For years, many companies have relied largely on intuition and 6 important HR metrics when it comes to people analytics. But in the face of rapidly-evolving ideas, methods, and tools in this space, many people have been challenged to consider their trust in data and the science that it is—ideally—based on. Even when these new data-gathering technologies have been relentlessly tested and their insights supported, HR data can still be met with some degree of skepticism.
These challenges in trust are becoming increasingly familiar to analysts and researchers who are looking to leverage data in more rigorous and scalable ways. This skepticism isn’t new—nor is it unique to people analytics. Past research has found that people lose trust in data and algorithms far more quickly than they lose trust in humans when both make the same error. Instead, humans tend to rely largely on their own intuition to maintain a sense of “control” over the outcome. However, the pace of growth in data and analytics could make it difficult for those who don’t get on board now to catch up later.
With an increase in data and analytics capability, it is more essential than ever before for HR professionals to begin making the case for data-driven HR. In the face of skepticism about data, the first major step to building a successful people analytics function is ensuring that your stakeholders are prepared to trust your data and methods. Here are three rules to help you gain and maintain that trust:
1. Maintain Great Data Hygiene
No HR data set is perfect. Whether you studied HR/analytics in school or fell into the field, we all start out a bit naive about the cleanliness of our HR data sets. Some of these challenges are the result of system limitations; some are the result of human error. Often, data is messy simply because the organization was not leveraging its data before and never had a true need for superb data quality. Whatever the reason, a lot can be done to improve the quality of your HR data.
First, take a look back and audit the cleanliness of your HR data. Are all the dates reasonable (e.g., no one born in 1899)? Are there any mistakes in the spelling of your departments, termination reasons, or other group names? As for your numeric data points, are the numbers all possible?
It’s not uncommon to find many inconsistencies in our data on first review. Be sure to clean up this data and establish standards for every field. Perhaps you require that all employees be assigned to a subdepartment (whereas it wasn’t required before), or you only capitalize the first letter in your job competency names. Establishing these seemingly small standards can actually work wonders for keeping your data clean and consistent.
Then train those who enter the data into your HR system about your standards. If you enter the data yourself, learn to be extra vigilant about following your own process. When training others, help them understand why the little details are so important. For example, maybe if they accidentally include a space after someone’s first name (“Eric ”), it becomes harder to link that HR record to other data sources where the employee’s name doesn’t have the extra space.
Better data hygiene will help you feel more confident, and you’ll be able to explain to stakeholders the rigor with which you and others maintain it. If you aren’t prioritizing data cleanliness, how can you expect others to trust the work that comes out of it?
Before you dive into an analysis, acknowledge your own expectations about what you might see in the results. Then, think about what other stakeholders and employees may expect to see in the same results—what are their beliefs about your workplace?
Recognizing these expectations upfront will provide clarity on how the analysis supports them and where it disproves them. Draw on your real-life knowledge of your organization to identify which findings might be of particular interest, and drill down into the data to understand those findings at a greater depth. Some results may have an associated shock factor, which will naturally require greater supporting detail to garner trust from others.
Perhaps one team had an exceptionally high Q1 turnover rate. But that means little without context. How much of the turnover was voluntary or involuntary? How small is the team? This team’s stakeholder will likely dig for greater detail, and it would be helpful to anticipate the follow-up needs. If presenting this finding to a broader audience, you’ll want to provide a clear context, so there is no misunderstanding or objection to the number.
By anticipating potential questions and concerns before analyzing your data set, you’ll be better prepared to tackle them with confidence—in turn inspiring the confidence of others.
3. Know When to Act—and When Not To
When stepping into the world of People Analytics, we often feel a bias toward acting on our findings. After all, if not to act, then why are we measuring anything at all? Ironically, just as important as the actions we take with data are the actions we don’t take.
Take that earlier example of high turnover. If the turnover was part of a planned workforce change, it may not reflect a true turnover problem. If we drive to action on something that doesn’t warrant it, we’re likely to waste money, time, and other resources building a solution for a problem that may not be real. So action itself can jeopardize the credibility of data efforts if it 1) tries to solve a non-existent problem or 2) fails to solve the root cause of a real problem. In either case, your effort won’t result in direct benefit to the organization, calling into question the value of the effort. These risks all point to the importance of preemptively understanding the organization and the true problems of its stakeholders.
As you improve at differentiating actionable from non-actionable findings, you’ll be demonstrating that you can leverage your organization’s data thoughtfully and efficiently. By driving impact on meaningful problems, you can start to earn greater trust from other stakeholders who recognize that their own challenges can also be addressed with data.
People analytics and HR metrics are taking on increasingly meaningful roles in the workplace. That’s where you come in: it’s up to HR professionals to ease their organizations into the experience of data-based decision making. Your analytics don’t need to be advanced or complex to result in meaningful change. Even simple reporting and analysis require that others trust the data you’re working with. Just remember our three rules: keep data clean, anticipate objections, and know when to act.
Together, these suggestions will set your data and analytics work up for success!