Why Data Refinement Is Normal (and Necessary) in System Implementations
In our last post, we talked about why system implementations often stumble — and how the problem usually isn’t the platform, but the data that feeds it.
Today, let’s take that idea further: refining data is not a one-time task. It’s an ongoing process, and that’s okay.
The Expectation vs. The Reality
Most teams approach a new system thinking: “Once we import the data, we’re done.”
But in practice:
-
The first import surfaces gaps nobody realized were there
-
Rules that looked good on paper don’t hold up in real-world workflows
-
Teams discover they want to use the system in ways they hadn’t imagined before
None of this means the implementation failed. It means you’re learning.
How to Think About Data Work
Data refinement is like:
-
Building a house: Blueprints always change once construction begins.
-
Launching software: Version 1.0 is never the final release — patches and updates are expected.
-
Training a team: Skills stick through practice, feedback, and iteration.
Your data is the same. You digitize. You test. You adjust. You repeat. That’s how you build trust in the system over time.
Real-World Examples
-
A beverage company implemented a spec system only to realize half their formulas used inconsistent units (grams, ounces, percentages). Fixing it didn’t mean failure — it meant the system was finally showing them the problem clearly.
-
A fashion brand discovered 30% of packaging records were missing critical fields. Instead of abandoning the platform, they used the gap as a roadmap for data cleanup.
-
A personal care company realized product claims were scattered across multiple fields. The fix wasn’t starting over — it was introducing governance so marketing and regulatory could finally align.
In every case, the system worked as designed. The refinement process made the data work as intended.
The Role of a Partner
System vendors are focused on getting data into the platform. At Dazmii, we focus on making sure the data is fit for purpose once it’s there.
That means:
-
Vetting and governing data before it’s imported
-
Creating taxonomies and glossaries to avoid duplication
-
Building rules that make the data usable across teams, not just “digitized”
The Takeaway
If you find yourself cleaning, revising, or reorganizing data after go-live — don’t panic. That’s not failure. That’s the real project.
The sooner you embrace refinement as part of the process, the sooner your system will deliver the clarity, confidence, and ROI you invested in.
In our next post: We’ll cover how aligning people, process, and system is the final ingredient that makes data work stick for the long haul.
Get Ready
Partners
