The 6 dimensions of data quality explained with real-world cases
When someone says "the data is of poor quality," they are saying very little. Data can fail in very different ways: be incomplete, be incorrect, contradict each other, arrive late, have the wrong format or be duplicated. Each of these failures is a different dimension of quality, requires different measurement and generates a different business impact. Understanding the six is the first step to building a data quality programme that solves real problems.
Why distinguishing the dimensions matters
An overall quality rate of 94% can hide very different realities. A domain with 94% completeness and 100% accuracy has a manageable problem: there are empty fields that can be recovered. A domain with 100% completeness and 94% accuracy has a more serious problem: all fields have a value, but one in sixteen values is incorrect, and that is much harder to detect and correct.
The six data quality dimensions are the industry standard according to DAMA-DMBOK and the implicit reference of Article 10 of the AI Act when it requires that training data be "relevant, representative, free from errors and complete as far as possible." Each of those words points to a specific dimension.
1. Completeness: the most visible problem and the most ignored
Completeness measures whether all required values are present. It is the easiest dimension to detect automatically — a null field is a null field — and paradoxically one of the most ignored in quality programmes because it is perceived as a minor problem.
What it measures exactly
There are three levels of completeness. Field completeness measures what percentage of records have a value in a specific field. Record completeness measures what percentage of mandatory fields have a value in a specific record. Entity completeness measures whether all expected records for an entity exist.
Real-world impact cases
In an e-commerce system, 8% of order records have the shipping address field empty. The data is operationally correct during the checkout process because the address is extracted in real-time from the customer profile. But when that order is exported to the logistics system, which expects the address embedded in the record, 8% of orders fail silently.
How to measure it and what threshold to set
In dbt: not_null test per field. In Soda: missing_count and missing_percent. Threshold depends on the field: 100% for primary and foreign keys, ≥ 99% for critical business fields, ≥ 95% for optional relevant fields.
2. Accuracy: the most costly failure and the hardest to detect
Accuracy measures whether a data value correctly reflects the reality it purports to represent. It is the most critical dimension for decision quality and the hardest to measure automatically, because it requires knowing the reality to compare the data against.
What it measures exactly
A data item can be present, have the correct format and be technically valid and still be inaccurate. The weight recorded as 75 kg when the actual weight is 82 kg is complete, valid and exactly incorrect data.
How to measure it and what threshold to set
The most effective approach is validation against authorised sources: master reference tables, official records (tax ID, IBAN, postcodes) or designated record systems. For fields without an external source of truth, business rules (expected ranges, historical distributions) are the best approximation. Reference threshold: ≥ 98%.
3. Consistency: when the data says different things depending on where you look
Consistency measures whether a data item does not contradict other data in the same system or in related systems. It is the dimension that most frequently generates loss of trust in data, because its failures are visible in meetings: the same KPI gives different results depending on which system it is extracted from.
What it measures exactly
There are two types of inconsistencies. Internal inconsistencies occur within the same record: the country is "ES" but the telephone prefix is "+1", or the cancellation date is earlier than the creation date. External inconsistencies occur between systems: the CRM records 45,000 active customers, the billing system records 43,200 and the data warehouse shows 46,800.
How to measure it and what threshold to set
In dbt: relationships tests and custom SQL tests validating conditions between fields. In Great Expectations: expect_column_pair_values_to_be_equal for consistency between fields. Cross-system inconsistencies require cross-comparisons in the integration pipeline. Threshold: ≥ 99%.
4. Timeliness: correct data that arrives late is useless data
Timeliness measures whether the data is available when needed. It is the most frequently ignored dimension in quality programmes because it is perceived as an infrastructure problem, not a quality one. Mistake: timeliness is a quality dimension as relevant as accuracy in contexts where decisions depend on real-time data or very short time windows.
How to measure it and what threshold to set
Monitor the timestamp of the last update of each critical table and compare it with the agreed SLA. In dbt, a custom test that alerts if max(updated_at) < current_timestamp - interval 'X hours' is sufficient to detect update delays. Threshold depends on the domain: minutes for operational data, hours for analytical data, days for master data.
5. Validity: the rules the data must meet before it is useful
Validity measures whether a data value complies with the format, range and domain rules defined for that field. It is the easiest dimension to implement automatically and the one that shows fastest results in a quality programme starting from zero.
How to measure it and what threshold to set
In dbt: accepted_values, expression_is_true for ranges, regex tests with custom macros. In Great Expectations: expect_column_values_to_match_regex, expect_column_values_to_be_between. Reference threshold: ≥ 99.5% for critical business fields, 100% for keys and identifier fields.
6. Uniqueness: duplicates that cost more than they seem
Uniqueness measures whether there are duplicate records where there should not be. It is the quality problem with the highest direct economic impact and the one most frequently discovered late: when duplicates have been in the system for months or years and have contaminated analyses, models and decisions.
How to measure it and what threshold to set
For exact duplicates: unique test in dbt on the natural key. For fuzzy duplicates: tools like Splink (open source) or IBM MDM (enterprise) with Levenshtein or Jaro-Winkler algorithms. The exact duplicate threshold should be 0% for primary keys and < 0.5% for business attribute combinations.
Summary: the 6 dimensions at a glance
| Dimension | Question it answers | Reference threshold | dbt tool |
|---|---|---|---|
| Completeness | Are all necessary values present? | ≥ 99% | not_null |
| Accuracy | Does the value reflect reality? | ≥ 98% | Custom test vs source |
| Consistency | Are there no internal or cross-system contradictions? | ≥ 99% | relationships, SQL custom |
| Timeliness | Does the data arrive when needed? | Per domain SLA | Test on max(updated_at) |
| Validity | Does the value meet format, range and domain rules? | ≥ 99.5% | accepted_values, regex |
| Uniqueness | Are there no duplicates? | 0% for PK; < 0.5% for natural key | unique |
The dimension the AI Act adds: representativeness
The six standard dimensions cover data quality in operational and analytical environments. The AI Act implicitly adds a seventh dimension for training datasets: representativeness.
A dataset can be complete, accurate, consistent, timely, valid and duplicate-free and still train a biased model if it does not adequately represent the distribution of the population on which the system will operate in production. Article 10.4 of the AI Act requires taking into account "the necessary statistical characteristics, including the representation of the persons or groups on which the system will operate." Measuring and documenting that representativeness is a regulatory obligation.
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