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People Analytics

Global Leadership Forecast 2018

People Analytics:

Reversal of Fortunes

Evan Sinar

Pressure on the HR function to adopt and excel in people analytics—defined as “the systematic identification and qualification of the people drivers of business outcomes, with the purpose of making better decisions”*—has been intense for several years. Analytics are viewed as a prime route to greater strategic influence, and the thirst for deeper analytics know-how has spawned a five-fold spike in interest over the past five years. Hundreds of articles have been written in HR publications, dozens of conferences dedicated, and entire academic programs in HR analytics offered at universities such as Cornell, Texas A&M, and New York University. With this steady drumbeat of exhortation and energy, surely HR has grown its proficiency in analytics methods since Global Leadership Forecast 2014|2015, right? This current Forecast took a detailed view of analytics progress and practices across more than 2,500 global organizations to ask: What’s the status? What’s emerging? and What works to drive HR—and the enterprise—forward?

People Analytics

A Stunning Regression

Instead of an expected increase in analytics effectiveness, we found that success rates declined over the past three years for every type of analytics we compared (shown in the figure above). Though companies attempted analytics more often than in 2014, these efforts were more likely to fail than to succeed.

These struggles were common for more traditional types of analytics (internal benchmarking and gathering efficiency, reactions, or results metrics). On average, only 21 percent of companies were succeeding in these. The picture is even more bleak for emerging forms of analytics, such as creating growth-centric leadership planning models, using “what if” scenarios to forecast future talent needs, and data visualization and storytelling to compel the audience. On average, only 18 percent of organizations succeed here.

To put these declines in context, it’s important to gauge whether this is a true backward step in effectiveness or a higher standard for success. Our data shows that it’s likely a case of the analytics bar rising faster than HR can leap over it. Despite low success rates, fully 70 percent of HR professionals reported an increase in their analytics skills and data-driven decision making. Yet, this clearly isn’t enough to keep pace.

Highest-Impact Analytics Practices

Though success rates are similar across analytics types, distinctions between them become clearer when linking to leadership and business outcomes. By doing so, we can show which forms of effective analytics outperform the others. The graphic at left shows the link between various types of analytics and two critical outcomes: bench strength (the ability to rapidly fill critical roles with qualified internal candidates) and a financial composite of revenue growth, operating margin, EBITDA, and return on equity.

Every type of analytics showed a notable link with one or both outcomes, led by benchmarking internally, creating leadership-planning models, and using data visualization/storytelling techniques. On average, companies excelling in these analytics were 6.5 times more likely to have high bench strength and 3.1 times more likely to outperform peers financially.

It’s Not Just About the Numbers

It’s easy to make the mistake that analytics are just about the numbers. But analytics also drive and exemplify objectivity and fairness. Companies strong in people analytics have more gender diversity in leadership (especially high-potential pools), a stronger culture of promotion from within, and higher leader success rates.

The bottom line: More analytical practices are more diverse practices, and they reinforce strong “grow your own” cultures. When analytics are in place, so are objective decision-making tools, systematic processes, and a bias-free, up-to-date status of talent.

Analytics as Accelerator

Analytics accelerate HR professionals deploying them: Those succeeding with advanced analytics are 6.3 times more likely to have new advancement opportunities and are 3.6 times more likely to have a stronger reputation with senior business leaders.

And analytics accelerate talent practices, making good ones even better. Companies that create leadership planning models, gather results metrics, and are adept at data visualization and storytelling see a higher return on their talent investments than those that neglect or struggle with these practices.

* van den Heuvel, S., & Bondarouk, T. (2017), The Rise (and Fall?) of HR Analytics: A Study Into the Future Application, Value, Structure, and System Support, Journal of Organizational Effectiveness, 4, 157–178.

Where to Start
  • Confirm business questions. Analytics that fail to start with business (not just HR) questions will be challenging to execute and, often, wholly unpersuasive to stakeholders.
  • Get your data house in order. Spend time planning your data foundation to avoid misinformed or impossible analytics.
  • Align effort toward highest-impact methods. Internal benchmarking, leadership planning, and visualization/storytelling had the strongest outcome links in our research.
  • Staff for data savvy. Analytics is a team sport; develop and hire not just for abilities in data science, but also business acumen, visualization, and ethics.
  • Adopt proven methods such as key driver analysis, text analytics, survival analysis, decision trees, and scenario planning.
How to Excel + Differentiate
  • Build skills in visualization and storytelling. These are powerful ways to communicate data; such messages are more memorable and persuasive than numbers or text.
  • Recognize analytics as change management. Numbers mean nothing if they don’t drive action; pair analytics with interactive data views to build engagement about a practical path forward.
  • Map talent scenarios to business strategies. Sophisticated, high-impact workforce planning matches talent needs to growth projections, ensuring that talent is where it’s needed and when.
  • Build internal and external networks. Seek partnerships with data-minded individuals across functions, local interest groups, and internal analytics centers of excellence.
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