Put yourself in the shoes of an executive at a big legacy company for a moment, operating in virtually any market vertical: you’re constantly hearing that data science is revolutionizing the world and the firms that survive and thrive in the coming years are those that execute on a data strategy. What does this mean for your company? How can you best guide your established firm through a successful transition to becoming data-driven? How do you balance the momentum your firm has right now, and the need to support all your current products, customers and operations, against a new and relatively unknown future?

If you’re working as a data scientist at a mature and well-established company, these are the worries on the mind of your boss’s boss’s boss. The worries on your mind may be similar: you’re trying to understand where your work fits into the bigger picture, you need to break down silos, you’re often running into cultural headwinds created by colleagues who don’t understand or trust your work. Congratulations, you’re in the midst of a classic set of challenges encountered by innovation initiatives everywhere. Harvard Business School professor Clayton Christensen wrote a classic business book (The Innovator’s Dilemma) explaining the paradox of trying to innovate in established companies, and why the structure and incentives of those companies almost guarantee an uphill climb to innovate. This week’s episode breaks down the innovator’s dilemma argument, and what it means for data scientists working in mature companies trying to become more data-centric.

Thinking of data science initiatives as innovation initiatives

Linear Digressions