Download Report

Thank you! Your copy of the report opened in a new tab. If you have trouble viewing it,click here.

Your personal information is kept in accordance with our Privacy Notice.

Building a data science team for your business


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.


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].

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

Keep Reading...Show less

Memories of panic buying may be fading here in the UK but have resurfaced elsewhere1. The near constant threat of another wave of Covid-19 may yet prompt another round of hyper demand. Whilst there is little hard evidence to determine the underlying drivers of panic buying2, there are numerous theories that the retail industry may benefit from exploring.

Feroud Seeparsand, dunnhumby's Senior Consumer Psychologist, outlines some likely theories to explain the 'why' behind the 'panic buy' and some implications for retailers to prevent it reoccurring in future.

Keep Reading...Show less

The dunnhumby Consumer Pulse Survey is a multi-phased, worldwide study of the impact of COVID-19 on customer attitudes and behavior. We surveyed more than 27,000 respondents online in 22 countries, with interviews conducted for Wave one from March 29 – April 1, for Wave two from April 11 – 14, and for Wave three from May 27 – 31. Due to the rapidly unfolding crisis in North America, dunnhumby conducted Wave four from July 9 – 12 in the U.S., Canada and Mexico only. Here are highlights from the study:

Keep Reading...Show less

In a series of posts published earlier this year, we covered the results of the dunnhumby Customer Pulse – a global study designed to explore changing consumer mindsets during the COVID-19 pandemic. Over three waves, conducted between March and the end of May, we polled thousands of people from more than 20 countries on subjects including supermarkets' responses to the outbreak, the economic outlook, and how their shopping behaviour had changed due to COVID.

At the beginning of September – three months on from the previous wave and with supply chains stable and the changing nature of lockdowns – we wanted to revisit the Customer Pulse to see what, if anything, had changed. Below are some of the standout findings from this fourth tranche of research.

Keep Reading...Show less

assorted fruits at the market

Photo by ja ma on Unsplash

In the decade since Richard Thaler and Cass Sunstein's Nudge: Improving Decisions About Health, Wealth and Happiness was published, nudge theory has enjoyed unprecedented success.

Predicated on the idea that individuals respond better to indirect suggestion than outright commands, nudge theory is commonly used as a way of subtly influencing our behaviour towards positive choices. The idea has gained such traction, in fact, that many governments around the world have created "nudge units" in a bid to tackle thorny issues like obesity and the climate emergency.

Keep Reading...Show less

Are you looking to increase your contactable Customer base? How much money are you losing on incorrectly identified Customer communications? Throughout our 30 years of big data experience working with clients across industries around the globe, we have found that maintaining contact through relevant Customer engagement is a crucial component of putting the Customer First.

Essential to preserving contact data is ensuring that you have the most up-to-date information from your Customers; not an easy task. On average, people in the United States will move an average of 12 times in their lifetime. United States Postal Service data indicates 14% of the population change addresses annually. As email contact has grown, it's important to note that, on average, 30% of people change their email addresses each year. This is driven by ISP or job changes, or just to stop being spammed. As people move away from home phones to primarily mobile devices, phone numbers are stabilizing as consumers maintain the same numbers through physical moves.

Keep Reading...Show less

It's a well-worn phrase by now, but it's true that the COVID-19 crisis has drastically altered the global retail landscape. Here in the Asia-Pacific region, a majority of markets are now looking past the panic of the first wave and towards the future. In this series of articles, we'll explore how grocery retailers must adapt to a more omnichannel reality to thrive in a post-pandemic world.

Keep Reading...Show less


Smarter operations and sustainable growth, powered by Customer Data Science.


Better understand and activate your Shoppers to grow sales.

Retail leaders must objectively understand how their business currently considers Customers before trying to set a more Customer-centric direction and focus. There are some formal assessment methodologies, like dunnhumby's Retail Preference Index (RPI) and Customer Centricity Assessment (CCA), which offer detailed evaluations of a business' capabilities, strengths and weaknesses based on Customer perceptions (RPI) or global best practices (CCA).

The approach outlined below is not intended to replace these formal tools; rather, these observations are intended as a kind of 'toe in the water' to help retail leaders form early hypotheses and points of views. These are rules of thumb, heuristics culled from global experience. Later, leaders might use these observations to informally check progress from time to time as a way of assessing whether the "program in the stores matches the program in our heads".

Keep Reading...Show less

In the first episode of Customer First Radio, Dave Clements, Global Head of Retail for dunnhumbyand David Ciancio, Global Head of Grocery for dunnhumby kick off the series by discussing what it means to be a truly Customer First business, share which retailers and brands today embody a Customer First mindset, and examine how Customer First materialized during the pandemic with retailers.