Here at the nextSource blog we’ve been following the growing interest in Big Data among those responsible for workforce management. Specifically, we’ve been discussing the extent to which Big Data can be, and in some cases, already is, used to derive more accurate and efficient workforce planning within mid-to-large-sized organizations. While we’re as excited as the rest of the field by the promise and potential of Big Data as it applies to sourcing candidates, we’re also pragmatic in our assessment of whether or not such activity seems feasible.
We’ve noted the number of significant obstacles that stand in the way of leveraging big data to yield truly effective workforce planning and concluded that until these cutting edge practices matured a bit more, their results would be middling at best. Minimally, as discussed in our recent post titled, “Dr. Ian Malcolm on Big Data”, we underscored the potential challenges in automating sourcing of an inherently complex commodity –human capital. The variability of job/skills-related nomenclature alone makes it difficult to derive apples-to-apples comparisons between two candidates for the same position; a challenge the industry has long been aware of.
Well, it seems that none other than the SIA (Staffing Industry Analysts) has addressed the monumental task of making Big Data mining an effective reality for such things as sourcing human capital. At the recent SIA sponsored Contingent Workforce Strategies (CWS) event in Las Vegas, the SIA announced the rollout of a new Talent Data Exchange. The new, membership-based data aggregation service is designed to enable organizations to compute the cost of non-employee labor and test the validity of their workforce strategies with greater precision than ever before.
According to an article at StaffingIndustry.com, “The tool will accomplish this by breaking work assignment needs into granular bits and pieces to define job functions more precisely, so companies can get a comprehensive and accurate picture of the labor market.”The standardization in terms for job functions has been the missing key factor depressing the efficacy of Big Data strategies for human capital management. Getting even a simple plurality of staffing suppliers to adopt a universal standard has long been an ideal to be pursued (and unlikely to be achieved).
Perhaps the tantalizing promise of Big Data science has swayed enough of the industry to adhere to new standards like those offered by the Talent Data Exchange. It doesn’t hurt that the well-regarded SIA is behind the push to do so. Ultimately, those early adopters within staffing organizations and workforce management practitioners across all industries will be rewarded for the risk they may take by leaning on this new and relatively untested technology. However, if the results yielded by Big Data mining in other industries serve as any guide, there will be much to celebrate when the results start to present themselves. Just ask Billy Beane and the 2002 Oakland A’s!