Talent analytics has rapidly grown from an experimental trend into something every organization uses. While many HR functions are investing in analytics, however, few are getting the kind of results they’d like to see. If the promise of talent analytics remains unfulfilled today, it’s not because the technology isn’t ready. Over the past two years, we have heard from HR leaders that their biggest challenge in implementing analytics has been in connecting the data to critical business questions and drawing actionable intelligence from it. Gartner research has also found that collecting high-quality, credible data is a significant hurdle for many organizations.
Perhaps as a result of these growing pains, a global survey earlier this year found that most C-suite leaders don’t have a high level of trust in their analytics programs. HR is still under pressure to get senior leadership on board with talent analytics and prove its value to the bottom line.
At Gartner’s ReimagineHR event in London last Wednesday, Principal Executive Advisor Clare Moncrieff moderated a discussion with a panel of leaders at major companies on the practical lessons they have learned in applying talent analytics on the ground. The panelists were Christian Cormack, Global Head of Workforce Analytics at AstraZeneca; Nanne Brouwer, Head of People Strategy and Analytics at Royal Philips; and Jacob Jeppesen, Specialist in HR Analytics at Novo Nordisk A/S.
The limiting factor for talent analytics professionals is rarely their knowledge of analytics, the panelists observed. Rather, it’s their knowledge of the rest of the business. Understanding how other business functions like supply chain or strategy work allows them to combine different sources of data that have never been looked at together before. This combination of data is ultimately more valuable than extremely advanced analytics that focus only on people data.
When it comes to turning analytics into action, the key is to build compelling narratives. Information doesn’t drive action; stories do. A list of facts and figures might go in one ear and out the other, but people will remember a good story years after they hear it. By the same token, however, if you tell a compelling story that’s wrong, misleading, or misunderstood, that story will also be remembered and continue to drive action years down the road. That’s why it’s also important to make sure the stories we tell with data accurately reflect what the data tell us.
One of the panelists noted the extensive work their organization’s analytics function does with academic researchers. Because analytics is such an exciting and new subject, academics are hungry for real people data to work with, which organizations have in spades. There are a lot of opportunities currently for organizations to collaborate with academics (who will do the work for free) in interpreting and drawing insight from their talent data. When working with academic researchers, however, it’s important to keep them focused on critical business questions, to prevent them from running down rabbit holes. (As other experts have noted, the same is true of data scientists employed by your organization.)
For talent analytics to succeed, it requires the collaboration of HRBPs and other stakeholders in HR and other parts of the business. These partners don’t need to be trained as data scientists, the panelists noted, but they should learn how to be good data consumers. In other words, they need to know how to interpret data responsibly and how to drive action with the data they see. High-level data science skills may be necessary for those designing and leading an analytics program, but data judgment is the skill the organization needs to put analytics to work. This is why companies have been exploring new ways to infuse data savvy throughout their workforce, such as Airbnb’s internal Data University.
The last key point touched on in last week’s discussion is the hot-button issue of data privacy. Employees may be wary of analytics programs that collect data on their day-to-day work activities. Even if employees’ names are not attached to their data, it often takes just a few data points to identify someone, so organizations should be very careful with what they promise their employees with regard to anonymity. Instead of promising anonymity, the panelists recommended, analytics leaders can communicate that the data is confidential—and it’s important for the organization to make sure employees can trust that confidentiality is actually upheld.