normalization
What It Means
Normalization is the process of standardizing data to make it consistent and usable across systems. In databases, it means organizing information to eliminate duplicate data and ensure accuracy. In AI/ML, it means converting different data ranges into a common scale so algorithms can process them effectively.
Why Chief AI Officers Care
Poor normalization leads to inconsistent AI model performance, unreliable analytics, and increased storage costs from duplicate data. It's essential for ensuring AI systems can accurately compare and process data from different sources, and for maintaining data quality standards that regulators increasingly scrutinize.
Real-World Example
A retail company combining customer data from their website (ages 18-65), mobile app (satisfaction scores 1-10), and stores (purchase amounts $5-$5000) must normalize these different scales before feeding them into their recommendation AI, otherwise the algorithm will incorrectly weight the purchase amounts as vastly more important than age or satisfaction.
Common Confusion
People often think normalization is just about cleaning messy data, but it's actually about mathematical standardization that ensures fair comparison between different types of measurements. It's also confused with data validation, which checks if data is correct rather than making it comparable.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare, normalization is critical for interoperability across EHR systems, ensuring patient data like lab values,...
Finance: In finance, normalization is critical for risk management and regulatory reporting, where data from multiple sources (tr...
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Includes:
- 6 industry-specific applications
- Relevant regulations by sector
- Real compliance scenarios
- Implementation guidance
Technical Definitions
NISTNational Institute of Standards and Technology
"Conceptual procedure in database design that removes redundancy in a complex database by establishing dependencies and relationships between database entities. Normalization reduces storage requirements and avoids database inconsistencies."Source: OECD
"The process of converting an actual range of values into a standard range of values, typically −1 to +1 or 0 to 1"Source: aime_measurement_2022, citing Machine Learning Glossary by Google
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