Emptoris: Readying the Spend Visibility Armaments for Battle

In the past few weeks, both Emptoris and Ariba have announced some significant enhancements to their respective spend visibility and analytics products. I'll tackle Ariba's enhancements in a separate post later this week, and focus on Emptoris enhancements in this one. But before diving in, with apologies to both readers and vendors, I should say that I've been unusually slow on the uptake covering these enhancements owing to a large backlog of blog fodder of late (I also have two forthcoming feature entries on how vendors aPriori and Akoya are working to bridge the design engineering / direct materials sourcing intelligence and costing gap, so keep your eyes peeled for these as well).

But I digress ... back to Emptoris, and their news. Given that Purchasing did a good job summarizing Emptoris briefing slides already, I'll focus this post on analyzing the news rather than relaying the specific enhancements in detail, which, in their own words, focus on such areas as core data management capabilities, spend classification, data extraction consulting, and the extension of existing BI and DW capabilities.

In my view, Emptoris has realized the core limitations of many earlier generations of spend visibility approaches which treated such efforts as one-off technology "island" approaches. In their current iteration, Emptoris' capabilities are directed at helping companies integrate and extend spend visibility and analysis capabilities on a continuous basis regardless of existing back-end technology, BI, or data warehousing environments. And they're focused on enabling visibility in a relative quick timeframe (I know some like BIQ would disagree that 90 days is quick, but in the traditional enterprise applications world, 3 months can go by in the blink of an eye, and is commendable).

From an enabling technology perspective, Emptoris is now offering different types of auto-classification algorithms including Bayesian, natural language processing and nearest neighbor. These are extended by rules-based learning and machine learning capabilities to constantly improve the accuracy of the cleansing and classfication capabilities. In my view, what's important here from a practitioner perspective is not the specific technique -- leave the sausage making to the pros, that's what I say -- but the ability to increase the percentage of spend which can be accurately auto-classified and analyzed on a continuous basis.

On the pragmatic level, Emptoris has some real advantages in their procure-to-pay contract monitoring capabilities (gained from leveraging diCarta's contract management with the rest of the Emptoris suite). On top of helping tee-up strategic sourcing opportunities, this can help companies spot control violations (e.g., contractual payment term violations) and potential fraud (e.g., rounded payments, phantom vendors, PO changes after approval) before it's too late to take a rapid yet forward-looking corrective approach. This proactive, results-driven compliance angle to spend visibility and analytics is one that should have strong appeal for more advanced organizations which are looking for next level Spend Management savings and cost avoidance opportunities.

Jason Busch

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