Data Strategy Isn't an IT Project
For mid-sized maisons, it's a commercial one
People I speak with this year describes some version of the same morning: three dashboards, three different numbers for the same quarter, and a meeting that starts by arguing about whose data is right before anyone can discuss what to do about it.
At mid-sized maisons (houses balancing wholesale, DTC and boutique in parallel), this isn't a technology failure. It's a structural one. Each channel was built to answer its own question, on its own timeline, with its own definition of a “customer” or a “sale.” Nobody designed them to agree with each other. Until recently, nobody had to.
That has changed, not because of a new dashboard mandate, but because the tools every board now wants (AI-driven forecasting, personalisation, predictive replenishment) only work as well as the data underneath them. A generative model layered on top of three disagreeing systems doesn't resolve the disagreement; it automates it, faster and with more confidence than before.
The scale of the gap is well documented at the enterprise end. Gartner's 2023 survey of data and analytics leaders found only 44% of D&A teams considered themselves effective at delivering business value. Most cite people and process gaps, not technology, as the blocker.¹ Wavestone's 2025 AI & Data Leadership survey of major firms tells a similar story: 84.3% now have a Chief Data Officer, up from just 12% in 2012, yet only 37.3% describe themselves as a genuinely data-driven organisation, and 91.2% name culture and change management, not tooling, as the principal obstacle.² Even companies that solved the technology problem haven't solved the strategy one.
Mid-sized maisons rarely have a CDO. What they usually have is a CMO quietly doing the reconciliation by hand, every month, before the real conversation can even start.
The near-term case
The first return on a real data strategy is speed, not sophistication. A single, trusted view across wholesale, DTC and boutique means a pricing or allocation decision that used to take a quarter of cross-checking can be made in weeks, and defended with confidence in the room. Gartner puts the average annual cost of poor data quality at $12.9M for large enterprises.³ At maison scale, the equivalent shows up less dramatically but just as reliably: in discount leakage nobody can trace, in media spend that can't be tied back to a channel, in replenishment calls made on last season's assumptions because this season's numbers aren't ready. Fixing the foundation pays for itself before it does anything clever.
The longer-term case
The compounding return is less visible and more valuable. Today, most companies investing in data credit it with productivity gains, not growth: Wavestone's survey found just 9.5% cite business growth as the primary value delivered, against 57.5% citing efficiency.⁴ The maisons that push past efficiency, into using clean commercial data to actually shape what they sell, to whom, and through which channel, are building an advantage most competitors haven't started on. And for founder- or family-controlled houses weighing a future raise, partial sale or succession, this is no longer a soft asset: buyers and investment partners increasingly treat data maturity as a diligence line, and price its absence into their offer.
The AI conversation every board is having right now assumes the data foundation already exists. For most mid-sized maisons, it doesn't yet, which means the strategic question isn't which AI tool to buy. It's whether the data underneath it can be trusted at all.
If this is a conversation you're already having internally, I'm always glad to compare notes.
Elisabeth
The Beauty of Data. Designed for Growth.
References
1. Gartner, “Gartner Survey Reveals Less Than Half of Data and Analytics Teams Effectively Provide Value to the Organization,” 2023. https://www.gartner.com/en/newsroom/press-releases/03-21-2023-gartner-survey-reveals-less-than-half-of-data-and-analytics-teams-effectively-provide-value-to-the-organization
2. Wavestone / NewVantage Partners, “2025 AI & Data Leadership Executive Benchmark Survey.” https://static1.squarespace.com/static/62adf3ca029a6808a6c5be30/t/67642c0d40b42a7d7e684f49/1734618125933/2025+AI+&+Data+Leadership+Executive+Benchmark+Survey+120624.pdf
3. Gartner, “Data Quality: Why It Matters and How to Achieve It” (citing 2020 research: poor data quality costs organisations an average of $12.9M per year). https://www.gartner.com/en/data-analytics/topics/data-quality
4. Wavestone / NewVantage Partners, “2025 AI & Data Leadership Executive Benchmark Survey.” https://static1.squarespace.com/static/62adf3ca029a6808a6c5be30/t/67642c0d40b42a7d7e684f49/1734618125933/2025+AI+&+Data+Leadership+Executive+Benchmark+Survey+120624.pdf
