Reporting capability is always a key requirement for any business deciding on an HR system. Interestingly, while this capability is considered important, most HR reporting remains on the lower end of the analytics maturity scale.
This begs the question: What is holding businesses back from completing more advanced analytics?
In this blog, I’ll explore some of the common reasons I’ve come across and recommend how to move your analytics up the scale.
We have issues with data accuracy
In a manager/employee self-service environment with thousands of entries from hundreds of people, no database will be 100% accurate. This is no reason to avoid using the data—in fact, just the opposite. When you use the data, you and many others are looking at it, which helps identify and correct errors.
We are so stuck in hygiene reporting, we don’t have time for strategic reporting
In many cases, hygiene reports such as headcount and turnover are the data leaders ask for, or even expect. But is this really the information they need? And do these reports provide true actionable insight?
Obviously, these reports have a level of operational importance. However, problems can be hidden within aggregated data. Take employee turnover, for instance. I’ve seen many companies fall into a trap whereby they are tracking their employee turnover, which has remained stable for many years. But in reality, hidden issues are bubbling away and remaining unseen.
During a recent customer visit, Peter Howes, vice president of Workforce Planning and Analytics at SAP SuccessFactors, compared analytics to mining. In mining, the first step is a seismic survey in which several high-level tests are done to identify areas of potential further investigation.
Based on the results of these surveys, test drilling is completed and more focused samples are taken and examined. If these results show items of interest, then core samples are taken. Investment in the mine happens only after the success of core samples.
If high-level metrics are like seismic surveys, then data segmentation is like test drilling.
In a recent example, I observed that employee turnover at an aggregated level had remained relatively static over the years, perhaps moving up or down by half a percent year on year. By simply adding a level of data segmentation in the form of performance ratings, a clearer picture emerged: A large percentage of those who left the business were in the higher levels of performance rating. Segmenting this even further showed that many of the employees who left the business left between 3 to 4 years of service.
With this new insight, a variety of strategies emerged. For instance, like aggregated data, aggregate feedback is difficult to act on. Rather than performing exit interviews for all employees, managers focused on high performers only. In addition, reporting on high performers with 2-3 years of service, they identified those high-performing employees who were at risk of leaving the business in the next 12 months. Based on the exit interview data, interventions were put in place to engage with these employees to turn this trend around.
We don’t know what reports we should be focusing on
The simplest way for HR teams to use metrics to speak the language of the business is to align HR KPIs with the businesses KPIs. That means if the business’s core aim is sales, HR should be measured on sales; if the business is measured on research funding generation, then HR should be measured on the same. By bringing this data into your analytics system, you then start to draw correlations between HR strategies and the primary output of the business.
One example of this was a retail customer with sales and store demographic information fed into their analytics database. This allowed them to track the impact of learning modules completion on sales. By identifying a sample set of stores using the demographic data, they rolled out training on product knowledge and sales skills to employees in these stores alone. They then measured the impact of this training on sales.
Based on the relative impact of this training, they were able to accurately measure the return on investment the training delivered and therefore determine which courses to roll out nationally and which ones to simply drop.
We are not sure we have the people with the right skills
While learning how to create queries and reports can be managed both on the job and through online courses, developing a sense of where to invest your seismic survey activities can be tricky.
A simple way to start developing confidence in this area, which Peter often shares with customers, is to meet once a month or every few months as a HR team. In these meetings, members of the team pick an element of data or a sample insight that interest them and take turns presenting and debating it. Do some test drilling, take a core sample, and explain why you think there could be more to find lurking underneath.
This will not only enable several elements to be investigated and considered over a shorter period, it will also allow you to hone these skills as a group and develop a culture of developing insights from analytics. In addition, it will help you tell a more compelling story, which can then be applied across the HR group with the various business leaders each HR partner supports.
For more on the power of data analytics in business strategy, see Data Lakes: Deep Insights.