What do I find the most enjoyable about HR Analytics?
Sure, being able to use data models and work with numbers interests me, but one of the best (and most vital) aspects is dealing with real-world business problems. That’s why this job exists!
Being able to translate important business problems into analytics questions is a vital skill for any HR Analytics practitioner. We’re not there to “mess with numbers,” we’re there to deliver value by pointing to data-driven solutions.
The problem has to be important to the business to get senior management buy-in for the solutions we offer. You need a combination of skills, including business acumen, technical knowledge and analytical skills.
Let’s look more closely at defining business problems as analytics questions:
Why defining business problems as analytics questions is important
The extent of analytics capability within any business really relies upon relevant, quality questions. Why? The value of HR Analytics lies in being able to come up with actionable ideas derived from accurate analysis.
It’s difficult to get anything beyond some kind of very basic insight without identifying the business problem, then forming your analytics questions from it first. It would be a bit like opening up your maps app but not really knowing where you want to go. If you wandered, there’s a chance you may get where you need to be, or at least somewhere interesting, but it’s not the same as having a clear plan.
Here’s another good reason why defining those questions is important; it saves you time by honing your focus. Very early on in my HR Analytics career I was excited to be asked to work on a project. After working all weekend and diving deep into the data, I had a presentation put together with a colleague for the following Monday. What happened? Well, we were told that our analysis wasn’t really what they were looking for. One of the main problems from our perspective was that the key problems weren’t defined well in the first place. We could work with the data and find all sorts of things, it’s just that those things weren’t important to the company right then.
“If I had an hour to solve a problem and my life depended on the solution, I would spend the first 55 minutes determining the proper question to ask, for once I know the proper question, I could solve the problem in less than five minutes.”
Albert Einstein
Einstein’s quote fits well with the world of HR Analytics. We’re at a point where we’re gathering so much data that of course we’ll find something of interest if we dig through it in search of anything juicy. The problem is that you need that clearly defined analytics hypothesis or questions first – otherwise, your “interesting find” may not help your business at all.
“Far better an approximate answer to the right question, which is often vague, than an exact answer to the wrong question, which can always be made precise.”
John W. Tukey
How to ask the right questions
To begin with, successful use of HR Analytics is about planning for the future, rather than being reactive to the present. You need to be able to look forward to predict how the issues or patterns of today may affect you tomorrow.
Being “anticipatory” is a good way to formulate the right questions, yet only 18% of HR Analytics professionals in one survey considered that they were looking forward. One of the common issues in HR departments is a tendency to be fighting whatever the latest “fire” is when it comes to people management. So they might be looking at people performance data, but not necessarily within the wider context of what’s happening in the business. To extract meaningful insights, it’s important to combine multiple layers of data.
In a One Brief report, here is how they put it:
“If HR departments focus only on performance data of employees without the broader business context, they will continue to struggle to see the big picture,” says Short. The key to extracting insight is to combine multiple layers of data to find meaningful relationships that help to predict future performance. Hickey explains, “employee engagement data can be combined with leadership feedback to build the profile of leaders that are generating the highest discretionary effort and productivity from their people. Ultimately, these insights will improve the overall quality of future investments in leadership recruitment and development.”
Mapping people data to priority business outcomes is a key way to help build business scenarios that will help achieve those goals. People are intrinsically linked with how the business does against goals for any department. Sales, revenue growth, return on investment – all of the “hard number” goals are related to the “soft” skills in managing people. The bottom line is that workforce measures impact on the bottom line…
According to SHRM:
The basis of all HR data study needs to lie in the real challenges of the business Click To Tweet“The point is to “get past the ‘what’ and fully understand the ‘why,’ ” says Cecile Alper-Leroux, vice president of human capital management innovation for Ultimate Software in Weston, Fla. “The benefit of using metrics is that the decisions are better-informed and backed by facts—rather than hunches—and thus make key people decisions far more ‘sellable’ to the business.”
So if we were to break asking the right questions down into a few steps, they would be:
- Identify a key concern, goal or problem for the business
- Create a hypothesis as to how human performance or behavior impacts upon that key concern
- Define exactly what you need to measure (the analytics question) to test that hypothesis
- Source the data from any departments that hold it.
What good questions look like
A critical factor for the success of HR analytics is to have the buy-in of senior management in the business. A common mistake is when HR gets excited about showing results, but they are really only meaningful to HR.
This can happen where the focus is too narrow. So you might look at data such as linkages between training programs and employee turnover, but when you’re presenting this to senior managers from other departments, it only loosely relates to something that is important to them.
It can be in how you present your findings. I’ve previously talked about how presentation skills are very important for HR Analytics practitioners. For example, if sales numbers are an important goal, perhaps an important piece of data to include is how turnover affects sales. You might demonstrate that training programs need further investment to lower turnover and improve sales numbers.
Marketing departments were earlier to the predictive analytics game than HR departments and HR can learn from their experience. Here’s how TLNT puts it:
“Today’s marketing leaders predict customers who will generate the most revenue (have high customer lifetime value). Marketing departments did not gain any traction with predictive analytics when they were predicting how many prospects would “click.” They needed to predict how many customers would buy.”
Good questions demonstrate correlation and/or causation with business goals.
Final thoughts
HR departments have a great opportunity to add value to the business through the use of predictive analytics. The key is to do so with strategic intent. The business problem should come first, followed by the definition of the analytics question.
Being very familiar with business problems outside of just the HR department will help practitioners to define the data they need to look at. The company will always want to see return on investment for data analytics to continue in HR, so it helps to look at problems that allow the business to continue!
Remember, your overall goal in HR Analytics is to get people not to say “interesting, however, irrelevant,” but to say “wow, let’s go ahead with that.”