To my surprise and delight, “communication” topped the list of key skills for data scientists in a CEB Market Insights blog post I read this week.
The post covered the top 10 skills for data scientists and 2 strategies for hiring them. Yet “communication” felt like a lone outlier among a list of highly quantitative skills, like managing structured data, mathematics, data mining and statistical modeling.
But indeed, the Business Broadway study the post cited showed that “communications” recurred the most frequently across a variety of data science roles.
When Thomas Davenport and D.J. Patil named “Data Scientist” the sexiest job of the 21st century in Harvard Business Review, they cited an enduring need “for data scientists to communicate in language that all their stakeholders understand – and to demonstrate the special skills involved in storytelling with data, whether verbally, visually, or – ideally – both.”
As a communicator who pivoted into marketing analytics, it’s heartening to to see data showing there’s a role and need for effective communication and storytelling skills.
And having led communications, the field is dramatically improved by data that demonstrates what works and what doesn’t, and helps predict how various audiences might respond to different communications strategies.
Beyond enabling data-driven decisions, clear communications about data can literally be a matter of life or death. Two fascinating examples crossed my path this morning in an article by Dr. Jenny Grant Rankin called Over-the-Counter Data: the heroics of well-displayed information.
The first example was an early use of data visualization in the summer of 1854. In London, 500 people died of mysterious causes in a 10-day period. A Dr. John Snow made his data user-friendly. He took a neighborhood map and noted the exact locations where people had died.
This pointed toward a local water pump that was the culprit in the spread of cholera. With this clearly displayed data, Dr. Snow was able to convince authorities to remove the pump’s handle in order to stop the outbreak.
Another example took a much more ominous turn. The night before the Space Shuttle Challenger launched in January 1986, NASA engineers and their supervisors looked at charts and data on the rocket’s O-ring function. This is what keeps hot gasses contained. Based on what they saw, the launch was cleared for takeoff.
But the available data was not displayed clearly. It showed failed launches, but not successful launches. And this led decision makers to overlook a critical piece of information – the O-rings worked properly only when the temperature was above 66 degrees. The day of the Challenger launch was 30 degrees below that. It was “so cold it does not even fit on the graph.” It’s still heart wrenching to recall the tragedy that occurred that day.
While thankfully the work of data scientists is rarely a life or death matter, these examples underscore the need for clarity in communicating data. For what cannot be understood cannot be implemented.
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