Three tips for building a data scientist team

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Maths GeniusIf I were to believe the feedback I get, statisticians are among the most difficult people to work with. What’s more, they’re the only group that should be allowed to work in data analytics. It sounds harsh, but this may explain why big data projects continually fail.

Businesses need statisticians who are both easy to work with and can take the conversation beyond math and statistics to actual business solutions. Because, not surprisingly, conversations based primarily on maths and statistics do not solve business problems -- far from it.

Businesses need to overcome the perception that data science is about feeding data into an engine and analyzing statistics to get answers. Answers require a logical mind as well as a creative one. And the starting point should be: What are the business challenges we need to solve? The statistics bit comes later and is just part of the process in getting to your business solution.

Data science = problem solving
Problem solving is a cognitive function that relies heavily on the creative side of our brains. Humans are curious beings. It’s our nature to want to solve mysteries and understand the world around us. It’s a rewarding experience that creates a strong motivation for people to want to do more.

Business leaders can tap into this behaviour by giving employees more interesting problems to solve. Data science provides the opportunity to satisfy someone’s curiosity, and that problem solving doesn’t have to be at an individual level. After all, data science is about team work. In my last blog, I explored the different roles on a data science team.

Every data analytics project is unique, so every project will have a unique team set-up. Our education system now provides the opportunity for us to nurture new talents that meet what’s required for the business manager, business analyst, data management expert, and statistical modeler. Adding together different geniuses is the key to business value.

Look for geniuses across the educational spectrum
Data science is seen as a technical field, but that’s only a small part of the job; data scientists are also business consultants and creatives. That's why we need to recruit talent from all disciplines, from arts and humanities to STEM subjects.

Businesses need to be more open-minded in their approach to data science. Hiring only statisticians is probably the worst thing a business can do.

Changing your approach won’t happen overnight, but when building a data science team:

  1. Look inside your organisation to assess what skills and interests are already there.
  2. Once you've identified your candidates, provide them with training courses and help them carve out a clearly defined learning pathway to develop their role within the business.
  3. Then explore the wider circles in universities and other industries to hire new talents that supplement your existing areas of expertise.

For more insight into what makes a great data scientist, see what we found out when we asked those in the industry.

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About Author

Dr. Laurie Miles

Director, Global Cloud Analytics

Laurie Miles is a Global Director of Cloud Analytics, providing analytical advice and thought leadership globally across all industry verticals. He brings over 25 years of real-world analytics experience to the role. After joining SAS in 1996, Laurie was a consultant delivering analysis focussed projects to organisations from a variety of industry sectors including financial services, telecommunications, retail and utilities. He became SAS UK’s Head of Retail Banking Technology in 2000. Laurie was later appointed Head of Analytics for SAS UK & Ireland in 2008, working with some of the UK’s largest organisations providing strategic advice and forming industry best practice. In this role Laurie also pioneered the development of the SAS Analytics-as-a-Service solution, “SAS Results”. In January 2015 was appointed to lead this globally as part of the SAS Cloud Analytics proposition. Laurie holds a BSc in Econometrics, an MSc in Game Theory and a PhD in Number Theory.

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