Managers make decisions that involve people every day—who to hire, how to train, and how to lead, just to name a few. Often managers rely on intuition and prior experience to make these decisions, but such approaches are not always reliable and can lead to poor decisions. For many decisions there is a better way—relying not on gut reactions but on data that can indicate what works and what doesn’t. That’s where using people analytics comes in.
Using People Analytics
People Analytics (or HR Analytics) are quantitative tools that can be used to help organizations make decisions involving people that are based on data. Such tools include artificial intelligence, machine learning, and statistics. The key is to collect data that are relevant to specific decisions. This can involve continuous monitoring of key indicators or conducting targeted studies that inform actions. Some examples include:
- Using pre-employment assessments to determine who has the knowledge and skill needed for a particular job.
- Pilot testing the effectiveness of a training course on sales volume prior to full company roll out.
- Determining the impact of a new policy on employee engagement.
- Seeing how employee experiences are linked to turnover
People Analytics versus Big Data
Often people equate people analytics with big data, but they are not the same thing. Big Data has to do with problems that involve tremendous amounts of data—so much that they cannot reside on a single computer. It is distinguished by the three Vs:
- Variability: Different forms of information, such as numbers, text and video.
- Velocity: Data are continuously being generated, and quickly.
- Volume: There is a tremendous amount of data—enough to fill many of the largest computer hard drives available.
Some people analytics problems might involve Big Data. When a large retailer collects all customer contacts—text and voice—for millions of interactions each day, they are using Big Data, and those data can be used in human analytics applications. They can be tied to customer experiences (by linking to customer satisfaction data) and sales. But other people analytics problems involve small amounts of data.
People Analytics Is a Mindset
Using people analytics does not come naturally to all managers. Many are comfortable relying on their own experience and know-how and might have a difficult time trusting data to help make decisions. Others might be uncomfortable because analytics uses complex programming and statistical methods that are unfamiliar. It takes a new mindset that accepts the idea that human judgment is flawed and that there are tools that can help avoid those flaws. The tools provide information to help the manager make decisions. Like any tool, the manager needs skill in knowing how to use it—when what it is saying makes sense and when it does not. At the end of the day, when decisions need to be made, the manager should ask what evidence there is that a particular course of action will have the desired effect. People analytics can provide that evidence.
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