Sophie Gallay, global data and client IT director at Etam, has been busy since joining the French retailer at the start of 2023. From establishing the foundations for data-led change to investigating the potential of artificial intelligence (AI), Gallay grabbed the opportunity to join a business exploring information and insight.
“There was pretty much everything to do,” she says. “We were starting from quite a low point versus what you might expect from a retailer. The teams and the executive committee were expecting a transformation. So, I felt that this was an ideal setup.”
Gallay has consulting experience with big-name companies, including Accenture, and was previously the digital and data division lead at luxury retail conglomerate LVMH, which owns world-famous brands such as Louis Vuitton, Bulgari and Dior. She wanted to hone her data leadership skills in another retail organisation like Etam. “I loved how I could apply what I learned at LVMH and the consulting industry to a smaller, French family-owned group,” she says.
In addition, Gallay knew she was joining a business eager to make the most of data. “There is a strong direction,” she says. “People at the top want to push this transformation and the teams are also looking for change. You have everything to do, everything to build, and lots to think about to make things better.”
Making progress
Gallay relishes the opportunities her role at Etam brings, but recognises the position comes with challenges. Industry issues and wider macroeconomic concerns mean the company has to work hard to stay successful.
“It’s a challenge because the market is not easy right now,” she says. “There is high inflation and it’s hard for all the ready-to-wear brands. We’re being challenged by international players that do not have the same constraints and can offer low prices.”
Gallay and her executive colleagues must expose new market opportunities – and that’s where data comes in. After joining Etam in February 2023, she began the first 18-month phase of her data strategy. This first phase will run until the end of 2024 and focuses on implementing a Snowflake data platform. Gallay’s team is already taking big strides forward.
“We’ve made great progress when you consider we’ve dealt with the RFP, the selection of Snowflake, and the implementation process in less than a year”
Sophie Gallay, Etam
“It’s a success because we’ve made significant progress in less than a year,” she says. “We’ve written a data strategy and a roadmap. We have the data foundations prepared. We’ve made great progress when you consider we’ve dealt with the RFP [request for proposal], the selection of Snowflake, and the implementation process in less than a year.”
Once the data foundations are ready, Gallay and her team will exploit business intelligence (BI), optimise performance and explore AI. The hype around emerging technology means data chiefs face pressure to embrace digital transformation. However, she recognises it’s important that digital leaders don’t over-promise and under-deliver.
“We have a few projects already mapped out, so we can deliver value from this year on,” she says. “We could have more resources and we could go faster. But if we compare what we have now to what we had before, everyone is excited and looking forward to the value we’ll create.”
Implementing a platform
Gallay says Snowflake gives Etam foundations for a group-wide data strategy for architecture, tooling and governance. She chose to implement Snowflake rather than build a bespoke system because its cloud-based platform supports the business’s transformation plans.
“There was no real question about the necessity of having a group data platform. We had a legacy platform that wasn’t made to scale analytics. There was no question at a senior level about the necessity of this platform. The real question was, ‘What direction are we going to follow?’,” she says.
“We considered whether we should build a custom platform and leverage a cloud provider directly, or choose a packaged approach and one platform that provides pretty much everything we need and that leverages cloud capabilities. So, it wasn’t a case of Snowflake versus another actor. There were two options that we considered – and it was super-clear because Snowflake was the only representative of the packaged data platform approach.”
Etam continues to refine its use of the platform. Gallay says the long-term aim might be for the company to use Snowflake as a hub to simplify data flows across the business. For now, she’s focused on making the most of the technology.
“We have a few considerations for the future,” she says. “Snowflake has lots of things coming. Depending on the roadmap, we could leverage even more of their technology for our CRM [customer relationship management] and transactional data. But Snowflake is at the centre of our data organisation and technology stack. I’m sure we can increase the scope.”
Delivering intelligence
Gallay recognises Etam is eager to exploit its data resources. While her first year in situ has focused on foundations, she’s keen to move to the next stage, which she refers to as the most important part of the data strategy, and which includes three sub-streams, the first being BI.
