Think of the nearly iconic television spots you've seen that promote the efforts of nonprofit organizations dedicated to combating hunger worldwide. These commercials invariably show stark images of children who have suffered the ravages of malnutrition and lack of medical care. Thinking back to those commercials, what do you recall? Is it the specific statistics regarding hunger the voiceover mentions or the images of the children and the stories told about their circumstances? For most of us, it's the latter, and it's not because the numbers aren't important but because our brains are wired to react more strongly to emotional cues rather than to dry facts.

As HR professionals, we obviously aren't facing problems as critical to humanity as world hunger, but I use that example because the people who design those campaigns know human nature and understand that statistics alone aren't enough to sway their audience. And although the decisions we make at work may not solve problems on that level, they can have a profound impact on the people with whom we work and their families, so the stories connected to the numbers are just as vital to communicate.

It wasn't all that long ago that it was difficult for HR to get basic measures such as headcount and demographic information. But as technology and techniques became more sophisticated, so too has our ability to capture and quantify data about abstract concepts such as engagement and commitment. Now we are sitting atop a mountain of data, with statistical proofs in one hand and probability models using predictive analytics in the other, shouting percentages of likelihood and blabbering on about correlations.

And nobody's listening.

That's because statistics can be overwhelmingly complex, confusing and dry, and storytelling is a difficult art that doesn't come naturally to many of us. At a recent meeting of i4cp's Evidence Based HR Exchange hosted by Cisco, I noted that an overwhelming number of participants were preoccupied with the idea of how to turn data into actionable information. And in a recent survey conducted by i4cp (to be released later this year) on behalf of the EBHR Exchange, 51% of respondents indicated that they have encountered "difficulty interpreting or finding the story within the data."

Obviously if this were an easy problem to solve, it wouldn't be such a prominent issue. However, the consistent advice suggested by those who are most successful at communicating data meaningfully is to always remember the human aspect. This is the one advantage HR has over almost every other department when it comes to data; there are nearly always examples of the data that involve individual people.

Here's an example: imagine you have data showing that 23% of your employees report being unhappy with recent changes in your organization's flex-time policy, and the policy has resulted in only a minute improvement in productivity. That may be slightly interesting, but if you put a face on the story, say, longtime employee "Monique," who was a high-performer but left the organization because her new work schedule made it impossible to pick her son up from day-care on time, you might have more people thinking about how these policy changes affect the employees, and how that in turn can impact the company.

Granted, it's time-consuming to interview employees and difficult to get people to speak candidly about their workplace experiences, but a blend of qualitative and quantitative data is sometimes necessary for effective storytelling purposes. We see this concept in action in every political season and especially in national elections. Presidential debates often degrade into volleys of statistics thrown out by both sides, but it's the anecdotal stories that resonate and that we remember. Case in point: the "Joe the Plumber" phenomenon that emerged during the 2008 elections. Bottom line: people remember characters and stories.

Ron Gier, VP of Human Resources at Sprint, recently spoke with i4cp and expanded on this idea in an example from his own workplace. "Our survey might tell us that we have 75% [favorable] score on job opportunity, but a lower score on job satisfaction. These scores might sound conflicting, putting the credibility of your whole survey at risk. To help our team best use the data to make decisions, we try to describe the results in a way that resonates intuitively and resolves the conflict."

"The intuitive description," Grier continued, "is that an employee could see their job as a great opportunity and be enjoying it, but also see a lot of stuff around here that he/she finds needing improvement. Can I imagine 20% of my workforce looking around their office and thinking those same things? Yeah, I can. I can get it. Now I can paint a picture of what the data tells me that improves decision making - capitalize on the strength of the job opportunity and focus improvement efforts on some of the day-to-day things that employees find frustrating.

That's the other part of the equation for overcoming data issues. You've got to paint a picture with it. My favorite common data conflict is the perception around pay satisfaction being critical to retention. Managers generally conclude that if there is low pay satisfaction there will be turnover, but most research suggests that pay satisfaction is not predictive of turnover. Managers struggle with this counterintuitive twist, and often need a more illustrative example to grasp it. I suggest that they probably know someone who never complained about their pay, but left anyway. They also probably know someone who complains about their pay all the time, but never intends to leave. By painting those mental pictures for people, they will tend to be a little more open to what the data are telling them.

Naturally, there will always be some data that doesn't lend itself as readily to telling a compelling story, but then again, not all data requires action. Just like data from any department, not all HR data is useful. But in the cases of data that should be acted on, look for details from the data set and try to get some actual examples of how it plays out in practice. Not only will the stories help form the narrative, they may help explain the causes of the results.

Storytelling may not be a skill that all of us were born with, but as it is with many things, it can be improved with conscious practice. Developing this capability is an imperative, because people are behind every number we crunch and the stories we tell help to effectively communicate the messages that are most critical to our organizations. To get started, focus on the change you want to promote, have strong statistical backing, and above all, remember to give your audience a relatable and human story that will motivate them to care about the data, the issue, and the people it represents.

Cliff Stevenson is a Human Capital Researcher for i4cp. Anyone who is interested in learning more about hunger in the United States, including where to donate, can follow the link here.