Data Quality Fundamentals

19 articles

Jun 11, 2026
What Is Adaptive Data Quality? (And Why Static Rules Aren't Enough)
Static data quality rules catch known problems. Adaptive data quality catches problems you never anticipated — by learning what 'normal' looks like for your data.
7 min read
Jun 11, 2026
How to Detect Data Distribution Shift Without Writing SQL
Distribution shift — when a categorical column gains new values or changes its frequency profile — is one of the hardest data quality issues to catch with static rules.
7 min read
Jun 11, 2026
What Is a Behavioral Anomaly in Data Quality?
A behavioral anomaly is a data quality issue that only becomes visible when you compare today's data against historical patterns — not against a fixed rule.
6 min read
Jun 11, 2026
5 Data Quality Issues That Static Rules Miss (But Behavioral Scoring Catches)
Static DQ rules are essential — but they have blind spots. Here are five data quality problems that slip through even well-configured rule sets and how behavioral scoring catches them.
8 min read
May 21, 2026
What Is Data Quality? A Complete Beginner's Guide
Data quality determines whether your data is fit for its intended purpose. Here's what it means, why it matters, and how to start improving it today.
3 min read
May 21, 2026
The 10 Dimensions of Data Quality Explained
The 10 dimensions of data quality give you a complete framework for measuring and improving your data. Here's what each one means in plain English.
3 min read
May 21, 2026
Data Validity: Ensuring Your Data Meets Your Business Rules
Data validity checks whether values conform to defined formats and business rules. Invalid data passes format checks but breaks logic downstream.
3 min read
May 21, 2026
Data Integrity: Keeping Relationships Between Your Data Correct
Data integrity ensures relationships between data points remain consistent and correct. When integrity breaks, related records point to nothing — or the wrong thing.
3 min read
May 21, 2026
6 Dimensions vs. 10 Dimensions of Data Quality: Which Framework Is Right for You?
DAMA uses 6 core dimensions. Other frameworks use 10 or more. Here's how to choose the right framework for your business without overcomplicating it.
3 min read
May 21, 2026
Data Conformity: Standardizing Formats Across Your Organization
Data conformity means values follow defined standards and formats. Non-conforming data creates import failures, join mismatches, and reporting chaos.
2 min read
May 21, 2026
Data Completeness: What It Is and Why It Matters
Data completeness measures what percentage of required fields have values. Low completeness silently breaks filters, segments, and reports.
3 min read
May 21, 2026
Data Consistency: The Silent Killer of Business Insights
Data consistency means the same fact is represented the same way across all systems. Inconsistency silently breaks every cross-system report you build.
3 min read
May 21, 2026
Data Timeliness: Why Stale Data Is Dangerous for Decision-Making
Data timeliness measures whether your data is current enough for its intended use. Stale data produces confident wrong decisions that are worse than no data.
3 min read
May 21, 2026
Data Precision: When Close Enough Is Not Good Enough
Data precision is the level of detail your data carries. Too little precision produces incorrect calculations; too much creates storage overhead without value.
2 min read
May 21, 2026
Data Accuracy: How to Measure and Improve It
Data accuracy measures how closely your data reflects reality. Inaccurate data produces wrong decisions, wasted spend, and broken trust.
3 min read
May 21, 2026
Data Uniqueness: How to Find and Eliminate Duplicate Records
Data uniqueness means each entity appears exactly once. Duplicates inflate counts, split engagement history, and cause customers to receive the same message twice.
3 min read
May 21, 2026
Data Quality vs. Data Governance: What's the Difference?
Data quality and data governance are related but distinct disciplines. Confusing them leads to solving the wrong problem. Here's how they fit together.
3 min read
May 21, 2026
What Makes Data Accurate? Understanding the Core Dimensions
Accuracy is one dimension of data quality — but multiple factors contribute to whether data is truly accurate. Here's the full picture.
3 min read
May 21, 2026
What Is a Data Quality Score and How Is It Calculated?
A data quality score is a single number that summarizes how fit your data is for use. Here's how it's calculated and what a good score actually looks like.
3 min read