It’s been said that a data scientist sits at the nexus of statistics, computer science, and domain knowledge. Why would you want to add one more thing to your plate?
Successwise, you’re better off being good at two complementary skills than being excellent at one.
Scott Adams, author and creator of the Dilbert comics, offers the idea that “every skill you acquire doubles your odds of success.” He acknowledges this may be somewhat of an oversimplification — “obviously some skills are more valuable than others, and the twelfth skill you acquire might have less value than each of the first eleven” — but the point is that sometimes it’s better to go wide than to go deep.
Setting aside the relative magnitude of the benefit (because I seriously doubt it’s 2x per skill… thank you, law of diminishing marginal returns), it seems unquestionable that broadening your skillset can lead to more significant gains relative to toiling away at learning one specific skill. In a nutshell, this is why I think it’s important for a data scientist to learn data strategy.
Generally speaking, having diversity in your skillset allows you to:
- Problem solve more effectively by drawing on cross-disciplinary learnings
- Communicate better with your teammates from other specialties
- Get your foot in the door in terms of gaining access to new projects
Understanding data strategy transforms you from a data consumer into an empowered data advocate at your organization. Learn how the Certified Data Management Professional exam can deepen your appreciation for the end-to-end knowledge generating process.