If you manage a product data team — or if product data quietly falls under your responsibility — there’s a good chance you’ve felt this tension before.

You know the work matters. You know the team is busy. And yet, despite everyone working hard, something feels off. The same issues keep popping up. The same questions keep coming back. The same people keep getting interrupted. Over time, you start to notice the toll it takes. People are burning out, even though they care deeply about doing good work.

This isn’t a failure of effort. In most organizations, it’s a failure of structure — and more specifically, a failure to focus the team on work that actually compounds instead of resets.

Let’s talk about what that looks like in practice, and what leaders can do differently.

In many B2B distribution and manufacturing organizations, product data work doesn’t arrive in a neat queue with clear priorities. It arrives as a steady stream of “quick asks.” A weight is wrong. A description doesn’t make sense. A supplier file won’t load. Someone downstream is blocked and needs help now.

So the team does what good teams do: they help. They fix the record, update the field, clean the file, and move on to the next request. From the outside, this looks like productivity. From the inside, it feels like whack-a-mole.

Over time, familiar patterns settle in. There’s usually a hero — the person who knows where everything lives, which system overwrites which, and what workaround will get things moving fastest. Everyone depends on them, even if no one ever says it out loud. Manual work becomes the default solution because it’s the fastest path in the moment. Stepping back to redesign the process feels like a luxury the team simply can’t afford.

The people doing the work rarely have time to improve how the work gets done. In many cases, they don’t even have the space to see above the wall of tasks in front of them. They’re too busy staying afloat. Ironically, the individuals who manage this chaos best are often the ones promoted into leadership roles — and once there, they struggle to understand why the rest of the team can’t replicate what they themselves did so effectively.

As leaders, we unintentionally reinforce this dynamic. We praise responsiveness. We reward speed. We escalate when something is urgent, even if it’s the tenth time the same issue has appeared. The result is a team that works incredibly hard and still feels undervalued, with no real sense that the work will ever get lighter.

Not because the work isn’t important — but because they can see that fixing today’s problem doesn’t prevent tomorrow’s.

So how do we get out of this trap?

The future state of a strong product data team doesn’t begin with a reorganization, additional headcount, a new PIM, or a grand data initiative. It starts with a quieter but far more important shift in how leadership defines success.

Instead of asking, “How fast can we fix this?” the more useful question becomes, “Why does this keep happening — and what would make it stop?”

When that question starts to guide the work, value shows up differently. Fewer repeat issues. Fewer last-minute escalations. More predictable data downstream. Clearer expectations upstream. Manual work still exists, but it’s intentional and purposeful, not endless.

To make this real, it helps to ground the idea in a single, familiar example.

Take something as mundane as product weight.

In many organizations, weight is a constant source of cleanup. Someone flags a discrepancy after a shipment is reclassified. The data team fixes the value in the ERP. Everyone moves on — until the next freight invoice shows up with another adjustment. The team spends an enormous amount of time correcting weights, yet the problem never really goes away.

In a scaled team, the response changes. Instead of fixing the next bad weight, the team steps back and asks why weight keeps arriving wrong in the first place. Is it coming from suppliers in inconsistent units? Is there confusion between net weight and shipping weight? Is there no shared rule for which value should be used where?

The work shifts from correction to design. One person documents what “weight” actually means in the organization. Another builds a simple intake rule or template that enforces units. A lightweight validation flags obvious outliers before the data moves downstream. Slowly, the volume of fixes drops — not because people are working harder, but because fewer bad weights make it into the system at all.

Nothing about the data became more complex. The work just moved upstream.

This is what it looks like when a product data team starts to scale its impact instead of absorbing demand. The shift doesn’t slow the business down. It speeds the organization up by removing friction before it spreads.

Downstream teams stop guessing. Upstream teams get clearer guidance. New hires ramp faster because expectations are written down instead of passed along informally. Fewer things need to be “fixed,” and when issues do arise, they’re easier to diagnose.

