Managing HR-related information is important to any organization’s success. But progress in HR analytics may be glacially slow. Consulting firms from the U.S. and Europe lament the slow progress. However a Harvard Business Review analytics study of 230 executives suggests a wonderful rate of anticipated progress: 15% said they will use “predictive analytics depending on HR data and knowledge business sources within or outside the corporation,” while 48% predicted they’d be doing regular so in 2 years. The reality seems less impressive, like a global IBM survey of more than 1,700 CEOs found that 71% identified human capital like a key source of competitive advantage, yet a global study by Tata Consultancy Services showed that only 5% of big-data investments were in recruiting.

Recently, my colleague Wayne Cascio and i also took up the issue of why Buy HR Management Books may be so slow despite many decades of research and practical tool building, an exponential rise in available HR data, and consistent evidence that improved HR and talent management contributes to stronger organizational performance. Our article from the Journal of Organizational Effectiveness: People and satisfaction 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 during the entire organization.

Around the “push” side, HR leaders are able to do a better job of presenting human capital metrics for the remaining organization while 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. As an example, beyond providing numbers that describe trends from the demographic makeup of an job, improved logic might describe how demographic diversity affects innovation, or it may depict the pipeline of talent movement to demonstrate what bottlenecks most affect career progress.
Analytics. Use appropriate techniques and tools to rework data into rigorous and relevant insights – statistical analysis, research design, etc. As an example, understanding whether employee engagement causes higher work performance requires analysis beyond correlations that show the association, to make sure that this is because not only that better performers be engaged.
Measures. Create accurate and verified numbers and indices calculated from data systems for everyone as input for the analytics, to avoid having “garbage in” compromise even with appropriate and sophisticated analysis.
Process. Utilize the right communication channels, timing, and techniques to motivate decision makers to do something on data insights. As an example, reports about employee engagement are often delivered as soon as the analysis is finished, nevertheless they be impactful if they’re delivered during business planning sessions of course, if they reveal the partnership between engagement and particular focus outcomes like innovation, cost, or speed.
Wayne and i also observed that HR’s attention typically may be dedicated to sophisticated analytics and creating more-accurate and finish measures. Perhaps the most sophisticated and accurate analysis must do not be lost from the shuffle since they can be embedded in may framework that’s understandable and tightly related to decision makers (like showing the analogy between employee engagement and customer engagement), or by communicating it in a manner that engages them through stories, analogies, and familiar examples. My colleague Ed Lawler and i also compared the outcome of surveys of more than 100 U.S. HR leaders in 2013 and 2016 determined that HR departments designed to use all of the LAMP elements play a stronger strategic role in their organizations. Balancing these four push factors produces a higher probability that HR’s analytic messaging will attain the right decision makers.

Around the pull side, Wayne and i also suggested that HR as well as other organizational leaders take into account the necessary conditions for HR metrics and analytics information to get to the pivotal audience of decision makers and influencers, who must:

have the analytics in the perfect time along with the right context
tackle the analytics and believe the analytics have value and they can handle utilizing them
believe the analytics answers are credible and certain to represent their “real world”
perceive how the impact with the analytics will be large and compelling enough to warrant their time and a focus
recognize that the analytics have specific implications for improving their very own decisions and actions
Achieving improvement on these five push factors mandates that HR leaders help decision makers understand the distinction between analytics which are dedicated to compliance versus HR departmental efficiency, versus HR services, compared to the impact of people about the business, compared to the quality of non-HR leaders’ decisions and behaviors. These has different implications to the analytics users. Yet most HR systems, scorecards, and reports don’t make these distinctions, leaving users to navigate a typically confusing and strange metrics landscape. Achieving better “push” signifies that HR leaders along with their constituents have to pay greater care about the way in which users interpret the knowledge they receive. As an example, reporting comparative employee retention and engagement levels across sections will first highlight those units where retention or engagement is lowest, middle, and highest (often depicted as red-yellow-green), and a decision to emphasize enhancing the “red” units. However, turnover and engagement tend not to affect all units exactly the same way, and it may be how the most impactful decision would be to come up with a green unit “even greener.” Yet we realize hardly any about whether users don’t act on HR analytics given that they don’t believe the outcome, given that they don’t begin to see the implications as vital, given that they don’t know how to act on the outcome, or some mix of the 3. There is hardly any research on these questions, and incredibly few organizations actually conduct the type of user “focus groups” necessary to answer these questions.

A fantastic great example is whether or not HR systems actually educate business leaders regarding the quality of these human capital decisions. We asked this question from the Lawler-Boudreau survey and consistently found that HR leaders rate this result of their HR and analytics systems lowest (around 2.5 over a 5-point scale). Yet higher ratings for this item are consistently of a stronger HR role in strategy, greater HR functional effectiveness, far better organizational performance. Educating leaders regarding the quality of these human capital decisions emerges as one of the most potent improvement opportunities in every single survey we now have conducted in the last Decade.

To set HR data, measures, and analytics to work more effectively takes a more “user-focused” perspective. HR must be more conscious of the merchandise features that successfully push the analytics messages forward also to the pull factors that can cause pivotal users to demand, understand, and use those analytics. Just as just about any website, application, and online strategy is constantly tweaked in response to data about user attention and actions, HR metrics and analytics ought to be improved by applying analytics tools for the buyer experience itself. Otherwise, all of the HR data on the planet won’t enable you to attract and retain the right talent to move your organization forward.
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