One of McKinsey's more astute observations on the power of Big Data is the ability to leverage it in "segmenting populations to customize actions." In this regard, McKinsey suggests, "Big data allows organizations to create highly specific segmentations and to tailor products and services precisely to meet those needs. This approach is well known in marketing and risk management but can be revolutionary elsewhere ... Even consumer goods and service companies that have used segmentation for many years are beginning to deploy ever more sophisticated big data techniques such as the real-time microsegmentation of customers to target promotions and advertising."
There are many corollaries in procurement as well. For one, the ability to examine suppliers to understand potential profiles that may suggest a subset of a vendor population is riskier than others -- and to then take action based on this information and/or work on further segmentation (e.g., into categories of suppliers that are replaceable or those where intervention is essential). The same is also true in leveraging Big Data to target internal campaigns to drive adoption for P2P systems, for example, based on past behaviors.
We also believe that Big Data can also have an impact on our ability not just to make one-off decisions for larger spend categories but to target highly effective micro-sourcing strategies that break the "three bids in a box" or reverse auction mold. In this research brief on Spend Matters PRO,
The Meaning of Big Data for Procurement and Supply Chain: A Fundamental Information Shift, we note that the ability to leverage optimization not as a fancy add-on to basic e-sourcing suites or for large-scale, infrequent events requiring an approach to network optimization (e.g., LTL, full truckload spend) but for common sourcing events will be one of the first areas where Big Data approaches truly become predominant in leading procurement organizations.
Further, big data sourcing approaches will allow companies to not only make better award decisions with an eye toward total cost but to optimize for lower cost structures before sourcing events themselves (e.g., by suggesting changes to specifications and tolerances, where possible, enabling and starting collaborations between design/engineering, business owners, and procurement teams) on a category (and sub-category) level. Think of it as "personalized sourcing medicine" for a specific category need.
Next up on McKinsey's list (and our procurement related extension and exploration): "Replacing/supporting human decision making with automated algorithms."