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What You Need to Know Before Hiring a Data Scientist

// by David Maman//

The classic path to knowledge is this:

  • You don’t know what you don’t know – so you cannot ask the right questions
  • You know what you don’t know – so you can ask the right questions.
  • You know – you received answers to the right questions.

What’s your plan for getting your organization on this path? Spending a $250K or more to hire a qualified data scientist isn’t going to do it. Don’t get me wrong, data scientists have a place in the food chain, but a good business strategist or analyst is critical to framing the key questions necessary to devise the strategies to drive your company forward – the questions and goals the data scientists need first before they can perform any deep, big-data analysis.

The big difference between a data scientist and a business strategist is that the data scientist can only help you after you’ve formulated the right questions. You’re better off hiring two lower-level analysts for the price of a single data scientist because these are the people who can move you from “you don’t know what you don’t know” to “you know what you don’t know.”

Building a comprehensive strategy should be your first step before you consider even a single item to analyze. You need to determine where your business is today and where you want it to be going. Are there new markets to explore? What new products, services, or offerings do you plan to develop over the next 12 months? How is the competitive market going to affect your business? You want to increase alignment by creating common KPIs. You want to be able to create single processes across multiple departments. You want to accelerate product delivery.

Is your data scientist going to be able to answer these questions? No. Will your business analysts and strategists? Absolutely.

The data scientist won’t really be able to help like that. They are the experts at manipulating the millions if not billions or trillions of data records in your long-term storage that you would love to extract value from. They’ll be able to crunch the numbers, but they cannot help you formulate the right questions or apply the answers to final implementation within the business.

Instead of putting the cart before the horse and spending millions on custom-created, in-house research and massive hiring, build your strategy first, not your data science infrastructure.