Compiling and reporting HR data have always vexed human capital professionals. Which data points they should look at, how often they should do it and how can they use it to improve people performance are questions every organization struggles with. This task is becoming increasingly challenging as more information, not less, is making people analytics exponentially complex and confusing.

Even so, HR has emerged as one of the most active functions when it comes to analytics, according to a survey published in Harvard Business Review. A majority of HR respondents (51%) said they could perform predictive or prescriptive analytics, compared with 37% of finance respondents. And 94% say they have accurate, real-time insight into employees’ career development goals currently.

These metrics might surprise a lot of business leaders who have long dismissed the notion that HR is a data-driven function. With the growth in HR systems and tools, however, making sense of converging data streams has become a priority for some companies. Still, many more are struggling to create a coherent data strategy for using relevant information in their talent decision-making process.  

"Some companies have access to valuable data and use it to create all kinds of dashboards. The HR business partners are expected to use these to tell stories, have meaningful interactions and ultimately create business value. However, this can be a struggle due to lack of training, confusion over which metrics affect business outcomes and indecision about how to resolve issues,” says Dirk Jonker, the CEO of Crunchr, an analytics cloud solution that supports workforce planning.

Pursuing meaningful insights from today’s HR systems typically requires significant resources. Fast Company reports that 69% of companies with more than 10,000 employees now have entire teams dedicated to HR analytics. People analytics is also the most in-demand skill that HR professionals desire, according to one training portal. And despite increased attention to HR data, according to Deloitte, only 8% of human capital leaders surveyed report they have usable data and just 9% believe they have a good understanding of which talent dimensions drive performance in their organizations.

So while companies are committing resources to unlocking the secret to talent success, the enormous investments in time and budget aren’t always paying off. What HR desperately needs resides in two areas: domain expertise – selecting the data points that help drive business performance – and tools to organize this information. The technology needs to not only collect past transactions but also leverages this information to predict future events – such as the likelihood a role will be filled within a specific region at a specified rate within a defined date range. Or it should reveal when key roles will turn over based on historic data as well as current workforce makeup.

why it’s important

Transforming HR from an art to a science has long been a key desire of talent leaders. That’s why data is becoming more important in the day-to-day operations of the function. Having critical insights into how talent is deployed, detailed information on vacancies, time to fill for key roles and other important metrics can help you better address immediate issues.

Additionally, you should have this data to support workforce planning. By guiding users to examine business drivers such as revenue forecasts, it can more accurately compile future workforce demand. As a result, business metrics are turned into workforce metrics.

Jonker points out that while many employers are building highly competent people analytics teams, they are hobbled by not having the right tool sets in place. Because large organizations often employ a disparate set of HR information systems and point-solution add-ons, they are challenged with extracting meaningful, predictive insights from so many sources. Often the platforms they operate are too static and incapable of drilling down to uncover important trends. So even when the analytics team has identified the right metrics to consider, the insights they uncover may be irrelevant or outdated. 

What an analytics platform should do is provide a simplified and visual view of key indicators of workforce performance, supporting tasks such as planning and acquisition. To do this, data needs to be properly mapped and organized within the technology so the insights are accurate, timely and relevant. Furthermore, it should be easy to use to facilitate adoption across the enterprise. When technology requires extensive training and has a poor user experience, the barriers to adoption become much higher.

In the future, Jonker says technology will evolve to the point where users won’t need to have contact with data. Rather, systems will recognize critical trends and alert users to issues occurring in their workforce and take corrective actions. 

“Analytics is disappearing as it exists today. We use analytics in ways we don’t even realize,” he said. “Do a google search and if you make a typo, google will correct it. Crunchr in the near future will nudge hiring manager, HR leaders, employees and procurement to drive them to hot zones.”

Leveraging analytics to improve employer brand 

A global telecom giant with 15,000 employees struggled to attract qualified candidates for critical technical positions. Constrained by budget cuts, the company could not increase compensation offers and instead looked for a cost-effective way to increase the strength of its employer brand to compete with other companies in the market.

The chief HR office turned to Crunchr’s Preference solution to solicit employees’ input about their jobs. Examining 16 different drivers including “hard” value propositions such as salary/bonus and “soft” ones such as workplace environment and challenging work, the company sought to segment and create different talent groups among respondents. The effort was able to achieve a participation rate of 85% of the workforce.

The tool helped the HR team to identify “challenging work,” “competitive salary” and “career opportunities” as the three most appealing aspects to workers. Data also revealed that there were three distinct subcultures in the workforce, which led the company to stop attracting candidates with a one-size-fits-all employee value proposition.

Crunchr Preference helped the company to:

  • create an improved employee value proposition, resulting in lower turnover and a more engaged workforce
  • achieve a higher job offer acceptance rate among technical talent
  • increase employee engagement through the survey process