Читать книгу Becoming a Data Head - Alex J. Gutman - Страница 43

So far, we've tied project failures to not defining the underlying problem correctly. Mostly, we've placed this failure in terms of losing money, time, and energy. But there's a broader issue happening all over the data space, and it's something that you wouldn't expect.

Right now, the industry is focused on training as many data workers as possible to meet the demand. That means universities, online programs, and the like are churning out critical thinkers at lightning speed. And if working in data is all about uncovering the truth, then Data Heads want to do just that.

What does it mean, then, when they sit down to a project that doesn't whet their appetite? What does it mean for them to have to work on a poorly defined issue where their skills become bragging rights for executives but don't actually solve meaningful problems?

It means many data workers are dissatisfied at their jobs. Having them work on problems overly focused on technology with ambiguous outcomes leads to frustration and disillusionment. Kaggle.com, where data scientists from all over the world compete in data science competitions and learn new analysis methods, posted a survey and asked data scientists what barriers they face at work.² Several of the barriers, listed here, are directly related to poorly defined problems and improper planning:

 Lack of clear question to answer (30.4% of respondents experienced this)

 Results not used by decision makers (24.3%)

 Lack of domain expert input (19.6%)

 Expectations of project impact (15.8%)

 Integrating findings into decisions (13.6%)

This has obvious consequences. Those who aren't satisfied in their roles leave.