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:
- Audit your data readiness process. Not just what happens in your MDM/PIM, but everything that happens before the data reaches those systems.
- Identify where manual effort is still driving critical workflows. Those are areas ripe for automation.
- Reevaluate your architecture. Is it supporting agility and scale? Or is it holding you back?
- 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.