Pre-Flight Checklist: Purify Your Data (Post #5 of 20)
How does clean data feel?
The quality of your data will make or break your success.
Purifying the Data Supply for AI isn’t glamorous or comfortable, and means having to confront the worst part of you system.
Think of the escape scene from “The Shawshank Redemption” as a metaphor for this example. (Spoiler alert if you haven’t seen the movie!) Andy has to crawl through 500 yards of raw sewage, the ultimate impurity (slop), to find freedom.
You can’t fix what you can’t face.
Andy didn’t escape by avoiding the sewer. He faced it head on, crawled through it and stepped into freedom. Your organization has a crucial opportunity to commit to passing through this messy, painful process of data remediation to emerge into the freedom and scalability of pure data.
To ensure your AI mission is built on a solid foundation, you must satisfy three non-negotiable conditions across Policy, Infrastructure and Process. Purify the data. Get it crystal clear. Build everything else on that.
3 Steps to Purify Data Supply
When met simultaneously, these non-negotiable conditions will require pure, reliable data for scalable AI.
Policy - e.g. Define and Rank Data Sources (R/Y/G). Label all data sources Red, Yellow, or Green to establish what data can be used internally or externally for AI.
Red Data: Contains PII (Personally Identifiable Information), sensitive IP, or is legally restricted. Must be anonymized of blocked entirely from LLMs.
Yellow Data: Proprietary data, aggregated and non-PII. May be only be used internally and not shared with external tools or customers.
Green Data: Public, non-proprietary, or approved for external training. May be use with third part external tools and customers.
Infrastructure - e.g. Single Source of Truth (SSOT) Data. Your data ecosystem must be reviewed and architected to establish a Single Source of Truth (SSOT). Require that all legacy and contradictory data sources are either decommissioned or deleted. The AI can only draw from SSOT.
Process - e.g. Formally Designate a human Data Steward. This individual role must be made permanently accountable for data quality, ensuring the purity is sustained, not just achieved once. This Data Steward is the human integrity checkpoint for the entire data supply.
Test Flight: The Data Purity Triage Drill
This drill immediately exposes the structural gap between your current data and the required purity standards.
Observation: Choose one critical business process reliant on data (e.g. Q4 sales forecasting).
Ranking: Have your team attempt to categorize the source data for that process using the Red, Yellow, Green system defined above.
Outcome: Identify and analyze the data sets where there are multiple sources and where there is ambiguity and/or data is exposed. (e.g. How many versions of “draft” are you using? Do you have Red data in an LLM that chat bots are accessing?)
Mission Debrief
How did it go? Did your team find any Slop risks?
Data Purity is the foundation of Scalable AI. By requiring a Red, Yellow, Green policy, establishing a Single Source of Truth (SSOT), and designating a permanent Data Steward, you move from chaos to control. Your commitment to this messy purification process is the only way to transform your system’s “Slop” into the pure data required for your Enterprise Super Highway.