Countless organizations are dialing up analytics to turn the glut of enterprise data into actionable business insights.
But many of the endless charts, dashboards, and visualizations fall flat with their intended audience. Sometimes it’s a matter of overwhelming recipients with too much data; other times, it’s about presenting the wrong data or not fully understanding how to create an effective narrative that will resonate with recipients.
Enter data storytelling, a skill set handcrafted for the era of big data. While interpretations vary, most experts describe data storytelling as the ability to convey data not just in numbers or charts, but as a narrative that humans can comprehend.
Just as with any good story, a data tale has to have a beginning, a middle, and an end. It needs to be presented without bias and with the proper empathy and context so business users can absorb and leverage the insights for more intelligent decision-making.
“If you want people to make the right decisions with data, you have to get in their head in a way they understand.
Throughout human history, the way to do that has been with stories,” saidMiro Kazakoff,an MIT Sloan lecturer who teaches Communications & Data Storytelling as part of the school’s Masters of Business Analytics curriculum.
If you want people to make the right decisions with data, you have to get in their head in a way they understand.
Would-be data storytellers are coached to anticipate an audience’s likely response to analysis, Kazakoff said. Students learn to structure their planning and presentation to address the needs of a specific audience — whether it’s a colleague, a customer, or a boss — so they’re able to take away the right insights and initiate appropriate actions.
That’s not always possible with common analytics dashboards that simply alert business users to a specific change — say, a dip in sales or a spike in customer support calls — without providing insight into the entire story.
“It’s hard for a dashboard to explain why something is happening,” Kazakoff said.
Communicate with context
This year, Glassdoor ranked data scientist as the third most desired job in the U. S. with more than 6,500 openings. But PhD experts in statistics and mathematical modeling, or techies fluent in languages like Python and R, are just part of what’s required to be successful with data analytics.
It’s also essential to effectively communicate the insights and understand the perspective of an audience, which may or may not share that same view or have comparable fluency with the data.
More often than not, data analysts and data scientists don’t have range across both skill sets, said J.T. Wolohan, author of “Mastering Large Datasets with Python,” who has experience hiring data scientists for the private sector.
“Data scientists typically have point-and-shoot skills, but they can’t explain why they are doing what they’re doing,” Wolohan said. “They have a hard time working backwards from questions into practical business solutions. That’s really the missing skill set.”
Proficiency with data storytelling means being able to present information without injecting bias and to recognize what’s important and what’s not with the aim of keeping things simple. This requires effective data storytellers to be ruthless editors, Kazakoff said; avoiding the tendency to adjust data to fit preexisting story lines and making sure to frame the data into a story that the audience cares about.
“The skill of data storytelling is removing the noise and focusing people’s attention on the key insights,” explained Brent Dykes, a data strategy consultant and author of “Effective Data Storytelling: How to Drive Change with Data, Narrative, and Visuals.”
The skill of data storytelling is removing the noise and focusing people’s attention on the key insights.
“Part of the skill is building narrative and revealing data in the proper order and sequence, and then there is the visualization piece,” Dykes said.
Adept data storytellers not only have good sensibilities for presenting data graphically, they are also able to synthesize the findings down to a core set of visuals that gets the point across in the most direct, succinct manner, he said.
Perhaps the most difficult data storytelling skill to master is empathy — to understand where the audience is coming from and which parts of the data analysis they’ll react to, Kazakoff said.
For example, a sales executive and a software development head typically have countering worldviews, so when sharing the same data with them, there is likely to be vastly different reactions. It’s critical, therefore, that whoever is tasked with the data analysis has the capacity to interpret the different viewpoints and present relevant material accordingly.
“It’s not going to be a black and-white answer — it’s very much a translation task,” Wolohan said.
Job skill or job title?
To fill the gap, should businesses create new data storyteller roles or upskill its workforce so everyone has a foundational ability to understand, work with, and analyze data? Experts contend organizations should be doing both.
Dykes argued data storytelling is a skill that is essential for the broader workforce for success in what he called “the last mile of analytics.”
“Being literate with data and able to explain the stories it reveals is as important a form of literacy as being able to read, write, and speak clearly,” Kazakoff maintained. “It’s a core skill, not a job function, and it cuts across all division and roles at a company.”
Just like communications, some roles will require a deeper understanding than others, but Kazakoff said no one whose job is informed by data will escape the need to understand and explain that data to others.
Althea Davis, enterprise data governance manager at Etihad Aviation Group, agreed that data storytelling is a much-needed enterprise skill, but said she’d love to see a specific role take root to balance out the range of a data analytics organization.
“It’s such a steep learning curve for business people to grasp data literacy to a level where they can benefit,” Davis said. “They need molding and mentoring in a way that they can absorb. If we had really good data storytellers, it would make it so much easier.”