[Avg. reading time: 5 minutes]
Data Quality & Governance
Data Quality is simple
Can you trust this data to make a decision?
If not, it’s useless.
What matters
- Accuracy : Is it correct?
- Completeness : Is anything important missing?
- Consistency : Does it match across systems?
- Timeliness : Is it fresh or stale?
- Relevance : Do we even need this data?
How you improve it (practical, not theory)
- Profile data : find issues early
- Validate at entry : stop bad data upfront
- Clean regularly : fix what slipped through
- Track metrics : monitor trends over time
- Standardize core data (MDM) : one version of truth
Data Governance (Who controls the data)
Data governance is not a document.
It’s control.
Who owns data, who can use it, and how it’s protected.
What it includes
- Policies : rules for storing and sharing data
- Ownership : someone accountable (data stewards)
- Security : who can access what
- Compliance : laws you cannot ignore
- Metadata : context (where data came from, how to use it)
Laws you can’t ignore
You don’t need to memorize all of them.
Just understand the pattern: protect user data or pay heavily.
- GDPR (EU) : strictest, global impact
- CCPA (California) : consumer rights
- HIPAA (US) : healthcare data
GDPR (the one everyone cares about)
- Consent : you must ask clearly
- Access : users can see their data
- Delete : users can ask to remove it
- Portability : users can take their data
- Breach reporting : within 72 hours
- Fines : up to 4% of global revenue
Summary
- Data Quality = Is the data good?
- Data Governance = Are we allowed to use it?