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How to Build a Big Data and Analytics Team

How to Build a Big Data and Analytics Team

As the data tsunami continues to surge and analytics tools become less expensive and easier to understand, big data is going mainstream. In fact, I would venture to say that businesses that aren’t considering their big data strategy and plans for the future are in very real danger of being left behind.

But what does that mean? Where does a business start to build a big data and analytics team? Here are some criteria to consider.

Hiring a great team doesn’t start with posting a job ad. It starts with the company taking a hard look at its goals and the talent it needs to achieve those goals.

As with anything surrounding data, the first step is to be clear on the questions that you want the data to answer and the challenges or goals you hope to address. No matter what size your business, don’t be afraid to start small and build your analytics as you go.

Start with the questions in mind and identify the key performance indicators that will allow you to accurately judge when the questions have been answered. Then – and only then – start considering which team members can help you answer the questions.

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The three main roles any big data team should include are:

Together, these three roles make up the basis of any good analytics team. Occasionally, you can find one person who can fill multiple roles, but this is often referred to as a “unicorn” because such people are so rare.

Once you have defined the goals of your big data project and decided which roles you need to fill, the next step is filling them.

And talent shortages are a critical issue. By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills, according to the McKinsey Global Institute.

Companies are looking outside the box for new talent, recruiting people from fields as varied as physics and mathematics to language arts. Walmart even turned to crowdsourcing to fill its big data needs with a contest to seek out talent.

Depending on the roles you choose to fill for your organization, consider these questions when interviewing and hiring:

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Does the candidate have solid programming skills? A data scientist needs the skills not only to view and analyze the data, but also to manipulate it. A statistician who reviews and interprets a set of data is very different from a data scientist, who can change the code that collects the data in the first place.

Does the candidate excel at producing analytics for computers or humans? (And which do you need?) There are two main types of big data analysts: those whose end user is solely a computer, and those whose end user is a computer. If your end result is a machine-learning algorithm to, for example, choose which ads to show on a website or make automatic stock trades, your analytics are for computers. If, on the other hand, a human will make a choice based on the analytics, your analyst needs a different set of skills – chiefly, being able to tell a story through data and providing good visualization of that data.;

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Data Science Congress 2017

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Data Science Congress 2017

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AI Paris

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Chief Data Officer Summit San Francisco

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Chief Data Officer Summit San Francisco

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Customer Analytics Innovation Summit Chicago

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Customer Analytics Innovation Summit Chicago

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Big Data and Analytics Marketing Summit London

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Big Data and Analytics Marketing Summit London

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