Month: May 2025

You modernised your stack. You rolled out the PIM. You trained the teams.

And yet the calls keep coming, distributors waiting on specs, sales chasing missing details, buyers dropping off mid-funnel.

Products sit in limbo. Partners lose patience. Your best SKUs underperform.

It’s not for lack of effort. You’ve done what everyone said to do.

But the gap between the tech investment and the business outcome never closed.

Because the rules of B2B have changed, and if your data hasn’t, you’re already behind.

For years, you have been told that implementing an MDM or PIM platform would solve your data challenges. And to be fair, those systems play an essential role: they structure, govern, and manage core master data across the business.

But here’s the issue: these platforms weren’t built to prepare data. They were built to manage it after it’s clean.

The Reality of Buyer Expectations Today

Your B2B buyers have changed, and fast.

Today, 25% of them prefer a fully digital, rep-free experience. That number keeps climbing. Even those who still rely on reps or distributors do most of their research online. If your product data isn’t instantly available, accurate, and detailed, they don’t wait around. They look somewhere else. Your competitors.

That means your product data can no longer be a back-office afterthought. It’s the first thing your buyers encounter and the foundation of their entire experience.

If your product data isn’t complete, consistent, and easy to consume, here’s what happens:

• Distributors delay listing your products or stop selling them entirely
• Channel partners lose trust in your brand and start favoring suppliers with better content
• Buyers drop out mid-funnel because specs are missing or inconsistent
• Sales teams spend more time fixing data than actually selling
• Launches get pushed back, opportunities close late, and your best products underperform

Why Your Platform and Team Are Set Up to Fail

Your teams are still stuck manually standardizing, validating, and syndicating product information. They’re patching up data issues in Excel, firefighting day after day. The platforms don’t do the heavy lifting for data preparation—they organize and store what you give them.

Plus, many platforms are built on rigid, complex architectures that make upgrades costly and slow. Every change or new data source adds friction. Instead of speeding you up, the system slows you down.

So your tools assumed you had a clean starting point, but you didn’t. That gap creates massive drag across your entire go-to-market operation.

The Hidden Cost of Bad Data

Bad product data doesn’t just cause internal headaches. It’s a revenue killer.

Industry reports show that poor product data can cost B2B companies up to 15% of annual revenue, not from IT inefficiency but from missed sales, lost market share, and slower growth.

The cost can be even worse in manufacturing and distribution, where channel complexity is high and buyer expectations are shifting rapidly.

Imagine losing nearly a sixth of your revenue because your data isn’t ready for today’s B2B buyer. That’s a competitive risk you can’t afford.

What You Need Instead: Data Readiness as a Discipline

It’s time to stop treating data management as the end of the story, because it’s not.

You need to treat data readiness as its own discipline, starting with how data is collected, validated, enriched, and syndicated before it reaches your PIM or MDM platform.

That means automating the tedious, error-prone tasks of data preparation. It means modernizing your architecture to be flexible and scalable.

It means rethinking your approach entirely—shifting the focus away from simply deploying technology for the sake of it, and toward what really drives value. Faster product launches that don’t get delayed by last-minute data scrambles. Stronger channel trust, because your partners can rely on consistent, high-quality product content. Better buyer engagement, because customers find the information they need the moment they need it, across every touchpoint. In short, it’s about aligning your data strategy with business outcomes, not IT checklists. Because clean data isn’t the end goal, growth is.

Because when your data is ready, everything else gets easier. Your partners trust you more. Your buyers find what they need faster. Your sales teams spend their time selling, not fixing.

Conclusion: Don’t Play Catch-Up in 2025

You’ve already invested time, money, and effort. But if you’re still stuck in manual data cleanup and slow launches, it’s not your fault.

You’re playing catch-up on a flawed foundation and that’s a losing game in 2025.

It’s time to step back, rethink your data approach, and embrace a new model focused on readiness, automation, and business outcomes.

Because your buyers expect it. Your competitors are moving fast. And you deserve better than constant firefighting.

If this sounds familiar, it might be time to look at your data differently. That’s what we at Thoughtspark help companies do- turning messy, stuck data into a competitive advantage. Rethinking your data strategy doesn’t require an overhaul- just a smarter start. It begins with one smart move.

You bought the platform. You hired the people. You followed the roadmap.

And yet here you are, still buried in Excel, chasing down issues, fixing the same problems month after month. Launches are delayed. Reports are questioned. Customer experience suffers.

