Three Simple Steps to Setting Up Your Analytics Strategy

Many organizations today embrace workforce analytics as an imperative to make smarter workforce decisions and drive more value for their business. A 2013 study showed 60% of HR executives indicated they have new budget allocated for analytics. However, only 4% of companies studied have done any predictive analytics related to people data and only 14% are doing anything significant beyond mere operational reporting.

As the infographic shows, HR ranks very low in the pecking order of departments currently benefiting from analytics, but that is likely to change significantly over the next couple of years based on the business patterns we are experiencing.

To move the needle from where they are now to a realistic desired state, HR executives often come to us for help getting started.

We have found a gap between wanting and actually setting up an analytics department, as the perceived complexity of it tends to intimidate people. Or, when creating an analytics strategy, they get excessively ambitious about seeing results in the short term. My recommendation to them is to start with a quick win.

One way to do this is to look at the outliers of performance. First, identify your key operational metrics that the top leaders look at every day because they lead to sales or profit. Second, understand what drives these metrics, and finally, replicate these drivers or processes across the organization.

For example, in the retail industry you might look at “items per transaction”, in banking it might be “net promoter scores”, or in manufacturing you could look at safety metrics. All of these have a human domain and can be dramatically influenced by people that work for you. Pinpoint the specific operational metric relevant to your organization then identify which job family has the biggest impact on that metric. Next, evaluate the best employees from that job family, which have a disproportionate impact on that specific metric. Then, dive deeper to identify specific personal and organizational attributes these individuals have or have experienced that influence this metric. You can do this through a series of diagnostics to ascertain all possible influencers, like personal behavioral traits, workforce characteristics, career events, engagement drivers, geographies, particular systems or processes they follow, people-influencers (e.g., social collaboration), and the like. You then use a model with all the data to help predict which of these systems, processes, or behaviors have the biggest impact on success – or a combination therein. Using this, you can use an evidence-based approach to HR and help eliminate some level of guesswork in your people strategy. Essentially you recreate the success of the top 10%, by understanding how they became your best.

This is an example of a quick win and can help you build beyond predictive models, leading to a higher level of analytics. It might sound counter-intuitive, but higher order of analytics can actually become quite simple in day-to-day management and make decision making easier for employees, managers, leaders and executives.

While big data is a buzzword at present, we are driving to make big data small and actionable so people can use it pragmatically, in their day-to-day lives, to augment decision-making process rather than replace it.

Starting off simple is the first step in building that analytics department –and if you don’t have a predictive model sitting in your back pocket, contact us. We will help you out there!