Why Distributors’ Data is Messy (and How to Clean It Up)

Distributors rely on data for pricing, forecasting, customer insights, but often don’t realize how flawed their data really is until a pricing project gets delayed, a report breaks, or their e-commerce launch hits a wall.

Data doesn’t need to be perfect. But if it’s inconsistent, incomplete, or scattered across legacy systems, it can’t support the decisions that drive profitable growth.

So, what makes data “junk,” and what can be done about it?

What Does “Junk” Data Look Like?

Distributors often think their data is fine until someone tries to use it. Here are some common signs your data may need a cleanup:

• Product records with inconsistent names and descriptions.
• Customer records with duplicates and variations, where the same company appears under five slightly different names or with conflicting addresses.
• Units of measure that don’t match or lack clear conversion rules
• Data gaps that make it unclear where products are sourced from or how, or whether duties apply.
• Multiple ERPs from past acquisitions, each with its own structure, naming conventions, and data quirks. One of our distributor clients was selling the same product under 11 different SKUs, for example.

Where the Mess Comes From

Distributors don’t set out to create bad data. It builds up as the result of mergers, legacy systems, manual data entry, and neglected cleanup. Even with a modern data platform, garbage in still means garbage out.

Some common contributors include:

• Old systems with field limitations (like 20-character product descriptions)
• Manual entry across different teams or locations
• Merged businesses that were never fully integrated
• Spreadsheets and workarounds that become permanent

Eventually, the mess becomes too big to ignore. But by then, it’s embedded in how the business operates.

Why Cleaning Up Your Data Matters More Now

Messy data may have just slowed down reporting or caused a few customer headaches in the past. Not great, but distributors’ teams would work around it. That’s no longer the case.

Today, distributors need real-time pricing, personalized selling at scale, predictive analytics, AI-powered tools. And all of these require clean, connected, and consistent data. If your data is broken, those initiatives stall before they start. 

A sales team might not realize how many repeat orders they’re losing, for example, because their system treats the same customer as three separate accounts. Or an ecommerce rollout may stall for months because no one can map SKUs to clean product names and photos.

What Good Data Looks Like

Clean data doesn’t mean perfect data. The goal should be to get usable, trustworthy, and structured data that can support the business.

Good data:

• Has a clear source of truth for customers, products, and suppliers
• Uses consistent naming, formatting, and categorization
• Is centralized, not buried in spreadsheets or one-off systems
• Can be connected across tools and departments
• Enables automation, forecasting, and reporting

You don’t have to fix everything at once. Start with a high-impact use case such as standardizing product data for e-commerce or building a clean customer master. After that, grow into a broader modern data platform. The key is to approach the task with structure and sustainability in mind.

Use entity resolution and matching tools to clean up duplicates. Entity resolution involves using rules and algorithms to detect and merge duplicates – for example, recognizing Acme Corp and Acme Corporation (East) as the same company.

Prioritize your cleanup by value, such as high-volume SKUs, top customers, and core suppliers. And then – this is important – build processes to prevent new data from going off track.

When you’re ready, partner with ProfitOptics to build a modern data platform to support cleanup and ongoing use.

The Payoff

Cleaning up data is one of the highest-ROI initiatives a distributor can do.

It improves reporting. It enables pricing and forecasting projects. It keeps ecommerce initiatives on track. It lays the groundwork for AI and automation. And it makes your team more confident in the decisions they’re making every day. Talk to our team today to learn more about the value of clean data.