Human capital analytics: too much data and analysis, not enough models and business insights

Alec Levenson (Center for Effective Organizations, University of Southern California, Los Angeles, California, USA)
Alexis Fink (Human Resources, Talent Intelligence and Analytics, Intel Corporation, Hillsboro, Oregon, USA)

Journal of Organizational Effectiveness: People and Performance

ISSN: 2051-6614

Publication date: 5 June 2017

Abstract

Purpose

The purpose of this paper is to address the barriers to the rapid development of effective HR analytics capabilities in organizations.

Design/methodology/approach

Literature and conceptual review of the current state of HR analytics.

Findings

“HR analytics” is used to refer to a too-wide array of measurement and analytical approaches, making strategic focus difficult. There is a misconception that doing more measurement of HR activities and human capital will necessarily lead to actionable insights. There is too much focus on incremental improvement of existing HR processes, detracting from diagnosing the problems with business performance. Too much time is spent on mining existing data, to the detriment of model building and testing, including collecting new more appropriate data. Too much energy is consumed with basic tasks of data management. Stakeholders avoid action by nitpicking the details of the data.

Practical implications

Practitioners who follow the guidance provided should find that their application of HR analytics leads to more relevant and actionable insights.

Social implications

More effective application of HR analytics should lead to better decision making in organizations and more effective resource allocation.

Originality/value

A new look at the field of HR analytics that synthesizes the research literature and current practice in organizations.

Keywords

Citation

Levenson, A. and Fink, A. (2017), "Human capital analytics: too much data and analysis, not enough models and business insights", Journal of Organizational Effectiveness: People and Performance, Vol. 4 No. 2, pp. 145-156. https://doi.org/10.1108/JOEPP-03-2017-0029

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Publisher

:

Emerald Publishing Limited

Copyright © 2017, Emerald Publishing Limited

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