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A dataset is good enough for AI only when it is fit for a specific task, population and operating context. Define what the model will do and what errors could cost before setting quality checks; then profile the data, investigate missingness and representation, verify provenance and assess evaluation data separately. There is no universal quality score or threshold that makes a dataset suitable for every AI use.
Start with the AI use, not a generic quality score
Data quality means fitness for purpose. The same dataset might be useful for one task and unsuitable for another: a collection of past customer messages, for example, may support analysis of those messages but not necessarily represent the people or conditions a system will encounter after deployment. Quality checks should follow the intended use rather than produce a score detached from it.
Before profiling fields, write a short use statement that answers the following:
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- Users and affected people: Who will use the output, and who may be affected by the resulting decision?
- Population: Which people, objects, events or cases should the data represent?
- Decision context: Is the output advisory or does it trigger an action? What human review, if any, sits between the output and that action?
- Operating conditions: Where, when and under what conditions will the system be used?
- Data period: Which time period does the task require, and how quickly can the underlying facts change?
- Error consequences: Which errors matter most, who could be harmed, and what level of error is tolerable for this use?
Use those answers to choose relevant quality dimensions and to justify minimum requirements. A typo in a low-impact descriptive field and a wrong value that drives a consequential decision should not automatically receive the same treatment. The intended users also matter: analysts may need caveats and field definitions that a downstream automated process cannot infer.
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Do not treat a single pass mark as a general definition of “AI-ready.” A completeness figure, for instance, says nothing by itself about whether the present values are correct, representative or permitted for the intended use. Record why each requirement exists and what happens if it is not met.
Profile the dataset across six core dimensions
Profiling turns broad concerns into observations about actual records and fields. The UK Government Data Quality Framework uses completeness and uniqueness examples to explain how these measures can be calculated. It gives 294 recorded emergency contact telephone numbers among 300 students as 98% completeness, and 500 distinct students among 501 records as 99.8% uniqueness. The year of those examples is not confirmed; they are explanatory calculations, not recommended thresholds for AI datasets.
| Dimension | Question to ask | Useful check | What the result does not establish |
|---|---|---|---|
| Completeness | Are the expected records and required values present? | Compare received records with the expected coverage; count missing values by field and, where relevant, by group or period. | A populated field may still be wrong, outdated or irrelevant. |
| Uniqueness | Are records distinct when one record per entity is expected? | Identify duplicate entities or events using the identifiers and rules appropriate to the data, then document any deduplication. | Two similar records are not necessarily duplicates; the right rule depends on what a record represents. |
| Consistency | Do values agree within a record and across records or sources? | Compare fields that should agree, and check corresponding values across source systems. | Agreement does not prove the shared value is true. |
| Timeliness | Is the data current enough for the task and does it cover the right period? | Inspect date ranges, last-updated information and the interval between changes in the real world and updates to the dataset. | Newer is not automatically better if the task requires a different historical period or data is still being checked. |
| Validity | Do values meet expected formats, ranges and rules? | Check required formats, allowed values, sensible ranges and relationships between fields. | A value can be validly formatted yet describe the wrong person, event or outcome. |
| Accuracy | Do values correctly represent the real-world entities or events they claim to describe? | Where possible, compare with trusted references or domain knowledge and investigate discrepancies. | A tidy schema or high rate of valid formats is not evidence of real-world correctness. |
These dimensions are related but not interchangeable. A dataset can be complete yet inaccurate; values can be consistent across two sources that share the same upstream mistake; and a correctly formatted date can still be out of date for the use. In the same way, deduplication can improve uniqueness while accidentally removing legitimate repeated events if the definition of a record is unclear.
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For each check, note the population of records examined, the fields covered, the rule applied, the result and exceptions that remain. If the dataset is too large for manual review, automated profiling can count patterns and flag violations, but someone still needs to determine whether the rule is meaningful in context.
