Cleaning Up the Mess
How Product Teams Should Approach Data Quality at Scale
In almost every product discovery meeting, the same question comes up again and again: How do we clean up large data sources?
It comes up most when teams begin integrating sustainability, packaging, or supplier data. The question sounds technical, but I feel it is really a product design problem. It focuses on trust, repeatability, and accountability. I have helped define these frameworks at several startups, and the goal is always the same: build systems that make messy data reliable enough for automation, reporting, and machine learning.
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The Real Problem Behind “Data Cleanup”
Raw data never arrives clean. EPR (Extended Producer Responsibility) files often include padded zeros, supplier names appear in multiple formats, and weight or CO₂ data may use inconsistent units. Bills of Lading and Bills of Material contain overlapping identifiers and regional codes. Any well intentioned export hide small issues such as stray apostrophes or invisible spaces that break scripts.
These are not cosmetic problems. They represent inconsistencies that undermine confidence. When teams cannot trust their inputs, they cannot trust their forecasts, emissions reports, or risk models. Data cleaning is not an operational chore. It is the foundation of every credible product decision.
Concepts That Ground the Solution
Effective data cleanup requires collaboration between data engineering, machine learning, and human review. Automation provides the scale. Algorithms adapt to new patterns. People intervene where judgment matters most.
Building It Into Product Development
Most teams still treat cleanup as a one-time effort. That approach fails. Data quality should be part of the product lifecycle from day one and measured continuously. The best teams define data quality KPIs, duplicate rates, field completeness, and error ratios, and link them to product OKRs.
When new supplier data arrives, automated checks run before it enters reporting pipelines. Issues are flagged early and tracked. Over time, data cleanup becomes a continuous improvement system instead of a crisis response.
The Roles Needed to Execute the Strategy
Scaling reliable data requires collaboration across technical and business roles. Each function owns a specific layer of responsibility.
In early-stage companies, these roles often overlap. What matters is shared accountability. Teams that treat data quality as a product feature outperform those that treat it as maintenance.
Where This Work Pays Off
The impact of structured cleanup appears across every data domain.
Despite the variety of datasets, the same principles apply. Normalize schema. Standardize units. Resolve ambiguous entities. Document every transformation.
Once these systems are in place, results are measurable. Teams report up to a 70 percent reduction in manual reporting and faster dashboard performance. More importantly, they gain confidence in their analytics and forecasts.
The Payoff
The benefit extends beyond clean data. It creates organizational clarity. Teams stop arguing over which dataset is correct and start focusing on decisions. In sustainability and packaging analytics, where accuracy drives regulatory trust, clarity itself becomes a competitive edge.
TLDR
Data cleanup rarely earns headlines, yet every credible AI model, forecast, or compliance dashboard depends on it. I have led this transformation multiple times, and it always starts with one mindset shift: data quality is a product problem, not an afterthought. The teams that recognize this build systems people trust. That trust is the real infrastructure everything else relies on.
Attribution and Inspiration
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Cleaning Dirty Data & Messy Data, by Paul Warburg, Alteryx, Sep 22, 2020
Data Cleaning Guide: How to Turn Messy Data into Actionable Insights, by Ben Hartig, Ingestro, August 12, 2025
Data Cleaning in ML, Geeks for Geeks, September 16, 2025
What is Data Cleaning? 3 Practical Examples of Fix Messy Data in Google Sheets and Excel, by Riley Walz, Numerous, Feb 15, 2025