How Workforce Analytics Can Boost Your Career In HR: a Q&A Guide
Workforce Analytics is “the process of collecting, analyzing, interpreting, and reporting people-related data to improve decision-making, achieve strategic objectives, and sustain a competitive advantage” (Bauer, Erdogan, Caughlin & Truxillo, 2019). In the run-up to teaching a new short course on this relevant subject, Dr Sébastien Fernandez guides us through the meaning and impact of Workforce Analytics. Ultimately, People Analytics matters and is critical in supporting the bottom line of HR function.
What exactly is Workforce Analytics?
The most common definition of Workforce Analytics (also known as HR or People Analytics) is the use of data to make people-related decisions. The field of Workforce Analytics offers solutions to demonstrate the value of individuals to an organization. In brief, it provides essential insights when hiring and managing people, this is why it is so useful in the domain of HR.
When it comes to recruitment and management, many leaders tend to simply follow their intuition. But intuition can be misleading. It is intuition that makes interviewers ask inappropriate questions in a selection interview or promote the wrong person. The field of Workforce Analytics offers techniques to better evaluate different courses of action. It helps determine if there is a problem of diversity in the company, it can help refine the strategy of the company, decide which employee advantages should be kept, understand why employees are leaving the company, or which kind of employee is the most productive. I hasten to mention that data never offer a single course of action, they will never tell managers what to do, but they can offer very impactful suggestions.
How does Workforce Analytics impact the hiring process?
Let’s imagine the following situation: Interviewers use a structured interview to recruit managers in a company. The same 15 questions are asked to all the candidates during the interviews. This process is used over many years and more than 300 managers are hired. If I were running this company, I would wonder if the questions we asked were “good questions”. To verify this, I would get the performance of the 300 managers during the interview and I would put these data in relationship with performance data collected months or years later. I would check there is a positive correlation between the interview and future performance. If this is the case, this would inform me that I should continue using the interview.
I could go a step further and determine if performance on some interview questions have more value to determine future performance. I might discover that some questions are uncorrelated with the future job performance of these managers. I may then decide to remove these questions or decide to modify them. I might also run other analyses to discover that men perform better on some questions than women. I might then reflect on the reasons why and take the right course of action (e.g. removing these questions or not taking them into account for the hiring decisions).
As you can see, it is fascinating because the results obtained could and should have an impact on the business. But obviously, the data will tell you if the solutions that are currently established in a company are working or not, it can give insight about the reasons why a solution is working or not, but it does not tell you precisely what to do. You still have to decide what is the right course of action to take.
Can Workforce Analytics optimise the cost of hiring?
Yes, people analytics helps to optimize the cost of hiring in different ways. It can help the HR team spend less time on the recruitment and selection phase, and spend more time on more strategic aspects of the business. It can also help identify better profiles that will contribute more to the company. If you pay an employee 75 000 CHF per year, we can estimate that the value of his/her contribution is at least 75 000 CHF (otherwise, the company would lose money). It is probably even a conservative estimate and the value could be even more. If employees are not performing to expectations, we can clearly claim that the value given to the company is lower than 75 000 CHF. Then, with some arithmetics, we can make estimates about the cost of having a poorly performing employee. Finally, hiring could be optimized to lower staff turnover. It is very easy to estimate the economic value of turnover and to reduce costs by better hiring.
How does Workforce Analytics impact the management of a team & employees?
Workforce Analytics can impact employees and teams in so many different ways. Any Workforce Analytics initiative should come from the top of the company with a problem to solve. This problem might be about high turnover, about the difficulty to recruit certain profiles, or a problem of inefficiency. The questions to be answered and the problems to be solved can be endless. However, I would like to give some ideas and possibilities. We can imagine that Workforce Analytics might offer solutions about the best way for employees to manage their time, and when they should take their break, we might also imagine that Workforce Analytics might offer solutions on what is the right ratio of employees working from home or in the office, we might also imagine that employees are allocated in teams in which they might better work.
How to convince a non-data HR person that Workforce Analytics is the future?
It might be difficult and/or it could take time to do so. First, I would probably say that it is not the future - it is the present. Second, there is a lot of resistance towards numbers. Many managers think we cannot manage or make people decisions in this manner, and that doing so is a dehumanizing process. I think we are indeed not used to making people decisions in this manner. However, in my opinion, it can help set up fairer processes. I would argue that using Workforce Analytics capabilities is not dehumanizing as it helps in the setting of objectives and macro-decisions, and does not replace the operational work of meeting candidates, telling them if they have been accepted or not.
Third, I would say that running a business is always a kind of lottery. When you make intuitive decisions, you may have a 40% chance of success, but making data-driven decisions might increase this chance of success to 60 or 70%. Of course, it will never be 100%, but personally I prefer to have 60% success than just 40%. I think it is then really important to demonstrate (again with numbers) that Workforce Analytics initiatives are effective in increasing employee performance, in increasing sales, lowering turnover or time-to-fill key positions.
Examples of Workforce Analytics in action
These examples are quite famous but I very often refer to them in my course. First, you have the story of the Oakland Athletics baseball team described in the book/movie “Moneyball”. Billy Beane, the manager of this team, decided to use data in a way that no other baseball team had used before. He discovered that some players were undervalued by other teams and that they were in fact very good. By hiring these undervalued players, he was able to assemble an impressive team that beat some records that were never reached again.
Second, Google is probably one of the first companies to create its own Workforce Analytics department. Their analyses have demonstrated why some leaders were more effective than others (Project Oxygen) and they undertook a major shift to train all leaders in their company to master the skills of effective leaders. Another project (Project Aristotle) demonstrated that the major ingredient for team performance was psychological safety (the extent to which team members feel they can speak up without being reprimanded by others).
As a last example, they observed that grades obtained at university were not a significant predictor of job performance at Google. As a consequence, hiring managers stopped asking candidates about their grade transcript (I would like to mention that many other studies have shown grades to predict job performance across a variety of occupations). So what is true at Google is not necessarily true in other companies.
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