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With vast amounts of data now being generated by every business process and customer touch point, companies in almost every industry are focussed on exploiting data for competitive advantage. This has led to data science becoming a 'must have' capability, and data science teams being rapidly formed in businesses of all sizes, in all sectors.

Having spent nearly 20 years working in, setting up or running teams designed to leverage this competitive advantage, I wanted to share my observations on what it takes to build a great data science team. The starting point is a simple 8-step framework that ensures you begin with the best foundation for success.


Step 1 – Recruit a diverse set of skills

We all know that the first step to building a team is to hire some people, but what skillsets are required to build a high-performing data science team? It's not just about recruiting a bunch of bright PhD students. Make sure you hire people who can understand your business, construct a problem statement and turn analytics into insights. These traits are as important as being able to use mathematical models to create the next algorithm.

Step 2 – Generate insights in an agile way

Analysts and data scientists don't always like agile. They like logical flows, assembling all data before building features and before building models. But businesses often don't have the luxury of time for this approach, wanting to see results quickly as they make day-to-day decisions. You need to strike a balance, be flexible, responsive and adaptable by working in an agile way, adding more data and features as you progress, and building confidence early.

Step 3 – Drive accountability through measurement

It's a tricky thing to do without results to benchmark, but you must estimate the value that your scientific models will add to the business if implemented. Even before you start, you should use your business knowledge to make assumptions about the outcomes. After implementation, measure the value and communicate this to stakeholders. We're all impatient to move onto the next thing, but the best ammunition for arguing for more resource, budget or time is demonstrating the value that your data science team can deliver.

Step 4 – Define the strategy

Be clear on your strategy from the get-go to ensure you don't become order takers for the business on tactical initiatives. Be proactive about where you believe data and models can drive an improvement. Mix quick wins with longer term development to ensure the business doesn't get bored waiting for results.

Step 5 – Integrate into the business

Don't sit in a fancy office in a city centre location because you think it's the only way to attract data scientists. Integrate your team into the business, build commercial understanding by being as close to the other departments as possible.

Step 6 – Communicate your results as well as your existence

Make sure the business knows you are there, what your remit is, and how it will benefit the business overall. Infiltrate as many parts of the organisation as you can and share insights widely, encouraging re-use. Communicate as widely and regularly as you can, demonstrating how and where data science is improving business processes, growing sales, helping win new customers, creating efficiencies.

Step 7 – Influence by building trust

It's natural that people will be sceptical of your team in the beginning, as many decisions are made on experience and gut instincts, not founded in data. Start small to build trust with your stakeholders, find those that are more open to the data-driven approach, and use your results in these areas to influence more widely.

Step 8 – Reuse and build on the best

Build a knowledge bank that ensures that insights and science can be researched and reused. Encourage teams to start with what is already known and develop a champion / challenger approach to model building. Bring the outside in, encourage curiosity and learn from others, both within the business and the wider data science community.

Conclusion

With data scientist now feted as one of the most sought-after roles in business, and investment in building this capability being ramped up across the board, it's clear that if your business is not already making moves in this direction, there's a danger you'll be left trailing your competitors. But do it well, and do it right, and you'll set your fledgling data science team up for the best possible success.

Want to leverage real value from your data asset? We can help - click here to find out more.

[This is the fourth in a series of articles advocating the voice of the Customer in the highly competitive food-retail industry. David Ciancio is Global Customer Strategist for dunnhumby, a pioneer in Customer data science, serving the world's most Customer-centric brands in a number of industries, including retail. David has 48 years experience in retail, 25 of which were in Store Management. He can be reached at David.Ciancio@dunnhumby.com].

Treating Customers differently based on their 'profitability' is counter-productive to building loyalty and toward creating a healthy retail Customer Experience.


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