Behind the Buzzwords: What Good People Analytics Looks Like
I was interviewed by friends at leapsome and here is the gist of it:
Why does People Analytics matter to you?
Better Decision Making Is Possible
People Analytics is simply about understanding the people at your company with the help of data, and using that information to benefit both employees and the company. It involves looking at the information you already have and using it to help you make good decisions. The information can be simple yet insightful, like the average characteristics of the employees you hire, or the length of time that people stay with your company before they leave. You can learn from that information, identify patterns and make better decisions. One example is understanding the difference between people who stay at your company, and people who leave. You do not need to have massive amounts of information points for this, or be a large company. Instead, it is OK to have just a few data points, spread out over time. You can then look at this information over time, and learn from it in order to make better decisions.
Practically speaking, what would you recommend for companies to get started with People Analytics?
Match the Right People, System, Question, and Users, and Give Context
First, find people who can analyze data. Internal company employees are often too busy to do this for HR. Therefore, you can hire or train this talent within your own HR team. Build a team with a mix of people, including people who can work with data, statistics, and software. If you do not have this in your budget, you can also contract an outside data analysis service.
Secondly, it is critical to have good tools for storing and displaying data. Spreadsheets are not enough. Use easy, open HR tools. Make sure the tools store data securely. Make sure it also has its own data visualization, or works with outside visual tools (for example, Tableau). Ensure you have processes to consistently update your tools, and your accompanying, company-facing visual dashboard.
Next, as I mentioned before, you need to have a good question. Then, work with your users through interviews, user tests, and iterations to answer that question in a useful way. Lastly, don’t just report back the statistics you find, but benchmark your results against a meaningful backdrop. A finding like “60% of our job-leavers are women” needs the context of industry averages, information on how the rate has changed over time at the company, and the gender makeup of the company’s workforce.
Collectively, the right people, system, question, and user involvement, and benchmarks will come together to create an effective approach to People Analytics.
