If 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 not only that, this may explain why big data projects continually fail to launch successfully in so many businesses.
What businesses actually need is statisticians that are easy to work with, because conversations based primarily on maths and statistics do not solve business problems. Far from it, in fact.
Businesses need to overcome the perception that data science – despite the lexicon – is about feeding data into the engine and analysing the statistics to get answers. It requires a logical as well as creative mind, for it to deliver real value. And the starting point should really be ‘what are the business challenges we need answering?’ The statistics bit comes later and is just part of the process in getting to your business solution.
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, whether they are a genius or not.
That problem solving doesn’t have to be at an individual level either. After all, data science is about team work. In my last blog, I explored the different roles in building 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 modeller. Adding together different geniuses is the key to business value.
Young people now have so much more choice over what they study at school and university. ‘Data scientist’ is seen as a technical role but it’s only a small part of the job: they are also business consultants and creatives. This is why we need to recruit talent from all disciplines, from arts and humanities to STEM subjects.
Businesses need to be 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, first look inside your organisation to assess what skills and interests are already there. Once you have 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. 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, check out what we found out when we asked those in the industry
Laurie Miles, Head of Analytics, SAS UK & Ireland
Image source: Shutterstock/McIek