Today’s HR leaders are relying on analytics to drive forward HR strategy—but there are some hurdles to cross along the way.
By Marta Chmielowicz
Human resources has always been a people-focused function—but now it is a numbers-based profession as well. In fact, LinkedIn’s Global Recruiting Trends report indicates that 64 percent of today’s organizations use data at least occasionally and 79 percent are somewhat likely to take advantage of it in the next two years.
The concept of leveraging analytics to drive more informed decisions isn’t new, but HR leaders are now faced with an unprecedented volume of data and the technology to analyze it quickly and efficiently. Rather than allowing data to sit underutilized in Excel sheets, organizations are increasingly relying on analytics to better understand the business impact of employee initiatives, improve people-related decision-making, measure employee productivity, and more. And data’s functionality goes further than that—sophisticated companies are even using it to power intelligent technologies like artificial intelligence.
According to LinkedIn’s report, the most common uses of data in HR are twofold: to understand a problem or to execute a growth strategy. Top uses in the realm of talent acquisition include:
- Increasing retention (56 percent)
- Evaluating the skills gap (50 percent)
- Building better offer letters (50 percent)
- Understanding candidate needs (46 percent)
- Workforce planning (41 percent)
- Predicting candidate success (39 percent)
- Assessing talent supply and demand (38 percent)
- Comparing talent metrics to competitors (31 percent)
- Forecasting hiring demands (29 percent)
For example, HR professionals looking to understand the cause of high turnover can begin by consulting compensation history, promotion history, employee satisfaction surveys, and performance reviews to get to the root of the issue and then comparing those metrics with those of key competitors. Triangulating these metrics across the employee lifecycle can help organizations predict when employees will leave and better understand how to make them happy during their tenure with the organization. All of this can have a huge impact on a company’s ability to execute on its strategy and meet business goals—and that’s what makes data so valuable.
However, quality issues can get in the way of a positive outcome. LinkedIn’s research reports that the greatest barriers to using data are:
- poor data quality (42 percent);
- difficulty finding the right metrics (20 percent);
- high expenses (18 percent);
- inability to use data effectively (14 percent); and
- other (6 percent).
So while people analytics have huge potential to transform HR from an operational to a strategic function, leaders are facing some fundamental hurdles when attempting to gather and use it effectively.
The Society of HR Management (SHRM) recommends two best practices to overcome these hurdles:
1. Monitor the quality of the data. Analytics are only helpful if the metrics they draw from are high quality, quick, and accessible. HR leaders should make sure that their systems collect data using the “CARE” framework:
- Consistent—The data must be measured steadily over time.
- Accurate—Information should be recorded precisely.
- Reliable—The metrics must dependably assess the correct behavior.
- Efficient—The cost of collecting the data must be minimal.
2. Look at data holistically. While metrics reveal important facts about employee performance, they should not be looked at in a vacuum. Instead, HR leaders should look at the data holistically, taking into consideration the ways that different types of metrics illustrate various angles of the underlying issue HR is trying to solve. Pure HR measurements do not provide much value to an organization—they need to be evaluated as a whole in order to tell the real story.
To do this effectively, HR leaders need to:
- Understand the business strategy.
- Identify the critical human capital problems that are holding the organizations back.
- Tie the business strategy to the HR strategy in order to demonstrate ROI.
- Determine the measurements that will help address the issues at hand.
- Work across the organization to develop and gather meaningful data.
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