Adventures in data storytelling: Three key traps to avoid (part 1)

As your organization seeks to become more data-driven, data will become an integral part of all communications. It should play a key role in your business meetings, internal conversations, and other daily interactions. As more of your people are exposed to data that is both relevant and crucial to their roles, communicating effectively with data will be an essential skill everyone will need to master—from the CEO to frontline employees.

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Whenever you combine or arrange data and other elements for a specific purpose—analysis or communication—you’re creating a data composition. Today, data specialists are no longer the only ones being tasked with creating various data compositions such as data presentations, reports, dashboards, infographics, data visualizations, and so on. Data stories are a unique set of data compositions that blend data, narrative and visuals in an effective manner to help explain, enlighten and engage. Data stories are powerful vehicles for sharing data insights to influence and drive change within an organization.

However, there’s more to a data story than just a flashy set of data visualizations or a compelling narrative. To a casual observer, many data compositions may look and smell like data stories because they include numbers and charts. While all data stories are data compositions, not all data compositions are data stories. On closer inspection, you’ll notice many supposed data stories are missing some of the essential attributes that constitute an effective data narrative. Flawed data stories can end up impeding business decision-making as their weak narratives, biased data and confusing charts fuel bad decisions or indecision. To avoid falling into potential data storytelling traps, it is important to first understand the two-step process in which data stories are formed.

Indiana Jones and how data stories are created

To help explain how data stories are developed in two stages, I’ll compare the process to my childhood hero, Indiana Jones. As a field archaeologist, Indy would travel around the world pursuing rare antiquities and battling evil Nazi henchmen. On the other hand, he was also a college professor who would share the tales and spoils of his archeological adventures with his students, fellow faculty and university museum.

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Similarly, all data stories begin with an exploratory phase where someone examines a dataset for meaningful insights. At this stage, you’re the daring, inquisitive adventurer, Indiana Jones. This initial phase can be as straightforward as studying a simple data table or as complex as performing a series of advanced statistical analyses. Data visualizations will often serve an integral role in helping you to uncover key patterns, trends, and anomalies in your data. Speed and flexibility drive these initial data visualizations as you navigate through the data, exploring different paths and perspectives.
At this stage, you are the sole audience of these exploratory data visualizations so they only need to speak to you and no one else. As you assemble the data for these charts, you gain a more intimate knowledge of the underlying data. While you may seek to confirm a hypothesis or hunch, you won’t yet know what narrative will emerge from the data. Your initial ideas may be proven right, completely contradicted or even pulled in an unexpected direction.

However, once you discover a meaningful insight you now have a story to tell and must transition to the explanatory phase. You now become Dr. Jones—professor of archeology and popular classroom lecturer. Now, you are no longer the audience as you seek to share your key findings with others. There’s also a very good chance your intended audience won’t know the data to the same degree as you do so simplicity and clarity become essential elements in your usage of explanatory data visualizations. Notably, the data visualizations that helped you to discover an insight will often need to be refined or even replaced to more effectively communicate what you discovered to a less-informed audience.

With this understanding of how data stories are formed, you’re ready to examine the three key traps that people can fall into when attempting to tell effective data stories.

**This article was originally published on Forbes.com on April 25, 2017.