Today’s post is from Eric Strovink of BIQ.

If I have learned one thing during my career as a software developer and software company executive, it’s this: contrary to what I believed when I was a know-it-all 20-something, there are a lot of clever people in the world. And clever people make smart decisions (for example, reading this blog, which thousands do every day).

One of those decisions is the decision NOT to perform low-probability ad hoc data analysis. It’s a sensible decision. Sometimes it’s based on empirical study and hard-won experience, and sometimes it’s a gut feel; but either way, the decision has a strong rational basis. It’s just not worthwhile.

A picture is helpful:

Click image to enlarge

The above shows the expected value of an ad hoc analysis of a $100K savings opportunity. On the X axis is the number of days required to prepare and analyze the data; on the Y axis, the probability that the analysis will be fruitful. I chose a $700 opportunity cost per analyst-day; choose your own number, it doesn’t really matter.

Note that the graph is mostly “underwater”; that is, the expected value of the analysis is largely negative. Unless the probability of success is quite high, or the time taken to perform the analysis is quite low, it’s simply not a good plan to undertake it.

We are all faced with the decision, from time to time, whether to explore a hunch or not. However, an analyst can only work for 220 days per year. Sending a key analyst off on a wild goose chase could be a serious setback, so it’s a risky decision, and we don’t do it, and so our hunches remain hunches forever.

But what if it wasn’t risky at all?

Nothing can be done about the “probability that the analysis will be fruitful”; that’s fixed. But plenty can be done about the “number of days required to prepare and analyze data.” Suppose a dataset could be built in 5 minutes, and analyzed in under an hour? This turns the expected value of speculative analysis sharply positive. Suddenly it is a very good idea indeed to perform ad hoc analysis of all kinds.

And that’s good news. Because there is a ton of useful data floating around the average company that nobody ever looks at. Top of the list? Invoice-level detail, from which all kinds of interesting conclusions can be drawn. Try this experiment: acquire invoice-level (PxQ) data from a supplier with whom you have a contract. Dump it into an analysis dataset, and chart price point by SKU over time. Chances are, like most companies, you’ll find something very wrong, such as prices all over the map for the same SKU (ironically, sometimes this happens even if you have an e-procurement system that’s supposed to prevent it). If you have a contract, only one of those prices is correct; the rest are not, and represent money on the table that you can recover trivially.

Of course, please don’t spend weeks or months on the exercise, because then it won’t pay off. Instead, find yourself a data analysis tool with which you can move quickly and efficiently — or a services provider who can use the tool efficiently for you (and thus make a contingency-based analysis worthwhile for both of you). Bottom line: if you can’t build a dataset by yourself, in minutes, you’ll end up underwater, just like the graph.

Next installment: Crosstabs Aren’t “Analysis”

Previous Installment: It’s the Analysis Stupid

Share This on Linked In

Today’s post is from Eric Strovink of BIQ.

James Carville is not my favorite person, but he’s a funny man. And the above bastardization of his (in)famous Clinton campaign quote seems quite apropos, given the current frenetic level of marketing activity around “spend analysis” (I’m always amused by vendors using this term, because… excuse me for asking… “where’s the “analysis?”)

So why is there so much spend analysis marketing activity, all of a sudden? I suspect it’s “Oracle Terror”. For the last nine years, I’ve watched spend analysis vendors promote their “product” — typically a service masquerading as a product — using the same tired strategy: “We classify data better than [those other guys].” Problem is, when you spend so much time and effort dumbing down spend analysis to a simple-minded premise, you open the door for almost anyone, even a sleepy ERP vendor, to steal your lunch. And that’s exactly what has happened. Oracle has neatly synthesized all of the “classification” messages together, packaging them up with some Silicon Valley marketing magic, and the legacy spend analysis vendors are in a panic. You’re absolutely right, folks, Oracle’s messaging is better than yours. Smarter, more sophisticated, priced innovatively — it’s both ironic and funny. The only surprise is that this didn’t happen years ago.

But here’s the point: real spend analysis is so much more than classification, that the whole classification discussion is absurd. It has always been absurd. Classification-centrism is the Titanic of spend analysis, aiming squarely at a snowball on the top of the iceberg, while completely ignoring the massive value beneath. Nevertheless, relentless classification-oriented marketing over many years has warped end-user perceptions, and carried analysts right along with it. Current analyst firm surveys are spending over 90% of their time on classification questions, Pandit’s hopelessly off-target book (previously dissected and dismissed by Sourcing Innovation) is garnering new attention, and so on.

My iconoclastic point of view has been outlined in these (and other) pages before, but put very simply, it’s this: Classification is easy. Armed with appropriate tools, any intelligent person (your admin, for example) can be trained to do it effectively, in about an hour; and the rules they generate can be applied automatically to new transactions, forever after. When you stop to consider that sourcing consultants have been performing effective spend analysis for years, using nothing more than pencil and paper, it’s obvious that the classification Emperor really doesn’t have any clothes.¹

In fact, true value lies in the analysis that you perform. Value is about results, and results come from analysis, not from a data classification process that is just a baby step toward value realization, and one that may not even be relevant. For example, consider that spend classification is really only useful for A/P data. There are many higher-value sources of data lying around, and many datasets can be built from them. In most of those datasets, classification has no place at all. By the way, how many spend datasets do you plan on building? One? Just on A/P data? Then you are missing out on value, by a wide margin.

In this series, I’ll discuss the requirements for ad hoc data analysis, and the very real value that results from it. Spend analysis, at the end of the day, is just data analysis; so it’s critical that your data analysis tools provide the necessary power and flexibility to make you successful.

Next installment: Why Data Analysis Is Avoided

¹Ironically, based on the datasets we’ve seen from customers who have walked away from their classification-centric vendors, talking a great game on classification doesn’t necessarily mean delivering great classification.

Share This on Linked In