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4 biggest misconceptions about hiring data scientists

Sarah K. White | April 12, 2016
You probably think you know what you need out of a data scientist. However, if you believe in any of these common misconceptions, it might be time to reevaluate your business' expectations for current and future data scientists.

And it's true that many data scientists at your company might have experience in development, you want them focused solely on data -- not only are they responsible for collecting that data and reiterating it to the right people, they also need to focus on building trust. "Asking anyone to take action based on data analysis means asking them to have trust in the analysis. And this is where presenting the data and the approach used to analyze the data becomes so important -- it's about building initial trust," Nejmeldeen says. If your data scientists are over burdened with not only analyzing the data but personally building and designing the systems to house it, they aren't going to have time to establish those relationships with the right decision-makers.

Make sure your data science team has the bandwidth to not only effectively manage data and build a strong analysis, but also the time to connect with relevant people in the organization. Technology has become an enormous part of modern business, and it would be detrimental to success if data scientists don't have time to present and explain data strategies.

One centralized team can handle every aspect of data

Bimodal IT is a new school of thought when it comes to IT management. On one side of bimodal IT is what you might consider the "more traditional" IT; it moves slower, the focus is on maintaining networks, hardware, software and security. On the other side, you'll find a more progressive IT, which focuses on innovation, adopting new technology quickly and staying ahead of the curve. It's a way to manage the enormous impact of technology on the overall business model by splitting it into two camps, each with a separate focus.

Nejmeldeen predicts the same future for data. "The term data scientist is often used collectively for anyone who touches data frequently. But in principle, it is helpful to think of one data science group as being the data architects or engineers that are collecting, transferring, organizing, and storing data for usage while the other data science group is heavy on problem-solvers that make use of the data."

Businesses should abandon the idea that they can have one or two dedicated data scientists mixed into the IT team responsible for handling all the data for the company. The bigger your organization, the more you will want to consider developing a data science team that is split into two different focuses. It might mean creating an entire department for data or figuring out how to put it under the umbrella of IT while still ensuring the data scientists have all the resources they need.

 

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