If you’re wondering why, you’re not alone. You did everything the market told you to do. But the model was incomplete.

The MDM/PIM Promise: What It Got Right (and What It Missed)

For years, organisations were told that implementing an MDM (Master Data Management) or PIM (Product Information Management) platform would be the silver bullet for solving their data challenges. And to be fair, these systems do a lot of heavy lifting. They govern your data. They manage core information. They establish standards across the business.

But here’s the catch: they don’t prepare your data. They weren’t built for that.

They assume your data is already clean, complete, and standardised before it reaches them. That assumption is where the breakdown starts.

The Reality:
Data Is Messy, Everywhere

The truth is, most businesses today are dealing with decentralised, inconsistent, and often downright messy data. It comes from suppliers, vendors, legacy systems, and siloed departments. It arrives in different formats, with different definitions, and varying degrees of completeness.

And before it ever gets to your shiny new platform, someone or more accurately, many people have to manually profile it, fix it, align it, and try to make sense of it.

That process? It’s usually happening in Excel. Or via email. Or through endless meetings that lead to temporary fixes but no lasting improvement.

The Hidden Cost of Manual Effort

This manual patchwork approach is where so many businesses get stuck. It’s not that your teams aren’t working hard. They’re working overtime. Cleaning, adjusting, validating, reconciling. But they’re doing it without the right tools.

So what do you end up with? Burnt-out teams. Slower time-to-market. Decisions based on questionable data. And a growing list of opportunities missed because the data wasn’t ready in time.

You might not see it in the budget sheet, but the cost is real. Every delay. Every rework. Every customer complaint traced back to a data issue.

Legacy Architecture Makes It Worse

Even the platforms you invested in start becoming part of the problem. Many traditional MDM and PIM tools are built on rigid architectures. They’re hard to customise. Expensive to upgrade. And not particularly friendly to change.

So when your data doesn’t fit perfectly into the boxes these systems expect, the friction only increases.

Suddenly, you’re not just fixing data issues. You’re managing workarounds, building bolt-ons, and fighting your own infrastructure just to keep things running.

The Flawed Assumption

Here’s the heart of the issue: your systems assumed that your data was ready. They weren’t designed to make it ready.

This assumption might sound small, but it has big consequences. Because when your foundation is flawed, no matter how hard you work or how smart your team is, you’re always playing catch-up.

And in 2025, playing catch-up isn’t good enough.

A New Approach: Start With Readiness

There’s a better way to think about this. It starts with treating data readiness as its own discipline. Not a side task. Not a one-time project. But a core capability of your data strategy.

What does that look like?

• It means automating profiling, standardisation, and enrichment, before the data hits your systems.
• It means building processes that are proactive, not reactive.
• It means architecture that is agile, composable, and cloud-native.

This is how you stop reacting to data issues and start preventing them.

From Firefighting to Forward Motion

When readiness becomes a priority, everything starts to shift. Your teams spend less time fixing and more time innovating. Your systems run smoother. Your launches move faster. Your customer experience gets better.

And perhaps most importantly, your investment starts delivering the outcomes it promised.

Because let’s be honest: you didn’t invest in data platforms just to govern spreadsheets. You did it to drive growth, improve insights, streamline operations, and serve your customers better.

That only happens when your data is truly business-ready.

So What Can You Do Now?

If all of this feels familiar, you’re not alone. Many of the most sophisticated organisations in the world are in the same boat. The difference is, some are starting to step back and rethink the approach.

Here are a few practical ways to start:

1. Audit your data readiness process. Not just what happens in your MDM/PIM, but everything that happens before the data reaches those systems.
2. Identify where manual effort is still driving critical workflows. Those are areas ripe for automation.
3. Reevaluate your architecture. Is it supporting agility and scale? Or is it holding you back?
4. Start small. You don’t need to rip and replace. You just need to find the right place to begin.

The Bottom Line

This isn’t about blaming your tools or your team. It’s about recognising a blind spot that many businesses share, and choosing to address it.

You did everything right, based on the model you were given. But that model was missing a critical piece.

Now, you have the opportunity to put it in place. Because in 2025, clean, trusted, business-ready data shouldn’t be the exception. It should be the standard.

If you’re still struggling to get there, maybe it’s time to look at the problem differently. That’s what we do at Thoughtspark. And we’d love to help. We are not asking you to replace your systems, yet. But we do believe data readiness should be at the top of your priorities. Because your data deserves a better foundation.

Let’s show you how to get there, starting here.