People Analytics: How Managers And Organizations Can Leverage Their Employee Data
Today we’ll be exploring Data and People Analytics, topics you may have heard of before but may not know exactly what they are or how they can benefit your organization. Even if you’re not already a data scientist, we’ll introduce you to these skills and show how you can start leveraging the data that your organization and employees are already generating
What Is Data Analytics?
In a nutshell, data analytics is the process of examining a raw data set and drawing conclusions from it. Hopefully, these conclusions improve organizational processes in some way.
Every organization generates all kinds of data about performance, sales, compliance and more. This data is being generated, regardless of whether it is being captured purposefully, by every action, process, and result that occurs within an organization. Leaders would benefit from capturing it and making use of it, instead of ignoring it. Then they could make data driven decisions.
Why Analytics Matters: Going Beyond Your Gut
Some managers may feel like they gain all the information that they need to make decisions through observing and talking with employees. And of course, talking to employees is valuable, so don’t ever stop doing that! However, analytics helps validate assumptions before a decision is made. The analytical tools are available so that people do not have to just go off their gut feeling anymore.
“How Good is Your Gut: In a room with 23 people in it, what is the chance that at least two people have the same birthday?”
There is an example from probability theory that shows how humans are intrinsically ineffective at assessing probability and thus assessing certain risks. This example is called the birthday problem or the birthday paradox, which asks how many people would need to be in a room to create a 50% probability that at least any two people in that room share the same birthday. Most people tend to guess a high number. However, probability theory and empirical studies have demonstrated that there is a greater than 50% chance at only 23 people. Surprising, right? This is just one example that shows how people can vastly misjudge a problem based only on their gut feeling.
“Every organization generates all kinds of data about performance, sales, compliance and more. This data is being generated, regardless of whether it is being captured purposefully…”
People analytics is simply data analytics applied to all the data describing and generated by organizational employees and processes. One small example of this would include employee development. A manager or leader might want to know, “How many of our employees are following a career path in our organization? What are the results of those who follow a career path?” So, people analytics would look at the career path data set to identify how the employees are progressing throughout their careers, their skills, and certifications, when they get promoted and to which position.
Another example of applied people analytics is employee retention. For instance, IBM has a retention program to track talent. With this system, they are able to accurately assess the flight risk of senior employees, to determine if they are leaving or staying with the firm. They also track what types of incentives were used to enable that process.
Cisco Systems, Inc has also taken interest in their talent acquisition and management strategy, hosting a Global HR Breakathon in 2016 to bring together ideas that can help redefine the employee experience using software and data solutions.
These are not rare interests. According to Brian Kropp of Gartner, “Almost every Fortune 100 company has a head of talent analytics and a team of data scientists in human resources.”
Analytics Is Not Just For Large Organizations Or Scientists!
Most people say that learning about analytics from scratch takes too much time. Yet the busier you are, the more you need these analytics to help you make complex decisions or even accurately assess a situation.
Unfortunately, active data analytics is not an out-of-the-box replicable process. It really depends on your own tailored questions based on the problem you are trying to solve. In addition, there needs to be a tight relationship between who is performing the analytics and the person guiding the process to get from point A to point B, which is the actionable conclusions.
To start with the basic skills and begin thinking about your own analytics process, Coursera is excellent starting point. Once you decide to take the plunge, here are three tips to help you.
Three Tips For Starting Out With Data Analytics
Tip number one is to define your questions that may solve the problem. What are you curious about? In which areas do you want additional insight? Use those questions to inform yourself about where to look or what other data you may need to capture. Then you can begin to determine what analytic methods would best answer those questions.
The second tip is to understand what kind of data you already have. You may already have data; it’s just not organized or aggregated yet. Are you currently capturing the data that your organization is naturally generating? Or are you letting data pass by because you do not have the processes in place to actually capture it? Without the knowledge and expertise to implement this first step around your business practices, all other thinking about data analytics is moot.
Finally, keep things simple because oftentimes your questions take you in new directions. Just try to take a look into any initial idea or hunch and see where that leads you.
Deepen And Share Your Insights With Data Visualization
Data visualization is a key component of the analytics process to help assess your data after its been captured; first you can use visualization and summarization tools to look at the data in various ways to help determine trends or other interesting features. If your results surprise you, then they may be able to hint at where to look further. And if not, then you may be able to save time by eliminating parts of your data and simplifying your model.
Throughout the analytic process, it’s essential to have your data and conclusions tell a story, often to non-data people. There are many visual tools to show the results in a way that helps managers make better informed decisions. For example, we recently used this color-coded bubble chart (Figure A, below) to clearly show management that certain data among their business units had distinct groupings and were starting points to address (data labels have been modified to protect confidentiality). To learn more about our process, you can read the business case study here.
Figure A: Visual representation of distinct groupings found in the data from two business unit dimensions.
Remember: Data Analytics Is An Ongoing Process
Data analytics is an ongoing process. It’s not one-time analysis saying, ‘Oh, there is the data that I have. What can I do with it?’ It is about creating a learning process within your own organization. A best practice is to use your tools to constantly assess and generate conclusions from your data on an ongoing basis. This means those key questions at the beginning a data analytics project may change over time. So, flexibility is the key!
How deeply do you rely on use of data analytics in making decisions? Does it overcomplicate the things? Is it complex and time consuming? Let’s share experiences! Leave a comment below, send us an email, or find us on Twitter.
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