I recently wrote an article for the IIA on decisions, decision management and analytics. This was prompted by Tom Davenport’s recent interview on the Sloan Business Review on Reengineering your decision making processes about analytics and how companies make decisions. This interview also prompted Boris Evelson of Forrester to write this blog post on decision management being possibly the last frontier in BI. Boris made a couple of excellent points in his post.
First he pointed out that, while companies should consider decision making something they should understand and systematically improve, all decision making is not the same. First decisions can be divided into those that are fairly structured and follow well defined rules and approaches and those that are more unstructured and collaborative. Structured decisions tend to lend themselves to precise descriptions of how to make the decision and repeatable analytics. Collaborative or unstructured decisions tend to lend themselves to exploration and visualization tools in contrast. Decisions can also be divided into automated and manual decisions.
Now, some time ago Neil Raden and I did some work on the characteristics of decisions. Boris’s collaborative/structured division combines two – the approach to making the decision and how repeatable the decision is. Other characteristics that really matter when it comes to deciding how to automate or support decisions include how measurable the decision is, how long it takes to see whether you made a good decision or a bad one, and how much difference there is between a good one and a bad one.
Whether you currently automate a decision or not, it seems to me, is a more transient characteristic of a decision – a consequence of other more fundamental ones. Companies should not be dividing up their decisions into manual and automated so much as conducting a decision audit or decision discovery to understand what decisions they have so they can make the right automation and decisioning technologies choices.
The importance of ongoing measurement and analysis, however, is an area where Boris and I are in strong agreement. The three phases of decision management are decision discovery to find the decisions that matter, decision services to build components to handle those decisions and then decision analysis to ensure that you continue to improve decisions over time.
As Boris points out, this last one is critical. If you don’t track the results of decisions you will never know what works and what does not. This is part of the reason I think it is so important to map decisions to Key Performance Indicators or KPIs so that you understand how each decision contributes to the measures that matter to you. Beyond tracking, though, if you don’t create a feedback loop so that you can improve decisions based on this your decision making will stagnate. This means it will get less good – decisions cannot be static as a good decision is good only in a context and that context changes continually. I would add that experimentation is also important. You need an ability to create challengers to your current decision making approach, test them on some decisions and compare results to see if a new approach would be preferable. If you look at companies successfully using analytics they have all of these – good decision results tracking, a formal feedback loop to keep improving a decision and an ability to challenge existing decision making with new and innovative approaches.