Five reasons why data and analytics-based stories are important to organizations, and four reasons why so many people and organizations do it badly or not at all.

WHENEVER I speak with successful analytics people—and I do that all the time—it’s usually not long before they mention the phrase “telling a story with data.” It may seem obvious that anyone who is doing data analysis would want to create a narrative of the process and outcome, but to many data analysts it’s not obvious at all. So in this essay I’ll describe five reasons why data and analytics-based stories are important to organizations, and four reasons why so many people and organizations do it badly or not at all.

Here’s why I think people who love data and analytics also need to be people who love stories and tell them well:

1. Stories have always been effective tools to transmit human experience; those that involve data and analysis are just relatively recent versions of them. Narrative is the way we simplify and make sense of a complex world. It supplies context, insight, interpretation—all the things that make data meaningful and analytics more relevant and interesting.

1. With analytics your goal is normally to change how someone makes a decision or takes an action. You’re attempting to persuade, inspire trust, and lead change with these powerful tools. No matter how impressive your analysis is, or how high-quality your data are, you’re not going to compel change unless the stakeholders for your work understand what you have done. That may require a visual story or a narrative one, but it does require a story.

1. Most people can’t understand the details of analytics, but they do want evidence of analysis and data. Stories that incorporate data and analytics are more convincing than those based on anecdotes or personal experience. Perhaps the most compelling stories of all are those that combine data and analytics, and a point of view or example that involves real people and organizations.

1. Data preparation and analysis often take quite a while, but we need shorthand representations of those activities for those who are spectators or beneficiaries of them. It would be time-consuming and boring to share all the details of a quantitative analysis with stakeholders. Analysts need to find a way to deliver the salient findings from an analysis in a brief, snappy way. Stories fit the bill.

1. As with other types of stories, there are only a few basic types; couching our analytical activities in stories can help to standardize communications about them and spread results. It has been argued that there are only seven basic plots in all of literature (http://en.wikipedia.org/wiki/The_Seven_Basic_Plots).  I once argued that there are ten types of analytical stories (https://hbr.org/2014/05/10-kinds-of-stories-to-tell-with-data/). Regardless of the number, if an organization is clear on the different types of stories that can be told with data and analytics, it makes it more likely that analysts will explore different types over time. Most importantly, the story repertoire should go well beyond basic “here’s what happened” reporting stories.

Despite these compelling reasons for the importance of stories, most quantitative analysts are not very good at creating or telling them. The implications of this are profound—it means that analytical initiatives don’t have the impact on decisions and actions that they should. It means that time and money spent on acquiring and managing data and analyzing it are effectively wasted.

So why are individuals and organizations so bad at telling stories with data? Let us count the reasons:

1. Analytics people often aren’t that motivated or successful at communicating with carbon-based life forms. They gravitated toward structured, unambiguous, unchanging fields like math and statistics and computer science in school, and they continue to favor interaction with numbers over interaction with humans in their work careers. Of course, not all quantitative analysts are of this persuasion, and someone with a strong numerical focus can transition over time to be more human and literary in their orientations. But let’s just acknowledge that telling compelling stories to other humans may not come naturally to many analysts.

1. If analysts don’t gravitate naturally toward storytelling, they probably don’t get a lot of instruction on it in school either. Many college faculty members teaching quantitative courses are themselves not terribly good at storytelling. And they may feel that it’s more important to impart more instruction on methods than to “waste time” on storytelling approaches. This is incorrect, however, from the customer’s perspective; a survey (see this link for a summary--http://www.statslice.com/wp-content/uploads/2013/03/State-of-Academics-My-Article.pdf) of about 400 recruiters of analytical college graduates found that the highest-ranked of all desired skills was communication.

1. To indulge in storytelling, some analysts may believe, is an insult—or at least a relatively less valuable investment of time in comparison—to an analysts’ technical capabilities. Capable quantitative analysts may justifiably argue that many people can tell good stories, but relatively few can run a logistical regression model with heteroskedasticity corrections. They may feel that the highest and best use of their time and brain cells is to do quantitative analysis, and to rely on others to tell stories about it. They may have a point, but relying on others to translate analytical results into stories has some perils of its own, in addition to being more labor-intensive.

1. It takes a lot of analysts’ time to think creatively about how to tell a good story with data. In fact, one senior analyst at a pharmaceutical company told me that he (and most members of his analytics group) spend about half their time thinking about how best to communicate their analytical results. Many analysts will be reluctant to devote that much time to the issue, even if it would make them more effective.

So there are several reasons why storytelling with data is critical to success with analytics programs, and several reasons why it doesn’t work very well. I’ve constructed this story so that there are more reasons to tell good stories than there are obstacles to the objective, so the story ends happily.