Whether your organization is considering the use of big data and analytics, or has taken its first
Telling a compelling story with your data helps you get your point across effectively. Here are four tips to keep your data from getting lost in translation.
Organizations can do a lot more with their data if they understand it better than they do. While businesses continue to invest dollars in business intelligence (BI) and analytics tools, they aren’t necessarily getting the information they need to improve business decision-making.
Data visualizations help by transforming complex information into something easier to understand. However, two people can interpret the same data visualization differently. Notably, data visualizations tend to answer “what” questions, but they don’t tend to explain the “why,” or provide other contextual information. Data storytelling does exactly that.
“Data storytelling weaves data and visualizations into a narrative tailored to a specific audience in order to convey credibility in the analytical approach, confidence in the results, and a compelling set of insights that is actionable to the audience.” said Ryan Fuller, general manager at Microsoft and former CEO and cofounder of enterprise analytics company VoloMetrix, in an interview. “The narrative is the key vehicle to convey insights, and the visualizations are important proof points to back up the narrative.”
Executives, managers, and employees have always told stories as part of their everyday work experience, but they are increasingly being required to use data to support their points of view, claims, and recommendations. The danger, of course, is data can be tortured into saying almost anything.
“One of the biggest mistakes is trying to fit the data to the story, which often results in a jumbled narrative that doesn’t arrive at a compelling conclusion,” said Francois Ajenstat, VP of product development at BI and analytics solution provider Tableau, in an interview. “Always start with the data, then build your story around it, rather than vice versa.”
After speaking with experts in data science and analytics, we’ve developed the following four tips to help guide your data storytelling.
Effective data storytelling is a lot like storytelling generally. The data story should have a beginning, a middle, and an end. It should also include a thesis (or a hypothesis), supporting facts (data), a logical structure, and a compelling presentation. Yet, all too often, those responsible for analyzing data are unable to present it in a way that’s meaningful to the audience.
“A common mistake is spending too much time on the technical aspect or methodology and not providing much creativity in pointing out how the data can help the business,” said David Liebskind, VP of analytics at consumer financial services company Synchrony Financial, in an interview. “While data visualization tools are effective, the human element to provide context, interpret results, and articulate insights and opportunities is a critical factor to influence key stakeholders and generate dialogue to drive strategic decisions.”[From fashion to food to healthcare, IBM’s Watson has many guises across different industries. To learn more about them, see IBM Watson: Machine-of-All-Trades.]
Like good stories generally, data stories should be designed to have an intended effect, which may be to evoke emotion, sway an opinion, justify a course of action, or inspire further exploration.
“An effective story includes an engaging and timely message, a point of view, an attractive visualization, and the right target audience,” said Zoher Karu, VP of global customer optimization and data at eBay, in an interview. “Classic structures in storytelling include three distinct acts: finding the conflict, adding the characters, and calling out the drama. The most successful data storytellers find a way to use these acts for impact.