SHRM’s Roy Maurer recently highlighted a survey from KPMG showing that corporate leaders around the world remain distrustful toward their organizations’ data and analytics when it comes to using these tools to make business decisions:
In the survey of 2,190 senior executives from Australia, Brazil, China, France, Germany, India, South Africa, the U.K. and the U.S., just 35 percent said they have a high level of trust in their organization’s use of data and analytics. Another 40 percent said they had reservations about relying on the data and analytics they produce, and 25 percent admitted they have either limited trust or active distrust in their data and analytics. Nearly all respondents (92 percent) worry about the impact flawed data could have on their company’s business and reputation.
“Executives and managers are being asked to make major decisions based on the output of an algorithm that they didn’t create and don’t always fully understand,” said Thomas Erwin, global head of KPMG International’s Lighthouse, the firm’s center of excellence for data, analytics and intelligent automation. “As a decision-maker, you really need to have confidence that the insights you are getting are reliable and accurate, but many of these executives can’t even be sure if their models are of sufficient quality to be trusted. It’s an uncomfortable situation for any decision-maker to be in.”
One barrier to the credibility of analytics for business leaders is the prevalence of incomplete data; another is that the metrics against which organizations are measuring are often ill-defined. HR metrics like source of hire and quality of hire are particularly hard to measure accurately, Kevin Wheeler, founder and president of the Future of Talent Institute, tells Maurer, and there is significant disagreement on how best to define them.
KPMG’s findings resonate with our own research at CEB, now Gartner, and speak to some of the key challenges talent analytics functions are facing as organizations invest more in data-driven decision making. HR leaders have told us that their most pressing concern is aligning analytics to critical business questions, which they recognize as a critical step toward securing the confidence of the C-suite and convincing the entire organization to “buy in” and participate in the analytics program. If analytics are not producing actionable intelligence with bottom-line value, it can be very hard to convince executives that they are worth paying attention to. (Google’s approach to analytics offers an interesting lesson in how to align data to real business issues and avoid the trap of analytics teams chasing the most interesting problems rather than the most relevant ones.)
Our own research has found that poor data quality is the most-cited reason why talent analytics leaders feel that they aren’t having the impact that they want. The most effective solution to the data quality problem, however, is human, not technological: Analytics teams need to build the right relationships across HR and the broader organization to make sure the data entered into the system is accurate and useful.
Another interesting finding from KPMG’s survey, which may go some way toward explaining why analytics is struggling, is that executives still look at this as primarily a technical problem: When low-quality data leads to poor business decisions, a majority said they blamed the technology function or service providers, while even more said IT should be responsible for these decisions. HR and other core business functions may be tempted to blame technology when their data produces wrong answers, but these functions and their leaders must take responsibility for the trustworthiness of analytics if they hope to really weave data into the way their business operates.