Establishing provenance by documenting the data's origin: Applying data governance, which practice most
Provenance documents the origin and chain of custody of data, addressing the unknown source of a purchased dataset.
The question
A team buys a labeled dataset from a broker but cannot say where the underlying records originated or whether they were lawfully collected. Applying data governance, which practice most directly addresses this gap before the data is used for training?
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- Establishing provenance by documenting the data's origin and chain of custody so its source and collection lawfulness can be verified. ✓Correct: provenance documents where data came from and how it changed hands, which is exactly what is missing for the purchased dataset.
- Running interpretability analysis on the trained model so the influence of the purchased data on its final predictions can be clearly explained.Plausible but wrong: interpretability explains model behavior after training but does not establish where the data came from.
- Applying data minimization so only the fields strictly needed for the training task are retained from the purchased dataset.Plausible but wrong: minimization reduces the data used but says nothing about verifying its origin or lawful collection.
- Encrypting the purchased dataset at rest so the confidentiality of the records is protected while they sit in the training store.Plausible but wrong: encryption protects confidentiality but cannot answer where the records came from or whether collection was lawful.
The trap
Addressing an origin-and-lawfulness gap with output analysis or security controls that never establish provenance. How to remember it
Provenance documents the origin and chain of custody of data, addressing the unknown source of a purchased dataset.
How many of these would you get right?
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