We’ve covered analytics, and spend analysis, a lot on this blog, but seeing the surge in articles on analytics as of late, and the large number that are missing the point, it seems we have to remind you again that Analytics is NOT Reporting. (Which, of course, would be clear if anyone bothered to pick up a dictionary anymore.)
As defined by the Oxford dictionary, analytics is the systematic computational analysis of data or statistics and a report is a written account of something that has been observed, heard, done, or investigated. In simple terms, analysis is what is done to identify useful information and reporting is the process of displaying that information in a fancy-shmancy graph. One is useful, one is, quite frankly, useless.
A key requirement of analysis is the ability to do arbitrary systematic computational analysis of data as needed to find the information that you need when you need it. Not just a small set of canned analysis on discrete data subsets that become completely and utterly useless once they are run the first time and you get the initial result — which will NEVER change if the analysis can’t change.
Nor is analysis a random AI application that applies a random statistical algorithm to bubble up, filter out, or generate a random “insight” that may or may not be useful from a Procurement viewpoint. Sometimes an outlier is indicative of fraud or a data error, and sometimes an outlier is just an outlier. Maybe the average transaction value with the services firm is 15,000 for the weekly bill; which makes the 3,000 an outlier, but it’s not fraud if the company only needed a cyber-security expert for one day to test a key system — in fact, the insight is useless.
As per our recent post on a true enterprise analytics solution, real analysis requires the ability to explore a hunch and find the answer to any question that pops up when it pops up. To build whatever cube is needed, on whatever dimensions are required, that rolls up data using whatever metrics are required to produce whatever insights are needed to determine if an opportunity is there and if it is worth being pursued. Quickly and cost-effectively in real-time. If you have to wait for a refresh, or spend days doing offline computation in Excel to answer a question that might only save you 20K, you’re not going to do it. (Three days and 6K of your time from a company perspective is not worth a 20K saving if that time spent preparing for a negotiation on a 10M category can save an extra 0.5%, which would equate to 50K. But if you can dynamically build a cube and get an answer in 30 minutes, that 30 minutes is definitely worth it if your hunch is right and you save 20K.)
Analysis is the ability to ask “what if” and pursue the answer. Now! Not tomorrow, next week, or next month on the cube refresh, or when the provider’s personnel can build that new report for you. Now! At any time you should be able to ask What if we reclassify the categories so that the primary classification is based on primary material (“steel”) and not usage (“electrical equipment”); What if the savings analysis is done by sourcing strategy (RFX, auction, re-negotiation, etc.) instead of contract value; and What if the risk analysis is done by trade lane instead of supplier or category. Analysis is the process of asking a question, any question, and working the data to get the answer using whatever computations are required. It’s not a canned report.
Analytics is doing, not viewing. And the basics haven’t changed since SI started writing about it, or publishing guest posts by the Old Greybeard himself. (Analytics I, II, III, IV, V, and VI.)