WHEAT x Delogue: The hidden cost of bad product data

Bad product data rarely shows up as a line in your budget. More often, it hides in the work surrounding the product: information being entered twice, questions travelling back and forth between departments, mistakes being discovered when the information is already needed and decisions being made later than they should have been.

Individually, these things might seem small, but across an entire collection they start adding up.

CMO and ESG Expert from Delogue, Anja Padget, recently sat down with Nicklas Vad, Head of Buying at WHEAT and Rethinkit Studios, for a webinar about what bad product data is really costing fashion brands, and what changes when you start creating more structure around it.

For WHEAT, that conversation comes from experience. Over the past few years, the Danish childrenswear brand has been rethinking how product information moves between people, suppliers and systems. And their starting point will probably sound familiar to a lot of fashion brands.

When the information exists, but the structure doesn't

Before WHEAT started creating more structure around its product data, the knowledge wasn't necessarily missing. Designers knew the collection, pattern makers had the measurements, buying held much of the supplier information, while marketing and sales worked with what they needed further down the line.

The challenge was that much of this knowledge lived with individual people and across different files, systems and spreadsheets.

“We had information lying in different parts of the company, and it was especially in the people working with the data,” Nicklas explains.

That can work when everyone knows where to go and who to ask. But as a company grows, so does the amount of information, and knowing that the answer exists somewhere isn't quite enough.

Around two years ago, WHEAT started working in Delogue PLM to create a more structured foundation for its product development. It didn't make every spreadsheet disappear overnight or solve every data challenge at once, but it gave the team a shared place to build its product information and create more consistency around how it is handled.

Because scattered information doesn't only create an inefficient internal process. If the right master data isn't available when a key account needs it, it can eventually become a barrier to actually selling the product.

As Nicklas puts it: “Internally the workload is very much on the people doing it, but ultimately the company pays.”

The hidden work behind bad data

One recent example at WHEAT shows how quickly something relatively small can turn into a much bigger task. For its technical outerwear, WHEAT needed information about breathability and water resistance to appear correctly on the garments. The hangtags received from the supplier, however, didn't match the information registered elsewhere.

The team was suddenly left with a very basic but important question: which information was actually correct? Finding the answer meant going back through the information, checking with suppliers, validating what had already been entered and potentially correcting it in several places. “It's manual labor to go over everything twice, and it's not something you really calculate,” Nicklas says.

This is where having product information structured in Delogue becomes important. The goal isn't simply to store more data, but to create one place where the people working on the product can find, add and validate information throughout development rather than piecing it together when somebody suddenly needs it.

But the cost of product data isn't only about correcting mistakes. Sometimes it's about finding the right information too late. Nicklas uses a pair of yellow leggings as an example.

Looking at one part of the sales picture, the leggings appeared to be performing well because retailers were buying them. But the sell-out data told another story: consumers weren't. The timing matters because WHEAT, like most fashion brands, works several collections ahead. By the time the performance of one collection becomes clear, the same style might already have been carried into the next two.

At that point, tech packs may have been created, suppliers contacted, packshots made and fabrics and trims ordered. What could have been a relatively inexpensive decision earlier in the process has already accumulated cost and workload.

With the right data available earlier, WHEAT can make that call sooner.

Better data starts earlier

One of the biggest changes at WHEAT has therefore been less about collecting more data and more about changing when the work happens. Sales performance can inform designers before the next collection takes shape rather than forcing changes once development is already underway. Product information can be built up as the product develops instead of being collected in one large bundle at the end, while suppliers can contribute information closer to its source.

Delogue plays a role in creating that shared foundation by building the information around the product itself and making it available to the people who need to work with it. That doesn't remove the work involved in good product data, but it moves more of it to a point where it makes sense. WHEAT has already seen a reduction in some of the last-minute follow-ups, re-entry and corrections simply because information is increasingly being handled earlier in the process.

And according to Nicklas, more structure hasn't come at the expense of creativity. 

If the data already shows which colours perform, which products should be rerun and which styles aren't moving, designers don't need to spend the same amount of time revisiting decisions the business already has an answer to. Or, in Nicklas' words: “Use the creativity where it's actually fun.”

Building the foundation for what's next

The need for good product data isn't getting smaller. Key accounts are asking for more detailed master data, requirements around traceability and the Digital Product Passport are developing, and much of the information brands need originates further back in the supply chain.

At WHEAT, Delogue helps involve suppliers in that flow, allowing them to contribute information they are better placed to provide while WHEAT can validate what comes back. But Nicklas also points out that brands aren't the only ones dealing with increasing data demands. “If we think data is a lot in the office, it must be crazy for the suppliers because they get the same request that we do and they don't know why they have to do it.”

And now AI adds another reason to get the foundation right. WHEAT is already exploring how AI could help enrich existing product information rather than simply validate it, but whether the next requirement comes from AI, legislation or a key account, much of it relies on the same thing underneath: structured and reliable product data.

WHEAT isn't presenting itself as having solved the entire challenge. There are still processes to improve, integrations to build and more work to do across the company and its supplier network. Nicklas' advice is simple: “Take it one step at a time.”

The first collection will require work, but the next one doesn't start from zero. Once fabrics, trims, styles and recurring product information begin building into a library in Delogue, that foundation can be reused season after season.

Because ultimately, getting product data right isn't about collecting as much information as possible. It's about having the right information available when it matters, so teams spend less time searching, correcting and repeating work – and more time moving the product forward.

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