In April, Ariba Inc. announced plans to acquire privately held Softface Inc., which makes software to help companies keep track of monies spent. It’s the third acquisition by the sourcing and procurement vendor this year.
Softface’s specialty is cleansing and analyzing spending data contained in disparate enterprise systems. Its tools cull data from ERP and legacy systems and convert it into formats that customers can use to identify corporate savings opportunities.
Knowing what is being spent is the first step in gaining control of spend management, analysts say. But few companies really know how much they spend and with whom, according to Tim Minahan, vice-president of supply chain research at Aberdeen Group. That lack of information is costing businesses US$260 billion in missed savings opportunities each year, Minahan said in a statement announcing the Ariba acquisition.
Ariba already had a spending analysis product called Ariba Analysis, but data quality was an issue, according to Pierre Mitchell, a vice-president of research at AMR Research Inc. “The single largest problem in spending analysis for end users is the fragmented, incomplete and inconsistent data that pumps toxic spending information into such an analytic application,” Mitchell wrote in a recent report.
Softface brings to the table specialized technology — including a classifier system, inference engine and knowledge libraries — that has been developed primarily for auto-classifying sparse, messy, spending data into targeted taxonomies, he wrote. Ariba’s bid for Softface makes a lot of sense, according to Mitchell.
The two vendors have worked as partners since last July and have six joint customers. One of those is PPG Industries Inc., which uses the vendors’ tools to analyze US$5 billion each year in spending data, culled from 20 different data sources, according to Jim Polak, director of general purchasing at the Pittsburgh manufacturer. Polak said in a statement that he expects the combined technologies will help PPG save millions annually just by improving the quality of spending data.
