by Angela Guess
Joe Brockmeier reported that an audience of data scientists recently “narrowly agreed that their arsenal of tools and algorithms trumped the knowledge and experience of the meteorologists, financiers, and retailers to whose domains data scientists are increasingly turning.” Paul Miller counters, “This is an extremely worrying attitude, and I can only hope that those who hold it realise the error of their ways before they make a catastrophic mistake that adversely affects the rest of us.”
Miller goes on, “Data scientists are an increasingly capable bunch, and the tools at their disposal sometimes appear almost magical in their capability to derive insight. Competitions such as those run by Kaggle (more on them in a moment) clearly show that an aptitude for numbers and analysis can deliver some remarkable results, even when that analysis is being undertaken by individuals who lack specific domain expertise.”
He adds, “But to suggest that simply ‘letting the numbers speak for themselves’ is an effective way to make real decisions is, quite simply, bonkers. Data is merely one input to an effective decision making process. Prior knowledge, policy considerations, and an awareness of experimental bias, sampling error, and quaint notions such as ground truth continue to play a fundamental part.”
photo credit: moonlightbulb
