How data science is transforming business
Tesco data scientists reveal how harnessing the power of data can have an impact across every area of a business
Over the last few years, data has become one of the most valuable commodities for any business, driving decisions, powering new models and producing insights that can increase a company’s revenue or save it millions of pounds. Yet data alone can’t deliver real-world value – it’s only when it’s applied to projects and systems that these insights emerge. It’s data scientists that put in the research, thinking, planning and hard graft that transform data into tangible results.
To do so, data scientists harness their technical and analytical expertise, planning and developing data-driven projects that meet existing or future business needs. They help define any data architectures, models and operations research techniques involved in making those projects work effectively, and train their machine learning systems to spot patterns, forecast probable scenarios and predict results. They also work closely with teams across the organisation to spot new opportunities for data projects and ensure that the resulting insights become integrated into processes and workflows.
A data-driven business
At Tesco, data-driven projects impact every area of the business, helping it serve customers better. These projects influence everything from how much of a product to stock in a specific store to how online grocery orders are packed onto different vans. Data scientists will be involved in automated processes that recommend products to an online customer, based on sales data and their website interactions, but also in the way products are placed around the floorspace of a Tesco store. “We’re kind of a centralised team that works across different bits of the business,” says Stuart Barrow, head of data science at Tesco, “which means we try to improve how Tesco operates in different ways across different aspects.”
To do so, Barrow’s team works with a variety of rich, anonymised data sources, including sales data, data on purchasing behaviour, data from Tesco’s Clubcard scheme and data relating to the products Tesco sells. The data sources change according to the area of the business and the challenge, so that an online project might focus more on customer interactions with the websites and previous purchase history, while a project aimed at optimising supply chains will focus on historical sales data and how products flow in and out of Tesco stores.
Data scientists aren’t usually responsible for filtering and preparing data, or for coding, integrating and deploying the platforms that do the work, though they may be involved and work alongside development teams. Instead, their core responsibilities focus more on the data and on how it’s processed through analytics and machine learning.
“Often our work comes down to trying to predict or forecast something,” Barrow explains, “which means we will use that data in order to train machine learning models. A simple example might be forecasting sales in a store over the next couple of weeks. We’ll use historic sales data and other sources of information that we have in order to forecast the amount of products that will be sold, and that can then be used to inform how many products we bring into the store, and all the things that are interlinked with that on the supply chain side.”
Yet the impact of the data science team percolates into so many other areas. For instance, when a customer starts an order, a predictive tool goes to work to figure out how much the order might weigh and a scheduling optimisation tool decides which van it should go out on, even if the order might not be finished on that day. Meanwhile, the colleagues who picks the products for that order will be following a data-driven route around the store, defined by modelling its layout, the products stocked and the shopping behaviour of online customers.
Here small changes build up to have a real impact. When assembling an order, about 50% of a store staff member’s time is spent moving between each of the customer’s chosen items, so providing the optimal route saves them time and helps them not only ensure that the online customer gets the right products but also frees them up to help customers shopping in the store. Similarly, optimising the route of delivery vehicles saves time and fuel costs, while – at scale – helping reduce Tesco’s environmental impact.
Requirements for the role
Becoming a data scientist requires a high level of technical and mathematical expertise. At Tesco, the team looks for a good background in mathematics, physics or related subjects, plus a specialism in an area like machine learning or operational research. Experience of coding is a must-have, along with experience in open-source big data technologies such as Apache Hadoop or Spark. Beyond this, though, data scientists need flexibility and the ability to collaborate. “We can never deliver anything of real value on our own,” says Barrow, “it’s always as part of a bigger project, so we’ll often work closely within that team.” Project managers, data analysts and wider stakeholders within the business, including those within marketing and retail teams, will all be involved, and data scientists need to understand their specific wants and needs.
In turn, this enables data scientists to see new opportunities, or how solving one problem might be the first step in transforming something wider. “When working with my colleagues, it’s also occasionally an education piece when you do spot an opportunity they haven’t seen,” adds Ben White, a data scientist for Tesco. “Because we’re the data scientists, we know how the models operate and where they’re going to work well, and for which problems.”
Data science is a fast-moving field, which can make keeping up a challenge. “We need to make sure that we’re not missing opportunities to provide further value by making sure that we’re up to date with new techniques,” says White. Yet it’s this that keeps data science exciting, particularly in a company where you can work across so many areas and explore specific problems in real depth. What’s more, Tesco encourages its data scientists to spend Friday afternoons researching and training, to keep the team on the cutting edge. With a team of data scientists from different disciplines and backgrounds, there’s always opportunity to learn, upskill and share ideas. This can all be done remotely and around flexible working.
Working as a data scientist for a company of Tesco’s scale can be daunting. “It’s sometimes challenging to align the different parts and it’s a very big business,” Stuart Barrow notes. Yet it’s also hugely rewarding. “The most satisfying thing is when you get something out of the door and deliver value,” he says, particularly when you can measure the performance and see the tangible benefit to millions of customers. “When it works, the benefits are massive.”
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