After two decades working inside distribution, I have learned the biggest business problems often masquerade as something else. Most distributors think they have a sales problem, pricing problem, rebate problem, or operational efficiency problem.
But in many cases, the culprit is data.
Distributors have enormous amounts of data. The challenge is that much of it is spread across systems, duplicated, or structured inconsistently enough that teams struggle to use it well.
That’s how data quality becomes a revenue problem. If customers can’t get fast, accurate, and personalized experiences regardless of how they’re interacting with a distributor, they will buy less or simply go somewhere else.
Sales teams are often the first group forced to work around bad data because they operate closest to the customer and are under the most time pressure. When information is unreliable, reps cannot hit pause until the data is corrected. They have to find workarounds in real time while still trying to respond quickly and maintain customer confidence.
And that slows down everything the sales team is trying to do.
Reps spend time manually searching for information, reconciling customer records, cross-referencing products, validating inventory, and trying to determine whether substitute products are truly comparable. Instead of selling, reps become detectives – searching across ERP systems, spreadsheets, emails, shared drives, and tribal knowledge inside the organization.
The impact:
- Quotes take longer to complete.
- Customer conversations are less confident.
- Product recommendations are inconsistent.
- Product onboarding slows down.
- Cross-sell and substitute opportunities are missed.
- Reps become more reactive instead of proactive.
- Rebate incentives are missed.
Over time, bad data creates trust issues. Sales teams stop trusting the systems they are supposed to rely on, so they begin building their own spreadsheets, shortcuts, and offline processes to compensate. That only creates more inconsistency.
And from the customer’s perspective, this all shows up as:
- Delayed responses
- Inaccurate recommendations
- Inconsistent pricing
- Duplicate outreach
- Inventory surprises
- Disconnected experiences
In other words, the customer may never see the bad data itself, but they bear the consequences of it.
The Real Goal is Connected, Trusted Data
Too many distributors think the answer is just “cleaning up the data,” a technical exercise. But that’s where things start going sideways. The market is full of generic matching tools and standalone MDM platforms that can identify duplicates. That’s useful, but matching records alone does not solve the business problem.
The real opportunity is improving how the company operates and sells. Right now, many are trying to operate across disconnected systems (systems, acquisitions, vendor files, ERP environments, etc.), so even if you identify duplicates, you may still struggle because no one is working from the same version of the truth.
That’s why this is bigger than a data cleanup project. The goal is to create data the business can use consistently across the board. That starts with four foundational steps:
- First, consolidate the data.
- Then match records across systems.
- Then determine which records are trustworthy enough to survive (the “golden records”).
- Finally, merge that trusted data back into operational systems so teams are no longer working from conflicting versions of the same information.
Don’t underestimate the complexity of these four simple steps. One customer may exist five different ways across multiple systems. Products may have inconsistent manufacturer naming conventions, different units of measure, incomplete attributes, or duplicate records created over years of acquisitions and manual product onboarding.
Which is correct? Which attributes should survive? What happens when records conflict?
This is where many distributors realize the problem is much bigger than just duplicate records. The issue is that different parts of the business are operating from different assumptions about the customer, the product, or the transaction itself.
This requires human oversight to validate exceptions, refine rules, and make judgment calls where automation alone isn’t enough.
It’s important to get this right, because without this foundation, every downstream process gets harder:
- Quoting
- Search
- Onboarding
- Personalization
- Rebates
- Substitute recommendations
- E-commerce
Customer experience depends on connected, trustworthy product intelligence underneath. If the data foundation is fragmented, eventually the customer experience becomes fragmented, too.
This can sound overwhelming, and that’s where many distributors stall out. They assume fixing data requires a massive multi-year transformation before the business will see any value. That’s rarely true.
The better approach is starting with one operational problem the business already knows is painful, proving value quickly, and expanding from there. That could be something as straightforward as improving product cross-referencing, consolidating duplicate customer records after an acquisition, or fixing inconsistent ecommerce search results.
Do It for the Business You Want to Become
A lot of companies build their data environment like a house of cards. At first, it works well enough. Teams add another spreadsheet, another workaround, another disconnected system, another manual process.
Then the business starts moving faster. Acquisitions get layered in, and e-commerce gets added. Pricing tools and CRM systems are implemented. They dip their toes in the water with AI-driven workflows.
And suddenly the weaknesses in the foundation become impossible to ignore.
Search results become unreliable. Product recommendations start to break down. Reporting conflicts across systems. Teams stop trusting the data.
That is why this cannot just be about “cleaning data.”
Distributors need to build data foundations around where they want the business to go, not just around the systems and processes they happen to have today.
Because once disconnected data structures become deeply embedded across the company, fixing them later becomes significantly harder, more expensive, and far more disruptive than most companies expect. The structure underneath becomes less stable over time.
The longer you wait, the more you pay for the same fix. Get the foundation right, and the sales problem you thought you had turns out never to have been a sales problem at all.
Brandon Lassiter is the chief data officer at ProfitOptics.
