The next chapter in analytics: data storytelling
As with any good story, a data tale needs a beginning, a middle, an end, and some actionable insights. Data scientists aren’t always up to the job.
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,” said 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. 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.
