… comes from a long tradition of guessing and then blaming others for inadequate results.
Scott Adams

And this summarizes how most decisions are made in most companies, especially in the C-Suite. Why? Because, historically, organizations didn’t have much in the way of good data, most data that was available (by the time it was assembled, analyzed, and reported on) was outdated, and most decisions made on the data were iffy if the organization was in a fast-moving business.

So, as a result, the best executives learned that they had to learn to “read the tea leaves”, listen to third parties, ignore them, then go with whatever their gut was telling them … especially if their gut was right more than wrong (and that’s how they got to their position). (And then if something didn’t work out, they blamed the underlings that gave them the analysis that supported their gut feeling.^*)

But in today’s fast moving hyperconnected world where, for every unsatisfied customer, there are three more companies waiting to jump in and satisfy that customer, there’s no time for slip-ups from bad, gut-feel, decisions. There’s no room for guessing.

And, with so many software applications today that can process more data than an organization needs to (as it’s not about how big a data set you can get, but how big a data set makes sense) in real time, every organization should be making decisions based upon good, extensive, data and likely possibilities. No data set is ever complete, no trend or data-based prediction is ever perfect, but when there are so many systems that can bat 950 when most of the best seat-of-the-pant executive decision makers struggle to bat 500, why isn’t the average organization using one of these systems for all decisions?

For starters, every organization should have a good spend analytics system that is capable of working on all spend, and numeric spend-related data, that can also compute trend lines. The organization should know what products are taking off, which are nearing the end of their life-cycle, and which are flat. And it should also be able to analyze market data to see how raw material cost and availability is trending.

But it needs to do more than just analyze organizational spend data, buying trends, and commodity markets. It also needs to analyze market trends in various product lines, including those it does not (yet) produce, and predict how good a new or altered product might sell based upon sales of similar product lines. So not only does it need a spend analytic solution, but it needs a predictive analytic solution as well. Every regular reader knows that the doctor does not believe in AI and that decisions should not be handed over blindly to a system, but that the suggestions of a good system with a good track record, properly configured, should be carefully evaluated, much more so than the gut-feel of a random executive. This is where analytics efforts are focussed, not down blind alleys. Especially when the batting average of these systems is almost double. They’ll miss occasionally, but a good analysis will reveal that (and why, which allow the system to be improved), and, most importantly, analytic effort will be focussed where it makes sense to focus, not on random areas to support random hypothesis with no foundation in reality (which is where a lot of effort is focussed in guess-work run organizations).

^* The really successful executives always asked for multiple analysis until they had data that supported their decision, just in case.