Despite its crucial role in corporate success, human capital has not received the rigorous study it deserves and workforce analytics has been underutilized. Workforce analytics is not about simple measurement — “counting heads,” reporting turnover, cataloguing employee knowledge and skills, reporting engagement scores, or buying analytics software. Instead, it is a systematic approach used to define workforce problems, test successful solutions, and make better decisions. This article provides six steps for maximizing the value of strategic workforce analytics: 1) Frame the central problem. 2) Apply a conceptual model to guide the analysis. 3) Capture relevant data. 4) Apply analytical methods. 5) Present statistical findings to stakeholders. 6) Define action steps to implement the solution. The authors leverage their experience at IBM and elsewhere to explain how embedding workforce analytics into companies requires the integration of timely, accurate, and multi-purpose data; a flexible and responsibe set of processes and technical infrastructure; individuals who possess the functional, statistical, and business-oriented skills needed; and adequate governance structures.

This is an MIT Sloan Management Review article. To understand the challenges and opportunities associated with the use of business analytics, MIT Sloan Management Review, in collaboration with the IBM Institute for Business Value, conducted a survey of more than 3,000 business executives, managers and analysts from organizations located around the world. The survey was part of the 2010 New Intelligent Enterprise Global Executive Study and Research Project, which attempts to understand better how all organizations are trying to capitalize on information and apply analytics today and in the future. One of the most significant findings is that there is a clear connection between performance and the competitive value of analytics. Survey respondents who agreed that the use of business information and analytics differentiated them were twice as likely to be top performers. Three stages, or capability levels, of analytics adoption emerged from the research: aspirational, experienced and transformed. The article provides a comprehensive description of each, enabling organizations to identify where they fall in the continuum. In addition, the authors include suggestions for the best entry points and techniques for each level, and measures to avoid the most common pitfalls. Based on insights from the survey, case studies and interviews with experts, the authors also describe an emerging five-point methodology for successfully implementing analytics-driven management and rapidly creating value -as leading businesses are already managing to do. These include (1) focus on the biggest and highest data priorities, (2) within each of those priorities, start by asking questions, not by looking at the available data, (3) embed insights into business processes to make them more understandable and actionable, (4) keep existing capabilities and tools while adding new ones and (5) develop an overarching information agenda that enables decision making and strategy for the future.