is-your-pattern-based-strategy/) and Jim Sinur (http://blogs.gartner.com/jim_sinur/2010/01/13/decisions-the-under-served-and-under-supported-partner-to-forward-progress/) have been pushing forward the Pattern Based Strategy paradigm, which is closely tied to decision management .
This is, of course, good news to the industry at large, but particularly good news for those who have early-on bet on the fact that they could lower their risks and increase their value by automating the decisions made in their business applications, managing them through time, and improve them through time. Their insight has given them a huge competitive advantage: just think of Amazon’s bet on smart search and recommendation engines. Or think about how large credit card companies differentiate from each other by the way they better leverage their portfolio of customers and account.
The drive towards more and more decision management is also fraught with challenges.
Some of these are technical: how will we really deal with the “big data” and the “real time” data flows that result from the mass consumer access to web commerce and social network eco-systems?
But others are more complicated: properly automating decisions in such a way that they are manageable, can be monitored for their business effectiveness, and improved through time requires a way of looking at the problem that combines business knowledge with understanding of data and some level of understanding of how decisions tie to business outcomes. In other words, it requires more than being able to ask a BI department for reports and looking at reports. It requires being able to discuss, even at a high level, the decision models that explicitly or implicitly support the way the decision being automated is specified and implemented.
Which leads me to the following: the industry seems to be running ahead of the academia in this space and we need to fix that.
We do not seem to benefit from a stream of young graduates who have even heard of decision management, let alone been trained on it. If you do a Google™ search on “decision management curriculum”, you get exactly one result (as of 1/25/2010 – the reference is for the ZhongYing International Business University), whereas the search on the more traditional “information management curriculum” yields over 77,000 results. A search on “decision modeling curriculum” will also yield one single result.
It has been my observation that we tend to see some level of focus on decision management related teaching in essentially three areas: MBA programs, medical schools, systems engineering. But in each one of these cases, what we tend to see is focus on one or another of the sub-areas: mostly analytics (focusing on dashboarding and analysis of past data for situational analysis) and optimization (optimum allocation under constraint problems).
This is not enough and will be a challenge for the growth of this discipline.
I believe that “Decision Management” should become part of the academic curriculum not just for the MBAs of the world, but also for the CS students who will be entering the world of business applications which will live or die by the quality of their management of the decisions they automate. Let’s spend less time talking datamarts and BI, and more time discussing decision management.
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