The hottest Normalization Substack posts right now

Develop intellectual curiosity and explore different solutions in data transformations and visualizations.
Always verify and normalize data to avoid base rate bias and misleading conclusions.
Consider both aggregating and disaggregating data to reveal different insights in visualizations.

Normalization structures data to reduce duplication and ensure integrity.
Goals of normalization include eliminating redundancy, minimizing data mutation issues, and protecting data integrity.
Denormalization introduces redundancy strategically to improve read performance, useful for reporting, analytics, and read-heavy applications.

In minimal modeling, links are defined with two anchors, not three.
Using two-way links can model examples effectively without the need for 3-way links.
Links in minimal modeling can introduce confusion when sentences in natural language aren't validated as actual links.

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Data modeling involves the choice between normalizing data and using denormalized data, each with its own strengths and tradeoffs.
Normalized data leads to less data duplication and easier data updates, but may result in challenges with historical data and performance.
CIAM systems, along with IAM and directory systems, normalize user data to centralize customer information, providing benefits like easy querying and centralized authentication, but also introducing challenges like session handling and updating data across systems.