Data Governance Strategies
What data governance is
The story: A library doesn't just store books. It decides which books to acquire, how to catalog them, who can borrow rare ones, how long to keep old newspapers, and when to recycle damaged copies.
In AI/AWS terms: Data governance for AI manages the data lifecycle from collection and storage to use and security.
For the exam: Data governance covers data from collection through storage, use, security, and disposal.
Strategies
The story: How a well-run library works:
- Books are checked for missing pages, and each one records where it came from and every repair it's had.
- Rare books sit in a locked room, and the library has a plan if one is stolen.
- Every book is labeled and cataloged, with rules on how long to keep newspapers and when to recycle them, plus backup copies of important records.
- The library makes sure its collection represents everyone fairly, and trains staff on this.
- A library board sets policy, with named people who look after, own, and physically store each collection.
- Libraries lend to each other through agreements, without giving up ownership of their books.
In AI/AWS terms:
- Data quality and integrity: quality standards, validation and cleansing, and lineage and provenance (where data came from and how it changed).
- Data protection and privacy: privacy policies, access controls, encryption, and breach response.
- Data lifecycle management: classify and catalog data, set retention and disposal policies, back up and recover.
- Responsible AI: frameworks for bias, fairness, transparency, and accountability, plus monitoring and team training.
- Governance structures and roles: a data governance council, and defined data stewards, owners, and custodians.
- Data sharing and collaboration: sharing agreements, and data virtualization or federation that keeps ownership intact.
For the exam: Lineage and provenance = where data came from and how it changed. Stewards, owners, and custodians are governance roles.
Concepts to know
The story: Library terms: the life of a book from purchase to recycling; the borrowing log; which building the rare books are physically kept in (some countries require it to be local); noticing that people suddenly borrow very different books than last year; studying borrowing statistics; and how long to keep old newspapers.
In AI/AWS terms:
| Library idea | Concept | Meaning |
|---|---|---|
| Life of a book | Data lifecycle | Collection, processing, storage, consumption, then disposal or archiving |
| Borrowing log | Data logging | Recording inputs, outputs, performance metrics, and system events |
| Which building | Data residency | Where data is physically stored and processed: privacy regulations, sovereignty, proximity to compute |
| Borrowing habits changing | Data monitoring | Watching quality, anomalies, and data drift (input distribution changing over time) |
| Borrowing statistics | Data analysis | Statistics, visualization, and exploratory data analysis (EDA) |
| How long to keep | Data retention | How long to keep data: regulations, retraining needs, storage cost |
Photocopying, sorting returns in bulk, and running the checkout desk are daily operations, not library policy. In the same way, transcription, batch processing, and OLTP are processing techniques, not governance strategies.
For the exam: Where data physically lives = data residency. Inputs shifting over time = data drift. Transcription, batch processing, and OLTP are not governance strategies.