A 5-Step Process to Get More Out of Your Organization’s Data
- by 7wData
“My best employees are leaving,” Daniel told me, “and I can’t seem to figure out why.”
Daniel (not his real name) was a VP human resource manager at a Fortune 500 company. I asked him whether he had collected any data that could provide him with insights into systematic patterns. “I made sure we get exit interviews done with every single employee who is leaving us,” he replied. “I even personally conducted some myself! But no consistent pattern is emerging. I’m not sure how I can prevent my best employees from leaving us in the future.”
Here is the problem with exit interviews: People aren’t honest about the reasons why they quit. And even if they were, such post hoc rationalizations rarely reflect the true reasons employees quit.
Daniel’s conundrum is one many HR managers encounter in their organizations. Why are the best employees leaving the organization? Why are some employees more productive than others? How can employees become more creative? Often, the information that can help answer these questions already exists within the company, hiding in plain sight.
Although companies collect a great deal of data about their employees, most of them don’t do a great job of leveraging it for insights to answer these questions. If companies could improve their data practices at five important stages, they could become much more effective at solving some of the most pressing problems they face.
Step 1: Improve data quality.
After listening to Daniel’s problems, I asked him what kind of data his company collects. A lot, it turns out. His department sends out a survey for all employees to fill out every six months. Managers conduct annual performance reviews that they log in a centralized system. The HR department keeps track of every promotion, while the operations department monitors which employees leave the organization.
However, when I asked to take a closer look at how Daniel’s department was collecting data, I was aghast. The survey did not collect data in a reliable, validated way. The performance reviews weren’t structured, and only 55% of managers filled them out. And the promotion and turnover data didn’t include dates.
Before you can use your data to get answers, you have to improve the quality of the data you collect. Design a more rigorous survey with better measures. Create a performance review system that makes it easier for managers to log their reviews. Think through what kind of data will be useful to collect, and then collect it — systematically. Have regular conversations with people from throughout the company to identify what questions are pressing and what kind of data you may need to answer those questions.
Step 2: Link different data.
To answer a question like “Why are my employees are leaving?” you need to compare employees who’ve stayed with employees who’ve moved on. (Which is another reason exit interviews often don’t work — you’re only getting half the story.)
To do this, you need to link the data from different sources throughout your organization. In Daniel’s case, the data was championed by different departments. Performance reviews and employee surveys were managed by the HR team, whereas data on turnover was held by the operations team. Neither team realized which data the other team held, so they needed to change their processes to ensure they could connect the employees who’d left with their survey responses and performance reviews.
Find out what kind of data is being collected in the organization. Design processes that make it easier to connect the dots between individuals to get as many data points on each employee as possible.
Step 3: Analyze your data.
Simply put, data analysis requires data processing abilities. For example, in some cases your performance outcomes might be at the group level: the success of a team project, or a successful outcome for a client team.
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