Sophie Gallay, global data and client IT director at Etam, has been busy since becoming a member of the French retailer at the beginning of 2023. From establishing the foundations for data-led change to investigating the potential of synthetic intelligence (AI), Gallay grabbed the chance to affix a enterprise exploring data and perception.
“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 expertise with big-name corporations, together with Accenture, and was beforehand the digital and data division lead at luxurious retail conglomerate LVMH, which owns world-famous manufacturers resembling Louis Vuitton, Bulgari and Dior. She wished to hone her data management abilities in one other 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 becoming a member of a enterprise desperate to take advantage 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 alternatives her position at Etam brings, however recognises the place comes with challenges. Industry points and wider macroeconomic considerations imply the corporate has to work exhausting to remain profitable.
“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 govt colleagues should expose new market alternatives – and that’s the place data is available in. After becoming a member of Etam in February 2023, she started the primary 18-month section of her data technique. This first section will run till the tip of 2024 and focuses on implementing a Snowflake data platform. Gallay’s workforce is already taking huge strides ahead.
“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 prepared, Gallay and her workforce will exploit enterprise intelligence (BI), optimise efficiency and discover AI. The hype round rising know-how means data chiefs face strain to embrace digital transformation. However, she recognises it’s essential 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 offers Etam foundations for a group-wide data technique for structure, tooling and governance. She selected to implement Snowflake reasonably than construct a bespoke system as a result of its cloud-based platform helps the enterprise’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 goal is perhaps for the corporate to make use of Snowflake as a hub to simplify data flows throughout the enterprise. For now, she’s centered on taking advantage of the know-how.
“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 raring to use its data assets. While her first yr in situ has centered on foundations, she’s eager to maneuver to the subsequent stage, which she refers to as an important a part of the data technique, and which incorporates three sub-streams, the primary being BI.
BI is much less horny than AI and generative AI, however important Sophie Gallay, Etam
“It’s less sexy than AI and generative AI, but essential,” says Gallay, reflecting on the hype round rising know-how. “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 workforce is constructing an built-in data stack. Etam runs Snowflake on Amazon Web Services. The firm’s stack contains Oracle CRM, SAP enterprise useful resource planning (ERP), a spread of Salesforce’s cloud-based instruments and Tableau’s BI know-how. Her workforce is operating tasks to show the worth of its data stack, together with a company-wide BI dashboard for a 360-degree view of buyer developments.
“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 efficiency
The second sub-stream of Gallay’s long-term data technique centres on efficiency optimisation. “This is a two- to three-year roadmap and we have many use cases,” she says.
Gallay is tasks to optimise every step of the provision chain. As effectively as utilizing data science to spice up forecasts for retailer replenishment, her workforce is tasks for gross sales forecasts, demand forecasts, and new methods to assist Etam consumers get the correct portions of merchandise that meet buyer necessities.
“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 workforce will take care of efficiency 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 equally thought of method to explorations into AI, the third sub-stream of her data technique. While it’s nonetheless early days for AI and generative AI (GenAI), she says Etam should develop a roadmap for rising know-how – and Gallay is pursuing a number of use circumstances fastidiously.
“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 productiveness
Gallay faces the same problem to different data and digital leaders – how you can transfer on the proper tempo into AI and accomplish that with out leaving the enterprise feeling like its calls for are being ignored. She intends to maneuver ahead with warning.
“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 these explorations into AI may pan out. While there might be robust use circumstances for e-commerce and advertising and marketing groups, she expects the primary use circumstances to deal with boosting the productiveness and effectivity of buyer and IT help groups. She explains how GenAI might assist to help Etam’s know-how 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.”
