Ah, supply chain data, more specifically, the collection of supply chain data. It's arguably the fuel that will power the engine of change in the textile industry (or any industry, for that matter). What’s not arguable, however, is how incredibly difficult it is to collect and derive clear, actionable insights from that data. Add the challenge of ensuring accuracy, and you’ll understand why sustainability teams spend so much time outside, they can't just stare endlessly at the green cells of Excel, they need the green of nature too.
But what type of supply chain data is causing all this frustration? Why is it so hard to collect and analyze?
The first thing to note is that not all data is created, or collected, equally. Anyone claiming otherwise is either misinformed, delusional, or both. The most specific data often comes directly from factories. But here’s the catch: specificity doesn’t guarantee accuracy. So, how can we ensure data is both specific and accurate? That’s the billion-dollar question many companies are struggling to answer. While there’s no silver bullet, I do have a place to start: empowering sustainability teams to better understand and utilize the data they already have.
Every brand and retailer I’ve worked with faces the same data quality challenges. The majority of which lean heavily on technology as the solution and in some ways, they’re right. Technology will be essential for scaling solutions. But here’s the overlooked truth: poor data quality isn’t just a technical issue; it’s also a structural one. The industry has normalized working with facilities through third-party vendors, leaving brands and retailers without direct contact with the sources of their data. Sustainability teams are expected to trace, collect, analyze, and monitor accurate primary data, often without clear pathways to even contact the facilities where the data resides.
And what’s the common response to this fragmented supply chain problem? “We need more data!” I disagree. The apparel industry doesn’t have a data shortage issue; it has a data efficiency and utilization problem. Companies shouldn’t default to collecting more data if they can’t fully understand or act on the data they already have.
As much as we want insights to be as black and white as they appear in spreadsheets, reality is far more nuanced. True insights come from the story behind the data, the whys and hows, that enable real change within the supply chain. These insights emerge from relationships and trust built with facilities, the producers, and those most affected by an opaque supply chain. Building these relationships and, by extension, actionable data, takes time. It’s not the fastest or most financially attractive strategy, but it’s the most enduring and sustainable one.
Ultimately, what appears on the surface as a “data issue” is often rooted in deeper power dynamics and mistrust that have shaped the brand-manufacturer relationship for centuries. If there’s one thing I’ve learned from working in sustainability, it’s this: while the problems we face are vast, they don’t always require the most complex solutions. Sometimes, the most effective answer is the simplest one.