19 Jun. 2017 | Comments (0)
Managing HR-related data is critical to any organization’s success. And yet progress in HR analytics has been glacially slow. Consulting firms in the U.S. and Europe lament the slow progress. But a Harvard Business Review analytics study of 230 executives suggests a stunning rate of anticipated progress: 15% said they use “predictive analytics based on HR data and data from other sources within or outside the organization,” while 48% predicted they would be doing so in two years. The reality seems less impressive, as a global IBM survey of more than 1,700 CEOs found that 71% identified human capital as a key source of competitive advantage, yet a global study by Tata Consultancy Services showed that only 5% of big-data investments were in human resources.
Recently, my colleague Wayne Cascio and I took up the question of why HR analytics progress has been so slow despite many decades of research and practical tool building, an exponential increase in available HR data, and consistent evidence that improved HR and talent management leads to stronger organizational performance. Our article in the Journal of Organizational Effectiveness: People and Performance discusses factors that can effectively “push” HR measures and analysis to audiences in a more impactful way, as well as factors that can effectively lead others to “pull” that data for analysis throughout the organization.
On the “push” side, HR leaders can do a better job of presenting human capital metrics to the rest of the organization using the LAMP framework:
- Logic. Articulate the connections between talent and strategic success, as well as the principles and conditions that predict individual and organizational behaviors. For example, beyond providing numbers that describe trends in the demographic makeup of a job, improved logic might describe how demographic diversity affects innovation, or it might depict the pipeline of talent movement to show what bottlenecks most affect career progress.
- Analytics. Use appropriate tools and techniques to transform data into rigorous and relevant insights — statistical analysis, research design, etc. For example, understanding whether employee engagement causes higher work performance requires analysis beyond correlations that show the association, to be certain that the reason is not simply that better performers become more engaged.
- Measures. Create accurate and verified numbers and indices calculated from data systems to serve as input to the analytics, to avoid having “garbage in” compromise even with appropriate and sophisticated analysis.
- Process. Use the right communication channels, timing, and techniques to motivate decision makers to act on data insights. For example, reports about employee engagement are often delivered as soon as the analysis is completed, but they become more impactful if they’re delivered during business planning sessions and if they show the relationship between engagement and specific focus outcomes like innovation, cost, or speed.
Wayne and I observed that HR’s attention typically has been focused on sophisticated analytics and creating more-accurate and complete measures. Even the most sophisticated and accurate analysis must avoid being lost in the shuffle by being embedded in a logical framework that is understandable and relevant to decision makers (such as showing the analogy between employee engagement and customer engagement), or by communicating it in a way that engages them through stories, analogies, and familiar examples. My colleague Ed Lawler and I compared the results of surveys of more than 100 U.S. HR leaders in 2013 and 2016 and found that HR departments that use all of the LAMP elements play a stronger strategic role in their organizations. Balancing these four push factors creates a higher probability that HR’s analytic messaging will reach the right decision makers.
On the pull side, Wayne and I suggested that HR and other organizational leaders consider the necessary conditions for HR metrics and analytics information to get through to the pivotal audience of decision makers and influencers, who must:
- receive the analytics at the right time and in the right context
- attend to the analytics and believe that the analytics have value and that they are capable of using them
- believe the analytics results are credible and likely to represent their “real world”
- perceive that the impact of the analytics will be large and compelling enough to justify their time and attention
- understand that the analytics have specific implications for improving their own decisions and actions
Achieving improvement on these five push factors requires that HR leaders help decision makers understand the difference between analytics that are focused on compliance versus HR departmental efficiency, versus HR services, versus the impact of people on the business, versus the quality of non-HR leaders’ decisions and behaviors. Each of these has very different implications for the analytics users. Yet most HR systems, scorecards, and reports fail to make these distinctions, leaving users to navigate an often confusing and strange metrics landscape. Achieving better “push” means that HR leaders and their constituents must pay greater attention to the way users interpret the information they receive. For example, reporting comparative employee retention and engagement levels across business units will naturally draw attention to those units where retention or engagement is lowest, middle, and highest (often depicted as red-yellow-green), and a decision to emphasize improving the “red” units. However, turnover and engagement do not affect all units the same way, and it may be that the most impactful decision would be to make a green unit “even greener.” Yet we know very little about whether users fail to act on HR analytics because they don’t believe the results, because they don’t see the implications as important, because they don’t know how to act on the results, or some combination of all three. There is virtually no research on these questions, and very few organizations actually conduct the sort of user “focus groups” needed to answer these questions.
A good case in point is whether HR systems actually educate business leaders about the quality of their human capital decisions. We asked this question in the Lawler-Boudreau survey and consistently found that HR leaders rate this outcome of their HR and analytics systems lowest (about 2.5 on a 5-point scale). Yet higher ratings on this item are consistently associated with a stronger HR role in strategy, greater HR functional effectiveness, and higher organizational performance. Educating leaders about the quality of their human capital decisions emerges as one of the most potent improvement opportunities in every survey we have conducted over the past 10 years.
To put HR data, measures, and analytics to work more effectively requires a more “user-focused” perspective. HR needs to pay more attention to the product features that successfully push the analytics messages forward and to the pull factors that cause pivotal users to demand, understand, and use those analytics. Just as virtually every website, application, and online product is constantly tweaked in response to data about user attention and actions, HR metrics and analytics should be improved by applying analytics tools to the user experience itself. Otherwise, all the HR data in the world won’t help you attract and retain the right talent to move your business forward.
This blog first appeared on Harvard Business Review on 06/16/2017.
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