John Lewis Partnership is a household name in the UK. It needed to unite its data silos to give its customer, trading, and operational teams timely and robust insight to drive informed strategic decisions. Find out why its Chief Data and Insight Officer chose Snowflake as its unifying data platform—and how it’s given the partnership greater control over its data.
John Lewis Partnership is made up of three widely known UK brands: John Lewis, Waitrose, and John Lewis Financial Services. It’s the UK’s largest employee-owned business with 80,000 partners working across its online division and brick-and-mortar stores. To operate successfully at scale, the partnership needs to make best use of its data. We spoke to its Chief Data and Insight Officer, Barry Panayi, to find out how it’s doing just that.
Unsurprisingly for a company with over 150 years of history, John Lewis was dealing with legacy infrastructure and disparate data sources which created an disconnected view of its operations. “We had data spread out—you could call that data silos,” said Panayi. “Ownership was difficult because we had replicas of the data everywhere, which meant we didn’t really know who to speak to about the different data sets. Governing it was overly onerous.”
A lack of data standardization from disconnected processes also posed a potential risk for John Lewis. “We wanted to eliminate those data silos, enable one version, and understand exactly who owns what, how to govern it, and what this data means,” added Panayi.
A stack-ready solution to secure and unify data
Together, the data and technology teams chose Snowflake for a number of reasons: “From my point of view, it’s the way it works with our other tools in the stack,” explained Panayi. “Since I’ve arrived, we’ve had a bit of a change, Snowflake included. So, we can link beautifully to Tableau, Python, dbt, and it all works really well. We don’t have to do anything that isn’t out of the box.”
Choosing Snowflake also benefited John Lewis’ data scientists. By splitting the platform’s compute and storage capabilities, the team now has access to all the data management tools they need without the risk of racking up huge costs.
In addition, Dynamic Data Masking ensures the safety of John Lewis’ data. “Dynamic Data Masking is super important because data security is absolutely paramount and enables us to work with a full range of data while keeping it safe,” explained Panayi.
And the John Lewis Partnership isn’t just using Snowflake to transform. By partnering with Deloitte as well, the company gets support in critical areas of its data and analytics program, including modernizing and migrating business critical data to the Partnership Data Platform. It’s also helping redesign the core reporting that drives John Lewis and Waitrose, and consolidating data from more than 100 source systems with a new enterprise data model that enables a wide range of other analytics and data science use cases.
This will help the Partnership maximize value from its data assets by bringing greater consistency and richness to reporting; empowering individual partners to self-serve the data they need, and enabling important and high-value initiatives across the Partnership.
Better insight into pricing, stock, and customer demand
John Lewis has put Snowflake’s Data Cloud at the heart of its data ecosystem—providing better controls and standardizing data analytics tooling internally and externally. “The Snowflake Data Cloud is massive for us,” said Panayi. “It enables us to make sure there’s only one version that multiple people can use.”
It’s vital for John Lewis to understand the demand for each product it stocks—including every variation, color, and size. This way, the retailer can sell at the right price while ensuring it keeps waste to a minimum to limit the cost burden of unsold stock. And it does all this through its steady stream of data through the Snowflake platform.
“We leverage our data in Snowflake in a number of ways, whether that be for a commercial, sustainability, or operational outcome,” said Panayi. “One example would be when we reduce the prices of some of our items. We want to ensure we reduce it at a price that’s attractive to our customers and to eliminate any waste. And being able to dynamically have these models running and making sure we’re giving the customer the thing they want—an attractive price and also delivering against some of the other metrics—is key.”
Data-driven collaboration that’s constantly improving
Riding on his success so far, Panayi explains John Lewis’ plans to make the most of Snowflake’s data sharing capabilities in the future to create better collaboration between its partners and suppliers: “We’re planning to use the Snowflake Data Cloud in the future once we’ve busted the silos and got our data in shape. It’s a tantalizing prospect, but we’re trying to focus on what we’re doing right now. But in the future I absolutely hope that we can start sharing using the Data Cloud.”