In our first post, we discussed how a culture of data is the lifeblood of a thriving digital business. But what does the typical data culture in industrial distribution look like today? For most, it’s a culture of firefighting—a reactive, manual struggle to manage the flow of information across disparate teams and systems. When a problem arises, the response is rarely self-reflection. Instead, it’s a shrug and a simple, “That’s someone else’s problem.” I’ve been in(and ashamedly even instigated sometimtes) many games of hot potato: identifying major data discrepancies, and then throwing around massive cleanup projects for another team to work on.

The reality is that bad product data isn’t a problem for one person or one team—it’s a problem for everyone. Trying to shove the responsibility to another team doesn’t win you much in terms of teamwork and respect, and it usually will come back to bite you. Also, whether you are the owner of the data or a consumer, the problem will remain until the work actually gets done.

I’ve seen it too often where the perception is that bad data can be hidden, or that it’s an internal problem. While that may have been the case in a previous era, it is farthest from the truth in the digital age! Not only is product data core to internal operational excellence (think inventory fulfillment and optimization, new product introductions, workflow automations, reporting accuracy, etc), it is also now essential within your customer experience. The information you display online—the descriptions, images, dimensions, and more—directly affects how a customer perceives and interacts with your brand. Bad data isn’t just a missed order; it’s a damaged reputation and a broken trust.

Think about it:

• A buyer in a specific region places an order for a chemical they found on your website, only to discover later in the process that it can’t legally be sold in their location.
• A customer looking for a replacement part for a decades-old piece of equipment can’t find a cross-reference number on your site.
• A project manager orders what they believe to be a single unit, only to have a case of 24 delivered, causing an inventory and cost headache.

In each of these scenarios, the customer’s experience has been directly harmed by a failure in the underlying data. Before, there was almost always a human to catch these discrepancies before the customer realized it. Now, these experiences are fully exposed!

The root of this problem isn’t usually malice or incompetence; it’s a lack of organizational alignment. In a modern B2B organization, different data is owned and created by different departments, each with its own perspective and priorities.

It starts with the manufacturer: They create data to better market and educate end-users as well as to define how a product moves through distribution. But when that data is passed to an industrial distributor, it’s consumed by multiple teams for different purposes.

• Supply Chain & Planning needs accurate lead times and pricing.
• Sourcing identifies exact or substitute products.
• Operations relies on volumetric data, UPCs, and barcodes.
• Logistics requires freight class and weight for shipping.
• Marketing needs proper images, product notes, and features.
• Inside Sales & Customer Service needs to manually track down information to fix customer issues.

The complexity of this flow is immense. Logistics might work directly with a manufacturer to book a truck, while Supply Chain deals with lead times. Operations tackles non-conforming issues, and Sourcing verifies product details for the sales team.

This is where the finger-pointing begins. The technical team blames the business teams for not having proper governance, while the business teams lack a holistic view of the data flow to understand the changes being made. This confusion and lack of clarity lead to missed KPIs, inaccurate invoices, and, ultimately, a poor customer experience. It’s the front-line teams, like Inside Sales and Customer Service, who often feel the brunt of this chaos.

While it’s easy to point fingers, a sustainable solution isn’t about assigning blame—it’s about accepting shared responsibility. Think of your product data as the digital currency of your business. Just as you have an audit trail for every financial transaction, you must have an audit trail for your product data:

• Do you have visibility of the flow of this currency from the manufacturer all the way through to the customer?
• Who is responsible for each of the steps along the way?
• Who needs to be notified when data changes?
• Do your consumers of data contribute to the rules that define it?
• Are there mechanisms in place for data consumers to provide feedback for improvement?

The first step toward a thriving digital business is moving from a culture of “whose problem is it?” to a culture of “how can we solve this together?” The data flowing from your manufacturers isn’t just a set of fields and values; it’s a story that your entire organization must learn to tell correctly and consistently.

The solution isn’t in a flashy new technology, but in a clearly defined strategy that bridges the gap between your teams, builds trust, and puts the customer experience at the center of every decision.

We often hear the objection, “Our product data is good enough.” But is it? Have you ever had to manage a customer’s disappointment because you couldn’t meet an expectation set by the data on your website? Have you ever traced back how it got this way? Was the manufacturer at fault for giving you wrong data, or was this an internal mistake? Be honest, it’s nearly always the latter, and even when the manufacturer is involved, it was likely provided out of context in being used for the website.

The cost of bad product data isn’t just an abstract concern; it’s a tangible loss of time, money, and trust. My aim is to help you see these hidden costs and begin the journey of transforming your data from a departmental burden into a shared, strategic asset.

Next week, we’ll discuss how to take the first step in this transformation by building a comprehensive product data strategy.