The 6 Data Quality Dimensions Explained (With Examples)

The short answer. The six core data quality dimensions are completeness, accuracy, consistency, validity, uniqueness, and timeliness. Completeness asks if the data is all there; accuracy if it is correct; consistency if it agrees across systems; validity if it follows the rules; uniqueness if there are no duplicates; and timeliness if it is current. Together they give you a structured way to measure and talk about whether data is fit for use.

When people say data is "bad", they usually mean it fails in one specific way: something is missing, or wrong, or duplicated. The data quality dimensions give names to those failure modes, which is what makes quality measurable rather than a vague complaint. Here are the six that matter, with examples.

1. Completeness

Completeness asks whether all the data that should be present actually is. A customer record missing a postal code, or an order missing a delivery date, is incomplete. Completeness is often the first dimension to check in a migration, because fields that were optional in an old system may be mandatory in the new one, and the gaps that were tolerated for years suddenly block the load.

2. Accuracy

Accuracy asks whether the data correctly reflects the real world. An address that is complete but wrong is accurate in form but not in fact. Accuracy is the hardest dimension to measure automatically, because confirming it often requires comparison against a trusted reference or a real-world check, but it is also among the most important, because inaccurate data misleads every decision built on it.

3. Consistency

Consistency asks whether the same fact agrees wherever it appears. If a customer's credit limit is one value in the sales system and another in finance, the data is inconsistent. Consistency problems are common where the same information lives in multiple systems, and they are a major reason reports from different departments fail to reconcile.

4. Validity

Validity asks whether data follows the required format and rules. A date in the wrong format, a code that is not in the allowed set, or a value outside a permitted range all fail validity. Validity is the most straightforward dimension to check automatically, because the rules are explicit, which makes it a good place to start when building quality checks.

5. Uniqueness

Uniqueness asks whether each real-world thing is represented once, without duplicates. The same customer entered three times, or a material created under slightly different descriptions, fails uniqueness. Duplicates are especially damaging because they scatter transactions across records that should be one, inflating stock, splitting spend, and confusing reporting. Catching them often requires fuzzy matching, because duplicates are rarely identical.

6. Timeliness

Timeliness asks whether the data is current enough to be useful. Data that was accurate when entered can become stale: addresses change, prices update, statuses move on. Timeliness matters most for data that drives time-sensitive decisions, where acting on yesterday's picture leads to today's mistake.

Using the dimensions together

A record can be complete, valid, and unique, and still be wrong. That is why you measure across all six.

No single dimension tells the whole story. A record can pass completeness, validity, and uniqueness while still being inaccurate or out of date. This is why the dimensions are used together, as a structured checklist that catches different kinds of problem. In practice, you attach a metric to each dimension for the data that matters, set thresholds by business impact, and track the results on a scorecard. The dimensions turn "is our data good?" into six specific, answerable questions.

How to prioritise the dimensions

Although all six dimensions matter, they do not all matter equally for every piece of data, and trying to maximise all of them everywhere wastes effort. The practical approach is to weigh the dimensions by the business impact of the data in question. For data feeding a payment, accuracy and validity are critical. For data driving a migration, completeness and uniqueness often come first. For a real-time operational decision, timeliness may dominate.

By asking which dimensions carry the most risk for a given dataset, you focus your measurement and your rules where they protect the most value, rather than spreading attention evenly across data that does not need it. The dimensions are a checklist, but the weighting of that checklist should reflect what the data is actually used for.

The dimensions in a migration

A migration is where the dimensions become very concrete, because the target system enforces them. Completeness matters because fields optional in the old system may be mandatory in the new one. Validity matters because values must conform to the target's rules. Uniqueness matters because duplicates carried across inflate and confuse the new system. Consistency matters because the same entity must be represented coherently. In effect, a migration is a hard test across every dimension at once, which is why data that passed for years in a legacy system suddenly reveals its weaknesses when held to the new model's standards. Measuring across the dimensions before a migration is how you find and fix those weaknesses before they block the load.

A shared language for data quality

Beyond measurement, the dimensions give an organisation a shared language for talking about data quality, which is valuable in itself. Without them, conversations about data tend to be vague and circular: one person says the data is fine, another points to a bad experience, and nothing gets resolved. With the dimensions, a discussion can be specific: this field fails completeness, that value fails validity, these records fail uniqueness. That precision turns arguments into problems that can be assigned and fixed. It also helps the business and technical teams communicate, because a completeness score means the same thing to both. The dimensions are as useful for how they structure the conversation as for how they structure the measurement.

How deKorvai helps

deKorvai profiles data and applies configurable business and technical rules to test it across these dimensions, reporting the results as real-time scorecards with trend insight. Its rules cover completeness, accuracy, and consistency checks, and its duplicate detection uses fuzzy matching to catch near-duplicates that a simple uniqueness check would miss. Because the scoring is continuous, quality across all six dimensions is watched over time rather than audited once, which is what lets a team see not just where their data stands but which way it is heading.

Key takeaways

• Six dimensions: completeness, accuracy, consistency, validity, uniqueness, timeliness.
• Each names a different failure mode, which makes quality measurable.
• No dimension is enough alone; use them together as a checklist.
• Attach metrics and thresholds, and track on a scorecard.

Frequently asked questions

What are the six data quality dimensions?

Completeness (is the data all there), accuracy (is it correct), consistency (does it agree across systems), validity (does it follow the rules), uniqueness (are there no duplicates), and timeliness (is it current). Together they give a structured way to measure whether data is fit for use.

Which data quality dimension is most important?

It depends on the use, but accuracy is often the most consequential because inaccurate data misleads every decision built on it, and it is the hardest to check automatically. Completeness and uniqueness tend to matter most in migrations. In practice you weigh dimensions by business impact.

What is the difference between validity and accuracy?

Validity asks whether data follows the required format and rules, which is easy to check automatically. Accuracy asks whether it correctly reflects the real world, which is harder to verify. A value can be valid in form, a correctly formatted date, but inaccurate in fact.

How do you measure the data quality dimensions?

Attach a concrete metric to each dimension for the data that matters, such as the percentage of records with all mandatory fields for completeness. Set thresholds by business impact, and track the results on a scorecard that shows both current state and trend.