Data mining and the use of analytics are typically applied to companies in an effort to improve operations to better the customer experience. However, what about using analytics to better the experience of employees to improve business functions and bottom line results?
Forrester Research in 2016 conducted a study on this growing trend of people analytics, which involves compiling and analyzing data to optimize talent management in the workplace. The study was titled, “Use HR People Analytics to Drive Business Results.” It dissected how company Human Resource departments should seek out analytics to determine the best team roster for optimum performance within a company.
Claire Schooley, the report’s lead author and Principal Analyst at Forrester Research, was quoted in HR Technologist, saying: “HR should be much more integrated with the business and understand what the business wants. HR needs to ask, ‘What is the business looking for? What kinds of people and what kinds of skills do we need to drive value to the business?’ Data should be used to drive the best value to the business.”
It sounds a lot like a sports team, and, scientifically speaking, it is. Just like various sports base their teams around high performing players fitting certain positions, so can most companies. For HR departments there are various data metrics that can be used to better understand their employees and what the difference is between high achievers and underachievers.
Here are four analytical process that can be helpful:
1. Descriptive Analytics reviews past data to summarize what happened. For instance, are you having trouble retaining employees? Descriptive analytics can look at how long employees at different levels stay at your company to see if there are any patterns.
2. Diagnostic Analytics looks at data to answer why something happened. This is taking data and doing a deeper dive to understand why something happened. Perhaps you have found that there is a certain period of time when rising star middle managers leave your company. Diagnostic analytics looks beyond the who, what, when and where to identify why these managers left.
3. Predictive Analytics uses data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. Using our example, perhaps you find that rising star middle managers are leaving right after annual reviews were changed a few years ago. Is there something about that process that triggers managers to leave?
4. Prescriptive Analytics uses data and analytics to improve decisions and actions. It analyzes options to determine likely outcomes and suggests the best course of action to improve an outcome. In this case, the HR team identifies what changes in the review process may have triggered rising stars to both leave or stay with the company, and makes decisions that will improve the outcome of keeping more rising stars at the company.
While these analytics options vary in complexity, they allow for important information to be drawn about employees. When you know your staff, you are able to understand their wants and likelihood that they will stay or leave. This insight can promote a harmonious work environment and prevent unnecessary turnover. It also provides more insights into the types of people you may want to recruit for your team.
So how can you get started? Identify members of your HR team that could play a role in analyzing your HR data to determine important trends with employees and recruits. Consider bringing on new talent to your HR team to focus on analytics. Seek help from appropriate departments in your organization to see how they might help you make better use of your HR data to identify important employment trends within your company and ways to improve outcomes for the company and its employees. Then identify what makes your workforce productive, satisfied and happy and that also leads to stellar company results to demonstrate to your leadership the important role HR can play in making for happier employees and an improved bottom line.