Building a Data‑Quality‑First Strategy: Five Steps to Friction‑Free AI

AI can only be as good as the data it’s built on. Yet poor data quality still derails the majority of AI projects. Research shows that bad data costs businesses 20–30% of revenue annually (Techment), and that organisations spend up to 80% of project time cleaning and preparing data (TechRadarPro). 

To deliver friction‑free AI, you need a data‑quality‑first strategy. At Beyond: Putting Data To Work, we recommend starting with these five steps: 

1.      Profile your data continuously Use automated profiling tools to detect gaps, anomalies, and inconsistencies before they infect AI models. 

2. Standardise and cleanse  Align formats, resolve duplicates, and fill in missing data. Modern AI‑powered cleansing tools can speed this dramatically. 

3. Enforce governance rules  Set ownership, permissions, and usage guidelines so that only clean, approved data feeds your AI. 

4. Monitor in real time  Detect and correct quality issues before they impact outputs, not after. 

5. Close the loop with feedback  Feed model performance metrics back into your data processes so the system improves over time. 

   
   

By treating data quality as the foundation — not a post‑launch clean‑up exercise — you set your AI up for accuracy, efficiency, and scalability. The best AI projects run on a clean, governed, and continuously monitored data pipeline.