“Pietà” (1498–1499) by Michelangelo (1475–1564). Photo by Art Gallery ErgsArt.
Jefferson McMillan-Wilhoit is the Director of Health Informatics and Technology at the Lake County Health Department and Community Health Center. He recently spoke of storytelling’s critical role in data analytics/science as part of modern data-skills school Promotable’s series of events. He stated—and restated—the importance of this step:
“Take the data and have it tell its story.”A prime directive. No magic formula. Obstacles are always in play against good data storytelling. Jefferson urged minding the ingrained bias and the quality of the data itself. The former is significant—if unchecked, data analytics gets skewed toward cognitive predisposition, notably confirmation bias (among a great many others). The latter reinforces what previous Promotable presenters have also stated—that the quality of the data is in direct correlation to the quality of its analysis.
A point by Jefferson that stood out most to me was how much he enjoys, as he put it, “Amazing Stories.” He shared his fandom for good storytelling in literature and movies. As it applies to data analytics/science, Jefferson referenced the primary building blocks possessed by a good story: the opening scene, episodes of crises and the convergence toward denouement, all happening along a timeline. Intellectual nourishment is found in stories. Jefferson encouraged making the thorough and transparent effort in achieving this outcome as it applies to the utilization of data. In essence, storytelling of data to promote data-driven understanding to then contribute to evidential decision-making.
Storytelling also brings a sense of wonder, even awe. Jefferson’s repeated ask of “Is this telling a good data story?” recalls one amazing account of creativity—a true story. Michelangelo di Lodovico Buonarroti Simoni (1475–1564) created masterpieces of art. From amorphous stone, he shaped compelling sculpture. His motto: “Beauty is the purgation of superfluities.” Through the lens of data analytics/science, “superfluities” could refer to analytical bias, dirty data or other nonessentials. Like a data analyst/scientist telling the story of a specific set of data, Michelangelo was telling the story of another kind of raw material: stone.
Great data. Great analysis. No superfluities. In key ways, Jefferson, a classically trained data analyst, is channeling the clarity also sought by Michelangelo. Whereas the Renaissance artist used marble, Jefferson and his team use data—using it because it makes the best job of the truth. Amazing.
Thanks again to Promotable who connect their virtual workshops to relevant perspectives through their organizing of regular talks online! Explore their channel on YouTube.
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