We have seen a great deal of buzz surrounding HR analytics in the last couple of years. There have been publications across the globe covering lessons learned in HR analytics initiatives. After reading much of this content, a few observations stand out.
First, getting from analysing people data to achieving tangible results for your organisation is a difficult thing to achieve. There are many initiatives in HR analytics, but when it comes to specific examples of results of this new business intelligence form, the same cases in point keep appearing. Especially, Google and its outstanding work that’s ahead of the rest.
Second, highly intelligent, analytical people are active in this field of sport. But when it comes to being business savvy and adding real dollar value to your organization, the pool narrows noticeably. HR analytics are a means to an end, not the other way around. If you are passionate about data science and less about running a business, a career in science may be a better match.
When reading about others’ experiences in this new field, a few basic pitfalls keep coming up. And these pitfalls I recognize from my own experience working on this subject. So here’s a look at the 5 most common mistakes when starting with HR analytics:
One of the first questions to ask yourself before beginning any HR analytics project is ‘why’? What is the goal of your efforts? How will your organisation profit? Frequently, HR departments start working on HR analytics without a clear vision on the subject. There is so much hype that HR directors are afraid they’ll be left behind if they don’t start doing something analytical soon. In too many cases, working on analytics becomes a goal in itself. Often, new software, complex statistical techniques or new HR technology is the starting point for analytical ambitions, which is putting the cart before the horse. Business questions should always be leading, not technology. HR analytics only adds value if you can tackle a real, specific business problem. Don’t start your analytics journey working on a flashy analytics dashboard that no one needs. Work on something that will lead to real cost savings, or otherwise, better performance. Only then will you earn the trust of business leaders.
This is simple. If your HR analytics project involves personal data about employees, serious risks concerning people’s privacy comes into play.There are rules and laws you need to comply to. You have to think of the legal consequences of what you are doing. You need to involve your colleagues from legal. Yes, this might take time and delay your initiative, but making mistakes in this area is lethal. Employees need to feel that their data is safe and handled with integrity. If you lose the trust of your colleagues, you will have a very challenging time earning it back. So get the information you need from legal, then collect and analyse data the right way without breaking any laws.
Meeting an expert data scientist that understands the role of HR and also convinces your CEO is a rare thing. HR analytics is a complicated field. To be successful, you need multiple types of people with varying skill sets in your talent analytics team. You need people who understand and are skilled in HR, IT and data science. But you will also need people who understand the business side of things to bridge the traditional gap between HR and business. You can only be effective in HR analytics if you can make this assortment of people work together cohesively. This is tricky because everybody thinks their background or skills should be leading the way. In reality, you need a range of skills and ways of thinking during each phase of your project. You need razor sharp focus to keep moving ahead and not get stuck in the complex details.
If you are an HR enthusiast, you will be eager about the new possibilities HR analytics have to offer. For the rest of the world, your cherished HR analytics project is just another business intelligence pilot. Chances are, there have been more than 20 new projects across your organisation involving data in this last year alone. Big data is red hot and not only in HR. So don’t expect your colleagues to be as over the moon as you are. Usually, I see HR professionals working on HR challenges nobody else really cares about. To get the attention of colleagues beyond the borders of HR, you have to address distinct business issues. You have to tell a convincing story about you adding value that everybody can directly understand. So always start with a real-life business concern that keeps your colleague’s up at night before you start thinking about an HR analytics approach.
It is better to start small. You already believe in the huge potential of this thrilling new field of sports. Your colleagues from Finance or IT have yet to be convinced about the value of HR analytics. Don’t make the mistake to overpromise on your expected outcomes. HR analytics will not lead to millions of cost reductions in the first few months. In HR analytics, progress comes slowly. It is a complex and time-consuming ordeal. HR analytics project always take longer than you think, and the outcomes are not as overwhelmingly clear as you hope. So stay humble and keep a low profile. Keep your focus and work hard. Once the analytics train start to build up speed and you can show your first real results, you can start to make more noise.
So where is this exciting new approach in HR taking us? Are HR analytics just a hype or more of the same? Considering everything that is written about this subject, I believe the real value of HR analytics is not so much in making HR more efficient or less costly. Reducing costs or risks is not the way to stand out in today’s modern age of business. Hiring and retaining the very best talent for key positions, having a highly engaged workforce, working on innovating in high-performance teams, that’s what the future holds. HR analytics gives an opportunity to finally understand the drivers behind high performance, motivation and innovation. So I believe HR analytics should focus on improving the quality of human capital and less on cost efficiency.