Intelligent Enterprise

Perhaps you noticed that I renamed this blog "Competing on Decisions" from "Addicted to BI." Now I'll admit that the latter had a certain anti-chic appeal with its allusion to substance abuse, but frankly, I'm recovering from my BI addiction, so it's time to move on. I've also come (slowly it seems) to the conclusion that informing people (BI) is part of an incomplete cycle. If we as a company make an investment in BI, it isn't the informing of people that matters, it's what happens next. The decisions.

Last year, James Taylor and I wrote a book, "Smart (Enough) Systems," to make the case for creating "decision services" in order to automate certain kinds of high-volume, low-latency decisions. Dubbed EDM for enterprise decision management, we laid out the sort of reference architecture for getting this done, which included predictive modeling, business rules engines, some form of either process automation, or at least a smooth handoff to operational systems and back end analytics to both evaluate the quality to the decisions and manage some form of adaptive control of the decision models (in other words, test new models and compare to the results of the existing ones).

In short, our goal was to point out that being informed by analytics is great, but in the end, it's the decisions that count. For the kinds of decisions we wrote about, small operational decisions like granting a credit line increase or making a next-best-action recommendation to a live customer service call, it isn't a very tough concept to sell. There are many systems that already do this to some degree, though not very well and not very efficiently. Part of the EDM pitch is how to architect these systems for agility, consistency, cost, precision and speed. But what we didn't address in the book and what needs addressing is the whole concept of decisions in general.

How many times have you heard the statement that the data warehouse and/or BI will "get the right information to the right person at the right time to make better decisions?" Other than, say, an air traffic controller, have you ever personally been directly affected by this implied phenomenon? In fact, does anyone even know what a better decision is? Has anyone ever measured it? More importantly, have we even attempted to understand how decisions are made or to what extent the timely delivery of information matters, if at all?

Here are some of the topics I'll be blogging about here. If you have suggestions, please let me know either here or directly:

Decision Theory — There is a considerable body of work on this topic, but in over two decades of experience in decision support and BI, I haven't seen it discussed a single time when proposing systems to aid people in decision making. Maybe the IT department or the systems integrator has better intuition about this than people who have studied it, but I'm skeptical

Current Research — What are people learning about how decisions made?

Decision Metrics and Audits — Tracking the decision process and measuring it (outcomes) and auditing the process to see if the process is working the way it should

Field Study — What are people actually doing, despite the theory and research? At Smart (enough) Systems, we'll be conducting research into this area later this year (vendors, let us know if you want to be a sponsor)

Decision Quality — This is tricky, because in a formal sense, a good decision is not related to its outcome. A good decision is a logical one based on the available information (and "available" information is pretty fungible, just look at the WMD mess), so measuring decision quality and decision effectiveness are two different things. It may have been a good decision to go surfing in Thailand as opposed to base jumping off the Golden Gate Bridge, but the outcome was skewed by a tsunami.

Decision Streams — You can't evaluate decisions in isolation. Decisions flow from one to another.

So whether you put your belief in Bayesian networks or you're just "the decider," stay tuned.

—NR