Callum Staff: Building on Pandemic-Era Data Science Successes at M&S

Callum Staff, Head of Data Science and Analytics at Marks &

Spencer, discusses his plan to build on his team’s successes

over the past 24 months and integrate data further with the

retailer’s business processes

At UK retailer Marks & Spencer, most conversations in the past two years

involving data and analytics have revolved around COVID-19.

For Callum Staff, Head of Data Science and Analytics at Marks & Spencer,

the global crisis meant putting major data science projects on ice to better

support the retailer’s supply chain. He considers the move ‘a real shift’ in the

relationship between business and data science at M&S.

“It showed the sort of support that we can provide; I want to further embed

the data and analytics team in key M&S functions,” he says. “We've got

tools that allow us to do that, and we want to turn them into fully fledged

software. It will help bake my operating model into any project.”

In this week’s Business of Data    podcast   , Staff talks about how he’s working

to build on the ties the pandemic-era has helped his team build with

business stakeholders to integrate data and analytics further into the

company’s operating model.

Making M&S Staff Sweet on Data

One of the ways Staff’s team is already helping to optimize M&S’s supply

chain is through space planning in stores. They provide insights to improve

demand forecasting and optimize inventory levels.

The data science team has also been involved in planning for a new law

regulating the sale of fatty, sugary and salty foods that will come into effect

in the UK this April. It will mean retailers can no longer place these foods at

store entrances and other main points. Advertising these products on TV

and online will also be restricted.

“Our data quality team's been involved in the conversations around this new

law from the start,” Staff says.

But Staff believes he must be mindful of his approach as he works towards

greater integration between data science and business teams. He wants to

ensure that the models and systems his team builds meet business needs

and integrate with staff workflows.

“It's all very well building a new dashboard or building a new machine

learning model,” he says. “But if you just plonk it somewhere and leave it,

it's never going to grow. We’re spending a lot of time thinking about how this

works day-to-day with people and where it impacts the steps they need to

take.”

Success will require constant communication and re-evaluation to ensure

Staff’s team’s priorities remain aligned with those of the wider business. But

Staff is confident that the greater cross-functional collaboration he’s seen

recently will help his team drive further organizational change in the coming

months and years.

“It’s really good to have the quants and the tech teams working alongside

sales and the traders,” he concludes. “It seems like a really good way of

working. COVID-19 showed me that, and hopefully it showed that to other

areas of the businesses as well.”

Key Takeaways

The pandemic has changed perspectives around data. COVID-19

helped Staff and his team show their colleagues how data science can

embed itself within the company and add value

Provide consultation on key business initiatives. M&S’s data team is

playing a strategic role as the organization reformulates thousands of

products to meet the UK’s new dietary regulations

Focus on alignment and adoption. Staff is keen to ensure his team is

developing the right tools and capabilities and that these can be integrated

effectively with staff workflows