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How HR Can Optimize People Analytics

How HR Can Optimize People Analytics

Story Highlights

  • HR should be a more intentional translator of workplace data
  • Focus on performance-predicting metrics over general performance reviews
  • Data analytics need specific, defined outcomes

It's the age of data -- and data analytics is revolutionizing HR.

Accenture calculated that there is $3.1 trillion in U.S. revenue opportunity for large publicly listed companies from new sources of digitally available workplace data.

But is HR prepared to optimize this opportunity?

HR has long been seen as the custodian of "hard" data, such as cost of employment, cost of turnover, absenteeism, labor costs and the like. All of this information is critical, but it's a lagging indicator of performance and productivity. By the time the numbers are in, it's too late to change the strategy.

HR can be -- and should be -- a more intentional translator of leading-indicator workforce data. To do that, CHROs must drive core people analytics harder, particularly concerning strategic performance and talent management. For strategic HR organizations, the days of data maintenance are over.

CHROs must drive core people analytics harder, particularly concerning strategic performance and talent management.

Fully Leverage Leading Indicators of Performance

Strategic analysis requires leading indicators and the ability to curate, synthesize and analyze that data. HR also requires the authority to enact real organizational changes as a result of their performance analysis. But to do any of that, HR needs very specific data.

For instance, according to Gallup research, only 29% of employees strongly agree that their performance reviews are fair and 26% strongly agree that they are accurate. Fewer yet say they're managed in a way that motivates them to do outstanding work. Those granular details add up to organization-level performance and growth issues.

HR should know the percentages of each of those metrics in their organizations. Those data explain factors -- such as employee engagement, talent performance, turnover drivers, etc. -- that predict performance, helping leaders understand what can be changed while there is still a chance.

But there are a lot of ways HR can help leaders to truly leverage the power of predictive analytics and accelerate quality decision-making. The key, however, is to determine the fewest workforce and performance metrics that provide maximum explanatory power over critical outcomes. In our experience, the following steps are essential:

  1. Audit and organize existing data from multiple sources and years into a single database (workforce, operational and business data).

  2. Leverage advanced analytics to determine what metrics are the most reliable, indicative and predictive of critical business outcomes (i.e., turnover, productivity, sales, profitability) and which have the highest data quality.

  3. Use the pared list of workforce metrics to monitor and forecast business performance, inform changes to strategy, and prioritize interventions and change initiatives. Focus these on answering fundamental questions that will help the business drive value. For instance: How effectively can we predict the quality of talent intake for a specific role based on applicant data? What factors increase the likelihood that top talent will stay with the company and continue to perform?

Leaders value such strategic analysis, as it helps them make the right decisions. Still, HR needs to get better at using such analytics to tell a story about what drives long-term value for the company (aligned with their strategic purpose) -- rather than narrow, short-term workforce enhancement initiatives based on descriptive analysis alone.

Disrupt Talent Analytics

Consider, for example, talent management discussions. In our experience, the talent review is one area that has seen regular, consistent abuse. Most companies have long relied on the nine-box model that classifies talent into categories such as top talent, consistent superstars or solid performers, and underperformers.

Nothing wrong with that. But the quality and objectivity of the data are concerning.

Traditionally, a "high-potential" employee is rated as such to reflect a set of competencies. The employee's manager designates that label, but managers' evaluations are frequently riddled with bias. The whole evaluation process takes a few months to complete. After that is an endless wait for executive input and, finally, the creation of the individual development plan. In the meantime, employees have moved on; the developmental input is late or not relevant anymore.

The long, cumbersome traditional talent review process needs to be disrupted. This starts with the assessment and analysis of more objective indicators of potential. But once an objective review has been completed, HR can move faster to translate the assessment insights into real development plans -- especially to help managers of top talent use those insights in their coaching conversations with every employee on a very regular basis. As it is, only 23% of employees strongly agree that their manager provides meaningful feedback -- and making them wait months for biased assessments is a questionable means of improving performance.

The long, cumbersome traditional talent review process needs to be disrupted. This starts with the assessment and analysis of more objective indicators of potential.

Google -- a company that bases all of its decisions on hard analytics -- offers a great example of using data better. Early on, Google's people analytics team came up with an algorithm to refine critical promotion decisions for software engineers.

The algorithm was used to make an impressive 90% of all promotion decisions. But the engineers wanted greater transparency, and an algorithm wasn't the answer. So, Google discontinued the program. The company knew that people should make people decisions, and analytics merely exists to arm decision-makers with the most reliable insights. In essence, having the right data versus having enough data was a critical point to keep in mind.

Connect Data Analytics to Long-Term Goals

HR's innovative use of predictive data analytics should have clearly defined outcomes, as all projects should. But to be maximally useful, those deliverables need to be tied to specific customer, operational and business outcomes -- as well as to outcomes at the organizational level such as time to market, shorter cycle times, rapid product innovation or accelerated quality improvement.

And to be genuinely agile, HR must go beyond outcomes to measures of continuous quality improvement on leading indicators, such as customer and employee engagement metrics and talent and development impact. These are the factors that truly drive business performance.

Backed by objective data and supported by managers in real time, HR could do what all the analysis in the world can't: lead to predictable, measurable, successful outcomes.

To be blunt, HR has the potential to catalyze disruption into opportunity -- but it has to be able to translate people analytics into business decisions first.

Turn the data you have into a competitive advantage. Not sure how?


Vibhas Ratanjee is Senior Practice Expert, Organizational and Leadership Development, at Gallup.

Jennifer Robison contributed to this article.

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