Yesterday SI featured a guest post from Brian Seipel who advised you to Stop Paying for More Analysis than You Need because, simply put, a lot of analytics effort and reports yield little to no return. As Brian expertly noted,

• Sometimes 80% classification at the transactional level is enough
  Especially if you can get 95%+ by supplier or dollar volume. Once it’s easy to see there’s no opportunity in a category (either because it’s all under contract, the spend is low, the spend versus market price on what is classified leaves little savings opportunity etc.), why classify more?
• If you are producing a heap of reports on a regular basis, many won’t get looked at
  Especially if the reports aren’t telling you anything new. Plus, as previously explained on SI, a great Spend Analysis Report is useful 3 times. The first time it is used to detect an opportunity, midway through a project to capture an identified savings opportunity to make sure the plan is coming together, at the end of the project to gauge the realized savings. That’s it.
• A 20% savings isn’t always meaningful
  You’re probably overspending on office supplies by 20%, but it may not matter. If office supplies (because you’ve moved to a mostly paperless office thanks to investments in 2nd monitors and tablets and secure electronic distribution and janitorial supplies is under MRO) is only 10K, and capturing that 2K would take a week of effort running a simple event and negotiating a master contract when your fully burdened cost is 2K a day, is it worth it? Heck no. You don’t spend 10K to save 2K. It’s all about the ROI.
• Speculative analysis on categories you have no control over may not pay out
  Just because you can show Marketing they are overspending by 50% doesn’t mean they are going to do anything about it. If they solemnly believe you can’t measure talent or impact on a spend basis, and you have no say over the final award, you will be fighting an uphill battle and while the argument should be made to the C-Suite, it has to come from the CPO, so until she is ready to take the battle on, spending on an analysis you can predict from intuition and market analysis is not going to give the ROI you need today.

When you put all this together, this gives you some rules about what you should be looking for, and spending on, when you select an analytics system (especially if you are not a do-it-yourselfer, even though there are systems today that are ridiculously easy to use compared to the reporting systems that first rolled out two decades ago).

• Don’t overpay for auto-class
  While no one wants to manually classify transactions (even though a crack analyst can classify a Fortune 500 spend by hand in 2 to 3 days to 95%+ with a powerful multi-level rules-based system with regular expression pattern match, augmented intelligence, and drag and drop reclassification capability), considering how easy it is to manually classify straggler transactions once you’ve achieved 90%+ auto-classification to a best-in-class industry categorization (with 95%+ reliability), don’t overpay for auto-class. In fact, don’t pay extra at all — there are a dozen systems with this feature that can get you there. Only pay extra for a system that makes it easy to accomplish mappings and re-mappings and maintain them in a consistent and non-conflicting manner.
• It doesn’t matter how many reports there are out of the box
  Because, once you get through the first set of projects that fix the spend issues identified, they will all be useless anyway. What matters is how many templates there are for customizing your own. It’s all about being able to define the top X from a subset of categories, geographies, suppliers, departments, users, etc. that are likely to contain your best opportunities, not just the top X spend or transaction volume. It’s about the Schneidermann diagrams and bubble charts on the dimensions that matter on the relevant subset of data. It should be easy to define any type of report you may need to run regularly on whatever filtered subset of data that is relevant to you at the time.
• Totals, CheckSums, and Data Validations Should be Easy
  … and auto-run on every data import. You want to be able to focus in on your mapping and verification efforts where the spend, and potential opportunity, is large enough to be worth your time, know that the totals add up (to what is expected), and that the data wasn’t corrupted on export or import. The system should verify the data is within the appropriate time window, that at least key dimensions (supplier [id], GL code, etc.) are within expected sets and ranges, and source system identifiers are present.
• Built In Category Intelligence is only valuable if you need it
  … don’t pay for community spend intelligence, integrated market feeds, or best-practice templates for categories you don’t source (regularly) or that don’t constitute a significant savings opportunity, especially if those fees are ongoing as part of a subscription. Unless it’s intelligence you will use every month, pay for it as a one-off from a market intelligence vendor that offers that service.

The reality is that second generation spend analysis systems are now a commodity, and you can get a great enterprise platform subscription that starts in the low to mid five figures annually that does more than than most organizations need. (And personal consultant licenses to great products for much, much, less.) Don’t overpay for the software, save it for the analyst who can use it to find you savings.