BI is less sexy than AI and generative AI, but essential Sophie Gallay, Etam
“It’s less sexy than AI and generative AI, but essential,” says Gallay, reflecting on the hype around emerging technology. “The key to success with data is giving line-of-business teams the ability to monitor the business correctly. People tend to mix the two things up. Just because you have lots of data doesn’t mean you necessarily monitor your business correctly.”
Gallay’s team is building an integrated data stack. Etam runs Snowflake on Amazon Web Services. The company’s stack includes Oracle CRM, SAP enterprise resource planning (ERP), a range of Salesforce’s cloud-based tools and Tableau’s BI technology. Her team is running projects to prove the value of its data stack, including a company-wide BI dashboard for a 360-degree view of customer trends.
“What we’ve been doing for the past eight months – and we have a two-year roadmap in front of us – is to work with the business teams to understand the key performance indicators that they need to monitor and to build a platform at scale within our BI tool,” she says.
“We are working with all the divisions, all the functions and all the brands, so you can imagine it’s a lot of work. We will start fresh on a new dashboard on the Tableau cloud and then progressively remove all the legacy infrastructure and tools. We’re starting fresh on BI as part of our data transformation. It’s a huge piece of work.”
Optimising performance
The second sub-stream of Gallay’s long-term data strategy centres on performance optimisation. “This is a two- to three-year roadmap and we have many use cases,” she says.
Gallay is looking at projects to optimise each step of the supply chain. As well as using data science to boost forecasts for store replenishment, her team is looking at projects for sales forecasts, demand forecasts, and new ways to help Etam buyers get the right quantities of products that meet customer requirements.
“I have this long list of use cases. I could list a dozen use cases on the operations side and we have the same on the marketing side. So, rebuilding our segmentations, client scoring and product recommendations – all these things were not being done internally,” she says, explaining how her team will deal with performance optimisation challenges.
“The choice we have to make during the next three years is what parts to develop internally and what parts to externalise to partners. I will never have enough resources to build and internalise everything, so I have to consider the rationale for what parts I need to keep in-house and what parts I can externalise.”
Gallay will take a similarly considered approach to explorations into AI, the third sub-stream of her data strategy. While it’s still early days for AI and generative AI (GenAI), she says Etam must develop a roadmap for emerging technology – and Gallay is pursuing a few use cases carefully.
“What I’m trying to do internally is to avoid the excitement of business teams who could think, quite easily, that generative AI makes everything super-easy and we don’t need BI and data science anymore,” she says. “Obviously, that’s not the case. So, what I’m trying to do in this roadmap is go back to basics and show where generative AI could have an impact.”
Boosting productivity
Gallay faces a similar challenge to other data and digital leaders – how to move at the right pace into AI and do so without leaving the business feeling like its demands are being ignored. She intends to move forward with caution.
“We’re not going to rewrite our roadmap for generative AI. But I’m sure we have lots of value to find, specifically around use cases for customer service and IT support. We will test a few things in the year to come between summer 2024 and summer 2025. Then we’ll maybe scale a few projects. But we’re not putting ourselves under too much pressure and giving ourselves objectives, like in the case of the first two sub-streams of the strategy.”
Gallay explains how those explorations into AI might pan out. While there could be strong use cases for e-commerce and marketing teams, she expects the first use cases to focus on boosting the productivity and efficiency of customer and IT support teams. She explains how GenAI could help to support Etam’s technology professionals.
“One challenge for us is that we often have the same people dealing with IT transformations, managing legacy systems and supporting business teams. When you have one person doing all that work, the time we can place on transformation is reduced,” she says.
“I want to reduce the support parts from their calendar, which can be up to 30% of their week, and replace it with time spent building new projects. I’m pretty sure that fuelling a generative AI conversational agent with our documentation could, with a basic user interface, mean that we could save hours within the IT teams.”