In fact, here is my issue with his complaint. As an analyst, every software vendor — from fledgling sourcing startups to old-school ERP and BI providers — touted their spending analysis capabilities. The caveat: you just needed to give them the data in a cleansed, classified, and structured format. Or, pay them or a systems integrator gobs of money to manually aggregate and structure your data for analysis. (And forget about data enrichment.) In short, most vendors pitched building a data cube or data warehouse from which you could run analyses and reports as spending analysis. They were wrong. And they confused the marketplace (possibly intentionally).

This distinction is illustrated in the real-world story of a global technology company I once counseled. Like many enterprises, this company felt it’s spend visibility and intelligence problems would be solved by standardizing on a single ERP solution globally. To its credit, the company achieved global deployment. Unfortnately, it sooned learned that the ERP system didn’t provide the granular-level visibility sourcing managers required to truly understand its spending position. “We knew IT was a big spending area for us, but [the system] couldn’t provide us visibility into whether we were buying general-line PCs or SPARC stations,” one supply management executive told me.

Next, the company adopted a datawarehouse, believing they could slice and dice spend data any way they wanted. But they soon learned that analyzing incomplete or poorly classified data is a fools errand. “Our buyers were spending their time pulling data into [Excel] pivot tables, classifying it, and putting it back into the datawarehouse for analysis,” said the exec.

Finally, the company adopted an automated spend data management tool that aggregated, cleansed, and classified spend data at a detailed level to the UNSPSC industry standard. The company also enriched classified data against a third party information source. Now the company refreshes its spend data on a monthly basis.

With this enhanced and timely intelligence, the technology firm was able to better understand and leverage its spending for improved sourcing, supply base rationalization, and compliance initiatives. They also changed buying policies to ensure for demand and specification management purposes. According to the exec: “We are now in a position to truly understand and leverage our spending for strategic, fact-based decisions.”

The distinction between spend data and spend intelligence is an important one. Whether it warrants an entirely new solution marketspace is debatable. Regardless, bravo Aberdeen for calling out the difference between dumb data and actionable intelligence.