What a Broken Coffee Table Taught Me About Distribution's Data Problem

Except that deal falls apart immediately in distribution.

And how could it not? We’re juggling 1,200 vendors sending data in wildly inconsistent formats. Spreadsheets, PDFs, supplier portals, the occasional photo of a product label emailed by a sales rep. And that’s just the intake problem. We then have to map, enrich, and normalize every piece of incoming data across anywhere from tens of thousands to millions of individual SKUs.

When a single vendor changes their data format, it can ripple across thousands of affected SKUs, and that’s just one vendor out of 1,200. Multiply that across your entire supplier base, and you have a task that is not just time-consuming but functionally impossible to do well by hand.

Some companies build internal data teams to do this manually. But most can't justify the headcount, so they try the alternatives.

Option one. Shared data services. Technically accurate, but they give every other distributor member the exact same data. This creates SEO issues when Google sees duplicate content across dozens of sites and penalizes everyone. You paid for data and got worse SEO rankings in return.

Option two. offshore agencies. Slow and chronically wrong on technical products. One distributor we talked to in the construction supply space hired a firm to enrich its mortar product line. After paying them thousands of dollars, the firm came back with specs for military mortar instead of mortar, the cement paste for building supplies. These types of expensive mix-ups happen all the time. Neither option solves the underlying issue.

The $5B problem you can’t see

The cost of lost sales, customer returns, and hours of manual data cleanup add up quickly. NAED research puts the cost of bad product data for electrical distributors and manufacturers at $5 billion annually. And that's just one vertical.

Every customer who can't find what they need on your site is part of that number. Here's how it plays out: a customer searches "3/4 inch brass fitting" and nothing comes up. Not because you don't carry it. But because it's cataloged as "0.75 in. brass connector." That customer doesn't call to flag the mismatch. They just leave your site, and you never knew they were there.

What AI changes for your product data

For a long time, there was genuinely no good answer. Automation at the scale and accuracy distribution requires simply didn't exist. Your options were bad data or expensive humans slowly producing bad data. That's now changed.

New AI models can scrape manufacturer websites, parse spec sheets, and normalize attributes across your entire catalog automatically. It can also normalize your entire product taxonomy at scale, and notices when different suppliers are describing the exact same spec in different ways. Take color: one supplier calls it "Navy," another calls it "00-Blue," a third calls it "Dark Blue." AI resolves all three to "Blue" in milliseconds, consistently, across every category in your catalog. That is what makes the search filters on your ecommerce site actually work.

This allows you to 10x the work of every employee manually enriching or organizing your product data. And when your product data is clean and consistent, everything downstream works better.

Search returns results because the terminology matches what customers actually type. Filters work because attributes are normalized across categories, not just within a single product line. Product pages convert more because the specs, images, and descriptions give customers all the information they need to make a confident purchase. And your SEO improves because your content is unique, not the exact same copy as the twelve other distributors pulling descriptions from the same data broker.

What to do next: Be your own customer for 10 minutes

If you're not sure where to start, do what I did when I was looking for that caster wheel. Go to your own website and try to buy something with a specific spec using only search and filters.

Can you identify the right item without calling a rep? From there, pull your zero results search report. Most eCommerce platforms log every search that returns nothing. That report is a direct map of your data problems, written in your customers' words, not yours.

Next, pull up five of your best-selling SKUs next to Grainger's. Note what they show that you don't. The missing specs, the absent filters, the thin product pages.

This is a great way to start mapping what's broken on your website. The next question is where the problem lives. Pick your worst-performing category and trace the data back to its source. A buying group feed? An offshore agency? A supplier portal nobody has touched in two years? That's where to start enriching your data.

That used to mean hiring a team and doing it by hand, category by category, SKU by SKU. Now there are AI tools that scrape, parse, and normalize your catalog automatically, at a fraction of the time and cost.

Sure, the gap between what you carry and what your customers can find is a data problem. But now for the first time, it's solvable at scale.

Every day you wait, someone is doing exactly what I did, going from site to site, looking for a part they know you carry. Most of them aren't waiting around to find it on yours.