In Brief
The distribution industry naturally produces massive amounts of product data thanks to its large and varied product catalogs. But many distributors don’t know how to use that data to produce business value. At best, they’re only on step two or three of a 10-step journey.
That’s OK. Just because there’s a gap doesn’t mean you can’t close it. By reading this post, you’re already moving in the right direction, because it means you’re at least looking for a way to make product data a competitive advantage.
The next step is building a data foundation. You’ll have to crawl before you can walk, and walk before you run. As long as you stay focused and patient, you’ll get where you need to be.
Before you can even think about advanced analytical capabilities, you need to get visibility into your existing data. That usually reveals opportunities for improvement. That’s normal.
Start with two critical steps:
● Normalize your product data across ERP systems. The same product may have different names, SKUs, or descriptions across systems, especially after acquisitions. One distributor was managing the same product across multiple ERP systems with different naming conventions. Normalization enabled accurate reporting, forecasting, and purchasing leverage.
● Centralize your data. Data can no longer be siloed in different parts of the company. Build a single source of truth, such as a data warehouse. A unified platform improves inventory visibility, streamline reporting, and support analytics initiatives.
AI can accelerate 80%-90% of the work, but pairing it with your team’s expertise ensures accuracy where technology alone falls short.
With your data cleaned and centralized, you can start stacking quick wins. That means better forecasting, smarter purchasing, and more complete reporting. These smaller, targeted projects reduce costs, improve efficiency, and strengthen supplier integration.
Focus on practical high-impact use cases:
● Gain visibility into supplier inventory: This will reduce on-hand stock and improve service levels.
● Mitigate M&A integration challenges: Acquisitions often create product data chaos. AI can reconcile data across systems, maximizing purchasing power and streamlining internal operations.
● Launch product matching at scale: AI accelerates the normalization of inconsistent descriptions, item codes, and manufacturer part numbers. Humans should still be a part of the process; a well-designed process makes it easy for product or category managers to quickly and consistently review and approve edge cases.
If you stay the course, those data-driven “special projects” will become part of the DNA of your business. Over time, data will shape supplier relationships, incentives, and negotiations.
In some industries – like medical supply – distributors are even monetizing their clean, packaged data by offering it back to suppliers through incentive programs. The right data, in the right format, can open new revenue streams.
Not every distributor is ready to jump into advanced AI or data science today. But by laying the groundwork now, you’ll be ready to take advantage of those technologies tomorrow.
Don’t get discouraged if your company isn’t sprinting yet. Just like any long-term transformation, building a data foundation takes time and deliberate steps. What matters most is that you start crawling in the right direction, with a clear view of what it will take to begin walking and eventually running.
Product data touches everything – pricing, inventory, purchasing, customer service – making it a powerful starting point. Clean, structured data delivers immediate value while setting the stage for larger initiatives.
A collaborative partner like ProfitOptics can help guide the journey, equipping you with the resources and expertise to build a scalable foundation and data-driven tools that create lasting business value.
