Читать книгу Becoming a Data Head - Alex J. Gutman - Страница 20

Why do data problems like this occur? We assign three causes: hard problems, lack of critical thinking, and poor communication.

First (as we said earlier), this stuff is complex. Many data problems are fundamentally difficult. Even with lots of data, the right tools and techniques, and the smartest analysts, mistakes happen. Predictions can and will be wrong. This is not a criticism of data and statistics. It's simply reality.

Second, some analysts and stakeholders stopped thinking critically about data problems. The Data Science Industrial Complex, in its hubris, painted a picture of certainty and simplicity, and a subset of people drank the proverbial “Kool-Aid.” Perhaps it's human nature—people don't want to admit they don't know what is going to happen. But a key part of thinking about and using data correctly is recognizing wrong decisions can happen. This means communicating and understanding risks and uncertainties. Somehow this message got lost. While we'd hope the tremendous progress in research and methods in data and analysis would sharpen everyone's critical thinking, it caused some to turn it off.

The third reason we think data problems continue to occur is poor communication between data scientists and decision makers. Even with the best intentions, results are often lost in translation. Decision makers don't speak the language because no one bothered to teach data literacy. And, frankly, data workers don't always explain things well. There's a communication gap.