As the drama and paradigm-shifting presentations at Big Data London wound to a close on the second day of the conference, organizers got some of the big names in British data science together for a feature panel on how to make the most of data science in business decisions.
Chaired by Nicholas Deveney, Director of Consulting and Data (Eden Smith), the panel included Richard Pugh, Chief Data Scientist (Ascent), Michael Terrell, Head of Data Science (Channel 4), Shorful Islam, CEO (Be Data Solutions), and Megan Stamper, Head of Data Science (BBC Product Group).
So – how do companies make the most of data science to guide their day-to-day and strategic operations?
Solve the Right Problem
Richard Pugh had definite ideas. “You have to make sure your data scientists engage with the actual business problem. But also, business stakeholders have to engage the data promise, too. Business stakeholders find it very difficult to engage with the process. And in fairness, we in the data science community probably haven’t demonstrated consistent value, because we end up relentlessly and brilliantly solving the wrong question. So, to get the best out of your data science, nail the right question. The success of data science depends as much on people as it does on science.”
Nicholas Deveney added “Our ability to deliver relevant, actionable insights is key, too. If the task was to sell clothes, we could bring you insights like ‘On a Wednesday, people called John buy more trousers.’ But what the hell do you do with that insight? It’s not targeted insight, it’s just a factual output based on crunching the data. So, getting the right questions are key for the business stakeholders to get the most out of their data scientists, but our delivering the right, actionable results is key to ensuring they trust us to do it again, time after time.”
When Is Data Science Not Data Science?
Deveney then threw a new question out to the panel. “How complex does the solution need to be to be described as data science?”
Michael Terrell pondered the levels on which the question was applicable. “When the business has broad problems, you can’t code for them, so the problem gets broken up. But when the problem gets broken up, there are lots of answers to the various parts of the question, but CEOs don’t see the answers to their broad, initial questions.”
Megan Stamper added to that. “The nature of R&D accepts failure over a long time – and it sees the value of that. If you find out a way not to answer a question, it has value because it takes you closer to the right answer.”
That’s a pure, scientific logic that has come down to common understanding through Thomas Edison. Asked by a journalist in the 1920s how it felt to fail 1000 times in his attempt to make an incandescent lightbulb, Edison spun the question. “I did not fail 1000 times to make an incandescent lightbulb,” he remarked. “The lightbulb was an invention with 1000 steps.”
“But,” continued Stamper, “many businesses in the modern world won’t accept that. The idea of spending money to ‘fail’ is not commercially acceptable.”
The Importance of Play
Deveny took up that point. “How important is the ‘playground’ approach to successful data science?”
Stamper responded. “At the BBC, we have a large R&D team, so we have …….
Source: https://techhq.com/2022/09/big-data-making-the-most-of-data-science/