Workforce Analytics: Making the Most of a Critical Asset
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.
Collection: Ivey Business School (Canada)
Ref: IVEY-9B12TD03-E
Format: PDF
Number of pages: 7
Publication Date: Aug 1, 2012
Language: English
Description
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.
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