Data Quality Dimensions
Completeness, accuracy, timeliness, consistency, validity and uniqueness as testable claims.
5 to work through
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beginner Multiple choice
A supplier table has 12% of rows with a blank tax identifier and 3% where the identifier is present but does not match the registry. Both are called data quality issues in the same report. Why does separating them change who is asked to fix them?
2 min answer -
intermediate Multiple choice
A nightly freshness check alerts when a table's daily row count deviates more than 20% from the previous day. Volume follows a weekly pattern: weekends run about 40% below weekdays. Roughly how many alerts a month does this produce before anything is actually wrong, and what should replace it?
2 min answer -
intermediate
A team wants to measure data quality. Which dimensions matter, and which tests actually catch problems?
2 min answer -
intermediate
You are setting data quality thresholds for a dataset with five consuming teams. How do you decide the numbers?
1 min answer -
advanced
A marketplace's seller metrics look wrong but every pipeline succeeded and every test passed. How do you diagnose it?
1 min answer
3 terms in this topic
Data Quality Dimensions
The standard axes along which data quality is measured, which turn a vague complaint into a testable assertion.
metricQuality Dimension Threshold
The stated numeric level at which a dataset is fit for its purpose on a given quality dimension, plus what happens when it is not met.
practiceSeasonality-Aware Threshold
Setting a data quality alarm against the comparable period rather than the previous one, so that a business's own weekly and monthly rhythm does not …
Neighbouring topics
Data Governance & Semantics
General material on ownership, meaning, quality and control of data at enterprise scale.
Data Mesh
Domain ownership, data as a product, self-serve platform, and federated governance.
Data Products
A dataset with an owner, an interface, an SLO, and consumers who can rely on it.
Data Contracts
Producers committing to schema, semantics and freshness, and breaking builds when they do not.
Data Catalog
Discovery, ownership and technical metadata, and why catalogues go stale.
Business Glossary
Agreeing what a term means before arguing about which number is right.
Semantic Layer
Metric definitions held once and served to every tool that asks.
Master Data Management
One authoritative record for a customer or product across systems that each have their own.
Reference Data
Code lists, hierarchies and currencies — small, shared, and quietly load-bearing.
Data Observability
Freshness, volume, schema and distribution monitoring for pipelines that fail silently.
Data Stewardship
The operating model that makes ownership a role rather than a slide.
Data Access Models
Role, attribute and purpose-based access over analytical data, and how they compose.
Row & Column-Level Security
Restricting slices of a table rather than the whole table, and where it is enforced.
Tokenisation & Masking
Dynamic masking, deterministic tokens, and preserving joinability without exposure.
Retention & Purge
Deleting from an append-only estate, and proving the deletion happened.
Data Sharing & Clean Rooms
Collaborating on data neither party may hand over, with computation as the interface.
Sensitivity Labelling
Propagating a classification through joins and derived tables so controls follow the data.
BI Governance
Dashboard sprawl, certified reports, and the number the board is allowed to see.
Self-Service vs Governed
Letting analysts move fast without four teams reporting four different revenues.