Leveraging People Analytics for an Adaptive Complex Talent Management System

Abstract

Data analytics inform many facets of our everyday life, from Netflix recommendations to the ads that pop up on our social media feeds. This same technology can make an enormous difference in human resource and talent management enabling individuals to market their skillsets and organizations to describe their job requirements down to a granular level of detail in the hopes that searches, optimization algorithms, and simple recommendation engines can guide them towards an optimal decision for talent management – the right person in the right job at the right time. While these analytic tools are important to optimizing decisions, it is not always evident where to apply them for the best possible effect. The Army advanced analytics in a way that allows them to forecast their ability to fill critical job requirements over time by forecasting new acquisitions, promotions, and losses at the aggregate level. However, that system falls far short of being able to match people to positions in an optimal manner and results in long lag times when it comes to meeting emerging requirements. A new data collection system identifying both unit-required and individual-possessed knowledge, skills, and behaviors (KSBs) will enable the Army to make forecasts and fill positions much more rapidly (along with assigning the best person to the position) provided the data is available to decision makers at the right time to best support talent management decisions. This paper outlines the new structure of this complex human resource (HR) system from data collection to analytic tools along with showing how modeling this system illustrates an adaptive complex system based on data engineering.