Data analysis that should be performed is often avoided, because it carries too much risk for the stakeholder. Let's consider two examples.

(1) Suppose I am an insurance company CPO with access to one or more analysts; and that some number of analyst hours are available to me, in order to investigate savings ideas that occur to me from time to time.

Now, suppose I begin wondering whether the company's current policy of auctioning off totaled vehicles is wise. I reason: what if we're actually losing money on some of these wrecks? I think: perhaps there is a closed-form sheet I can provide to my adjusters that lists make/model/year and gives them an auction/no auction decision; perhaps that sheet would save the company money.

My problem is that I'm not entirely sure that this idea is worthwhile. Perhaps the company makes money on almost every auction, and I will waste the valuable time of one of my analysts by chasing phantom savings that aren't there. I must weigh not only the cost of the analysts' time, but also the lost opportunity cost associated with the analyst chasing a low-probability idea -- against using that analyst for some immediately useful purpose, such as prettying up a report that the CEO complained about, or double-checking a number for the CFO.

I reason as follows: if I think it's going to take longer than X hours to determine whether this is a good idea or not, then I can't chase the idea. I don't have the resources to do so, and perhaps I never will.

However, if I know that my analyst can load up a new spend dataset with auction costs and revenues within minutes; and I know that a subsequent slice/dice by make/model/year would be trivial; and I know that a report of precisely the format I need could be produced without significant effort; then the decision is a no-brainer. I make the decision to analyze rather than the decision not to analyze.

(2) Suppose I am a CPO with a large A/P spend data warehouse available to me, but the particular question I want answered is not supported by the dimensions and hierarchies that it contains. Those dimensions and hierarchies were built perhaps by the IT department, or perhaps by a spend analysis vendor, or perhaps by a team of internal support people who are responsible for maintaining the warehouse; and those dimensions and hierarchies were the result of a number of committee decisions that will be difficult to alter. Furthermore, the data warehouse is being used by hundreds of other people in the organization -- which means that I'll need the permission of all those potential users to change or add anything.

I reason as follows: I know it will take weeks, perhaps months to convince my colleagues to change the dataset organization, even if they can be convinced to do so; and once they are convinced, it will take even longer for whomever it is that controls the warehouse to implement the changes, perhaps at high cost that I will need to justify; so is it really worthwhile for me to pursue using the warehouse to answer my question?

I decide: probably not. Which means that my analyst will have to spend many hours extracting raw transactions from the warehouse; re-organizing them herself on her personal computer, using Access or other desktop tools; and then creating the report that I need. As above, I reason as follows: if I think it's going to take longer than X hours to answer my question, then I'll live without the answer rather than risk wasting precious analyst cycles.

However, if I know that my analyst can tweak her private copy of the dataset, adding dimensions and changing hierarchies in just a few minutes, and that my answer will be available shortly thereafter, I make the decision to analyze rather than the decision not to analyze.

A flexible and powerful spend analysis system can make a huge psychological difference to an organization. It changes the analysis playing field from "we just can't afford to look into this" to "of course we should look into this!"

Next installment: Common Sense Cleansing