Why Clean Data Doesn’t Mean Correct Data
“Clean data” is one of the most misleading compliments in analytics. A dataset can be clean, structured, and consistent—and still be wrong for decision-making.
Correctness is not just about whether the data is formatted properly. It’s about whether the data reflects the real business, the real user journey, and the real definitions stakeholders are using today.
1. The Two Types of “Wrong”
Most analysts think wrong data means broken tracking or missing events. That’s one type.
- Wrong technically: tags not firing, events missing, duplicate hits.
- Wrong logically: data is clean, but business logic, filters, exclusions, and definitions make the KPI misleading.
The second type is more dangerous because it looks fine—and teams make decisions confidently.
2. How “Clean” Data Becomes Incorrect Over Time
Most analytics systems evolve like this:
- Someone builds a clean base table or dashboard view with a specific scope.
- The business changes: new domains, new flows, new markets, new product rules.
- The “clean” logic stays the same because nobody revisits it.
- Now your dashboard is stable—but wrong.
3. Real Examples of Clean-but-Wrong Data
- Legacy domain filtering: dashboards only include
www.site.com, but new checkout/upsell happens onpay.site.com. - “Helpful” exclusions: internal users excluded using rules that now remove real customers (corporate networks, B2B buyers, partner traffic).
- Definition drift: “conversion” was an order last year; now it’s an order + subscription activation.
- Channel logic drift: paid channels are grouped incorrectly, or UTM conventions change and break attribution.
- Event naming drift: tracking changes, but reporting tables still expect old event names.
4. The Cost of “Correct-Looking” Dashboards
- False confidence: teams act faster, but in the wrong direction.
- Broken alignment: departments disagree because each uses a different “truth.”
- Analyst credibility risk: stakeholders stop trusting analytics as a whole.
- Wasted time: analysts become validators instead of decision enablers.
5. How to Build Correctness (Not Just Cleanliness)
- Always document scope: what’s included, excluded, and why.
- Keep a raw baseline: an unfiltered view you can always compare against.
- Version KPI logic: treat definition changes like product releases.
- Audit quarterly: check whether tables and dashboards still match the business reality.
- Make assumptions visible: show filters and logic inside dashboards.
Final Thoughts
Clean data is a hygiene standard. Correct data is a business standard.
If you want stakeholders to trust analytics, your job is not to make the data “pretty.” Your job is to make the logic transparent, reviewable, and aligned with what the business actually does today.
Written with support from AI tools and edited by Hisham Ghanayem. All insights reflect real-world analytics practice.
