Posted March 31, 2010 6:38 PM
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I will use this blog to discuss business challenges and how technologies like analytics, optimization and business rules can meet those challenges.
James is the CEO of Decision Management Solutions and works with clients to automate and improve the decisions underpinning their business. James is the leading expert in decision management and a passionate advocate of decisioning technologies – business rules, predictive analytics and data mining. James helps companies develop smarter and more agile processes and systems and has more than 20 years of experience developing software and solutions for clients. He has led decision management efforts for leading companies in insurance, banking, health management and telecommunications. James is a regular keynote speaker and trainer and he wrote Smart (Enough) Systems (Prentice Hall, 2007) with Neil Raden. James is a faculty member of the International Institute for Analytics.
In a recent post, Big BI is Stuck: Illustrated by SAP BusinessObjects Explorer, Stephen Few took issue with the claims of SAP BusinessObjects Explorer. I have not spent any time with the product so I am not going to discuss his specific criticisms but I was struck by a caution he added in the post:
Don't mistake what I've written as a case against Big BI in favor of Small BI. It is entirely possible for large BI vendors to provide effective tools for data sense-making [analytics]. To do this, they need to switch from a technology-centric engineering-focused approach to a human-centric design-focus approach, and base their efforts on a deep understanding of data sense-making. Most of the small BI vendors have done no better in cracking this nut than the big guys. They might be more agile due to their small size and thus able to bring a new product to market more quickly, but when they approach the problem in the same dysfunctional way as the big guys, they fail just as miserably. Just like politicians who sell themselves as "not like the guys in Washington," new players in the BI space often point to the failures of the big guys and then go on to do exactly the same. I am not making a case of small vs. big, but of clear-headed, informed, and effective vs. an old paradigm that doesn't work for the challenges of data sense-making.
It seems to me that part of what Stephen is getting at here is a need to focus not on the technical capabilities but on the ability of a tool to support better decision-making. I see his post as pointing out a key reason I believe companies must begin with the decision in mind, figuring out what kinds of analytic insight will help improve a specific decision and drilling back into their data from there. In contrast, most companies today start with the data and go forward - and most BI tools (big BI, small BI, in-memory BI, SaaS BI) work this way too.