Continuing from an earlier post on the Journey of BI systems from being just decision support to decision automation. On this topic I read a couple of interesting articles or posts , first was a blog post by Tom Davenport the author of Competing with Analytics and an article titled "What Good are Experts" in the document "Breakthrough ideas for 2008" by HBR.
As discussed in the first post Data/information from BI systems are supposed to aid/assist management/information worker make decisions. But is this what is really happening?
As indicated by Tom Davenport
When my former Accenture colleague Jeanne Harris and I surveyed companies in 2002, and again in 2006, about their enterprise systems, “better decision-making” was the objective most frequently mentioned as the reason for implementing the systems. Of course, that’s an easy response to give, since virtually no company measures the quality of decisions. Yet in interviews with some of the companies, we found not a single effort to actually connect enterprise information with decisions.
As a solution to this he suggests the following before any BI project is embarked in an organization
Projects need to start with an inventory of key decisions. For each one, a company could note who’s responsible for making it, how often it gets made, and what information and knowledge are necessary to make it well. A second approach would be to precede any effort to build business intelligence solutions—the type of IT that is most closely related to decision-making—with identification of the key decisions that would be made on the data and analysis. A third would be to begin assessing managers not only on the outcomes of their decisions, but on the processes they employed.
While on this topic lets take a deep dive into types of decisions. In the article "What Good are Experts" problems are categorized into two types
The Business Intelligence solutions of the future needs to be able to address all these 3 solution types, that is should be able to support rule based engines for computer driven decision making, should be able to support internal prediction markets/model which leverage the Wisdom of the Crows and finally be able to provide the experts all the required information so that they can better leverage their expertise and provide the best solutions.