How to Build Successful Big Data and Data Science Teams

by   |   September 14, 2016 5:30 am   |   0 Comments

Bernard Marr

Bernard Marr

Hiring for big data and data science teams continues to be difficult, as the gap between the supply of qualified, experienced data scientists and the demand for them remains large.

To build a strong, successful big data and data science team, organizations should focus less on finding candidates who
are “perfect” on paper and direct their efforts more toward putting together a group that is greater than the mere sum of its parts.

The key is to create the full skills mix among team members, not necessarily within each individual.

The three main roles that any big data or data science team should include are as follows:


    • Business analyst. This role existed long before big data, and people in this role continue to perform an important function. They have intimate knowledge of your industry and your company, and analyze business-level data to produce actionable insights.


    • Data scientist / Machine-learning expert. This person is statistically minded, with experience in programming and building data models. They develop algorithms and crunch numbers in order to help answer questions and make predictions with data.


  • Data engineer. The data engineer is concerned with the capture, storage, and processing of the data itself.


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 people who can fill multiple roles in this manner are often referred to as “unicorns,” because such candidates are so rare.

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Depending on the roles you choose to fill for your organization, it is essential that you to create a mix of the following skills among your team members:

    • Programming skills. A data scientist needs the skills not only to view and analyze the data, but also to manipulate it as well.  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.


    • Visualization and reporting skills. There are two main types of big data analytics: those whose end user is solely a computer, and those whose end user is a person. If your end result is a machine-learning algorithm that will, 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 decision based on the analytics, your analyst needs a different set of skills – chiefly, being able to tell a story through data and to provide good visualization of that data.


    • Business skills. It’s an under-appreciated reality that effective data scientists also need to be familiar with the particular business niche that they study.  Someone on your big data team needs to understand your particular industry in order to be able to create and answer the pertinent questions that you need answered. It is important to understand how that data affects profitability, user experience, and employee retention, or any of a myriad of other factors important to the business. Someone with a background in business will be better at spotting trends that will benefit your business.


    • Communication skills. Having poor communications skills is not an option for a data scientist. He or she needs to be able to communicate effectively with people who don’t “speak the same language.” Data scientists must be able to tell a story through data and use visual communications effectively.


  • Creativity. Despite the stereotype of data being all about numbers and statistics, big data is a rapidly changing and expanding field that requires a certain open-mindedness and creativity. To innovate, a good data scientist must be able to look beyond what came before and explore new ideas.


eBook: Big Data Analytics: Advice from Big Data Guru Bernard Marr


Of course, the same qualities that will make any team work well also apply to a data science team.  But having a good balance of the aforementioned skills among your big data team members will ensure that your team members are not only successful working together, but also successful at using data to get answers that are vital to your business.

Bernard Marr is a bestselling author, keynote speaker, strategic performance consultant, and analytics, KPI, and big data guru. In addition, he is a member of the Data Informed Board of Advisers. He helps companies to better manage, measure, report, and analyze performance. His leading-edge work with major companies, organizations, and governments across the globe makes him an acclaimed and award-winning keynote speaker, researcher, consultant, and teacher.

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