Treat the Problem, not the Symptoms: Common Mistakes in Data Cleansing

Mistake 1: Fixing Instead of Preventing

Regex and fuzzy matching aren’t a strategy. Smarter teams prevent bad data at the source:

• Dropdowns instead of free text.
• Enforced formats (YYYY-MM-DD, not “today”).
• Push errors back to the system owner, don’t silently correct them.

Business impact: Every hour spent cleansing data is an hour not spent using it.
Prevention scales. Patching doesn’t.
‍

Mistake 2: Cleansing Without Lineage

No lineage = cleansing blind.
If you don’t know where dirty data originates, you’ll never stop the leak. That’s why fixes keep repeating.

Business impact: Without lineage, errors resurface in every dashboard, eroding trust and slowing adoption of BI tools.
‍

Mistake 3: Checking Format, Not Meaning

Empty vs. filled isn’t enough. You need to ask:

"Does it make sense?"

Order date after delivery date → technically valid, logically wrong.

In my more than 15 years of working with textile data, I have often observed the following phenomenon: Color variants.
Usually entered as free text. Beyond typos, you end up with hundreds of variations: 
Variation 1: “yellow,”
Variation 2: “gold,”
Variation 3: “lemon,”
Variation 4: “sunflower”...

This doesn’t just create a cleansing problem, it makes the entire dimension unreliable for reporting.

Aggregations and comparisons that should be simple take far more time, because categories first need to be reconciled. Imagine trying to align KPIs across 50 shades of yellow.

And it’s not limited to niche industries. In many systems, date fields default to a placeholder if left empty, for example, birthdays defaulting to 01.01.1900. Dashboards will happily report a “phantom generation” of centenarians until proper validation rules are enforced.

Business impact: Misaligned or nonsensical values don’t just confuse analysts. They mislead decision-makers, slow down reporting processes, and undermine confidence in data.
‍

Mistake 4: One-Off Fixes Instead of Contracts

Quick scripts feel productive. In reality, they accumulate silent data debt.
Without data contracts between producers and consumers, the same errors leak into every new pipeline.

Business impact: One-off fixes create hidden maintenance costs that compound over time. Interest payments on your data debt.

The Real Cure

Cleansing isn’t about polishing data downstream. It’s about designing for trust:

• Prevent errors at entry.
• Trace and fix issues with lineage.
• Enforce data contracts.
• Validate business logic.

Treat the problem, not the symptoms.
That’s how you stop data debt before it starts, and how you turn reporting from frustration into a trusted decision engine.