One of the most common misconceptions about product data work is that it’s primarily about typing accuracy or tool expertise. In reality, the people who succeed long-term in data management tend to share a different set of strengths.

They get curious instead of frustrated when the same issue keeps coming back. They’re comfortable saying, “This shouldn’t be manual.” They think about who uses the data next, not just who asked for it. And they’re willing to write things down so the organization doesn’t have to relearn the same lesson over and over again.

These qualities don’t always show up cleanly on a résumé, and they’re easy to overlook when the pressure is on to just get things done. This is where leadership enablement matters most.

If we want people to move beyond firefighting, we have to give them small, immediate ways to improve their own work — not someday, and not through a six-month initiative, but now. That often means bringing lightweight, practical technology directly into the team itself.

Advanced Excel templates or macros that eliminate repetitive cleanup. Power Query workflows that standardize supplier files automatically. Simple Power BI views that reveal where issues keep recurring. Basic analytics that show how complete — or incomplete — the data really is.

These aren’t big technology bets. They’re confidence builders. When these capabilities live inside the team, the people who own the data also own the improvement. They don’t have to submit a request or wait for approval. They can see a problem, change the process, and feel the impact immediately.

That’s empowerment in a very real, very practical sense.

If you’re reading this and wondering where to start, the good news is you don’t need to overhaul your organization, buy new systems, or announce a big “data initiative” to make progress. Small, intentional moves — done consistently — can change the trajectory of a team far faster than most people expect.

A simple place to begin is conversation. Not a status meeting. Not a review of what’s broken today. But an honest discussion with the team about what they fix over and over again. Ask them where their time actually goes. Ask which problems feel familiar before they even finish explaining them. You’ll usually hear the same themes surface quickly, and those themes are your roadmap. They point directly to the work that isn’t scaling.

Once those patterns start to emerge, document them. Not perfectly, not formally, and not for an audit — just write them down. Capture what keeps breaking, why people think it happens, and what usually gets done to fix it. Documentation at this stage isn’t about governance; it’s about shared understanding. It gives the team permission to move from “this is just how it is” to “this is something we can improve.”

As a leader, one of your most important roles here is to protect a small amount of time for that improvement work. Even a few hours a week sends a clear signal that prevention matters, not just responsiveness. Without that protection, urgency will always win, and the team will stay trapped reacting to the same issues indefinitely.

And when the team does find a win — even a small one — it’s your responsibility to make it visible. Share it. Talk about it. Explain what changed and why it matters. This helps everyone understand what success actually looks like, especially in a function where good work often goes unnoticed because nothing breaks. Over time, these shared examples become the team’s internal playbook.

Equally important is how present you are during this process. Spend time with the team while they’re thinking through problems, not just when you’re reviewing outcomes. Listen to how they reason through tradeoffs. Ask questions that nudge the thinking upstream. This isn’t micromanagement; it’s mentorship. It helps steer the team toward better habits without prescribing every answer.

And yes — this is work. But it’s also allowed to be fun.

There’s something genuinely energizing about removing unnecessary effort, simplifying messy processes, and watching people take pride in improvements they designed themselves. When leaders engage this way, they’re no longer seen only as taskmasters or escalation points. They’re seen as humans — partners in the work — which builds trust and momentum far more effectively than pressure ever could.

Above all, treat data management as the skilled, thoughtful work that it is. Not entry-level. Not a dumping ground. And certainly not invisible. When people feel trusted, supported, and recognized for building things that last, they tend to rise to the responsibility.

None of this requires perfection. It requires intention — and a willingness to lead alongside the team, not just over it.

Product data will never stop flowing. The real question is whether your team is stuck catching it one record at a time, or shaping how it moves through the organization.

When leaders shift the focus from manual output to lasting impact, something changes. The work becomes lighter. The team becomes more confident. And the business starts moving faster — not because people are rushing, but because fewer things are breaking in the first place.

That’s what a well-supported product data team makes possible.