SECAI Core · phase 3 of 15
Data profiling
Determining the volume of data, the types of data and quality of the data.
The Explain card
- Plain English
- Data profiling is taking stock of a dataset: how much there is, what types it contains, and how good the quality is, before you build anything on it.
- Example
- Before training on a log archive, an engineer profiles it and discovers a month of missing records, three timestamp formats and unmasked credit card numbers.
- Why it matters
- Profiling finds sensitive fields that should never reach a model, and it establishes a baseline so that future poisoning or drift shows up as a change in the profile.
- Hook
- Inventory the warehouse before you start cooking with what is in it.
Where it sits in the deck
Phase 3: Data Fundamentals: Types, Pipelines, and Preparation
Models are only as good as their data — understand the raw material and the engineering that shapes it before covering how it flows or breaks.