Investigate missing values and errors, not just their totals
A missing-value percentage is a starting point, not an explanation. Find out which fields and records are affected, whether absence is expected, and how the value became unavailable. Missingness can result from a field not being collected, a source system not supporting it, a failed transformation, a privacy or operational decision, or a process that captures some cases more reliably than others. The cause changes what the gap means for the AI use.
Look at missingness by relevant population groups, locations, time periods and operating conditions. A modest overall rate can conceal a serious coverage problem if a particular group or kind of case is systematically absent. Conversely, a field may be intentionally blank for cases where it does not apply. Marking intentional absence distinctly from accidental omission can help prevent those cases from being treated as equivalent.
- List required and optional fields, and explain why each is needed for the task.
- Count absent, invalid and conflicting values separately rather than combining them into one “bad data” total.
- Trace unexpected gaps to the collection or transformation step that introduced them.
- Check whether missingness changes over time or differs across relevant groups and conditions.
- Decide whether a gap requires correction, a documented limitation, a change to the task, or rejection of the dataset.
Apply the same reasoning to errors. Inspect examples and ask whether the error is isolated, repeated, concentrated in certain categories or introduced by a shared source or transformation. A single aggregate accuracy estimate can hide different error patterns. When an error could cause meaningful harm, determine which cases are affected and whether the dataset can support the proposed use without an effective mitigation.
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Representativeness is an AI-specific concern that goes beyond whether a table is complete. Compare the dataset with the target population and the operating conditions the system is expected to encounter. Consider relevant groups, locations, time periods, case types and environmental conditions. The comparison should be guided by the use statement; there is no single group breakdown that is appropriate for every task.
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Ask how the data came to include the cases it contains. Collection channels, eligibility rules, user behavior and historical processes can all shape which examples appear. A large dataset can still omit important cases. Likewise, a distribution that looks balanced overall does not prove that each subgroup or operating condition is adequately covered.
Document known bias and limits in terms that matter to users of the model. Describe which populations or conditions are less represented, which conclusions may not transfer, and what sorts of errors could follow. Where it is appropriate and lawful, examine data quality and model evaluation results separately for relevant groups rather than relying only on an overall average. Set any representativeness requirements in relation to the consequences of errors and the task—not as a universal quota.
Relevance and sufficiency also deserve explicit checks. Relevance asks whether the examples contain information connected to the task and reflect the kind of input the system will receive. Sufficiency asks whether there is enough appropriate data for the intended analysis or evaluation. A record count alone cannot answer either question: examples may be numerous but repetitive, unrelated to the target task or drawn from too narrow a set of conditions.
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Technical cleanliness does not establish that a dataset is appropriate or lawful to use. Establish its history and the conditions governing its use before treating it as a candidate for model development.
- Origin and collection: Identify who supplied or generated the data, where it came from and how it was collected. Clarify what each record represents.
- Ownership and authority: Identify who is responsible for the dataset and who can approve access or reuse.
- Transformations: Record cleaning, filtering, labeling, joins, deduplication, aggregation and other changes. Preserve enough information to understand how the current version was produced.
- Metadata and limitations: Keep field definitions, collection context, date coverage, known gaps and quality results with the dataset.
- Use constraints: Review licensing, privacy and intellectual property constraints, and whether the proposed AI use is acceptable under them.
- Maintenance: Find out when the dataset was last updated, how often it is expected to change and who is responsible for future quality checks.
These details affect interpretation as well as governance. A transformation may introduce inconsistent labels; a join may duplicate records; a filter may exclude cases important to the target population. If a dataset has been passed between teams, confirm that its documentation describes the version actually being assessed, not merely an earlier source.
Do not infer permission from access. If rights, privacy constraints or acceptable use are unclear, resolve that uncertainty with the responsible owner before using the data for training or evaluation.
Assess evaluation data separately from training data
Evaluation data is the basis for claims about how well an AI system performs. If it does not reflect the intended population and operational conditions, the results may not answer the question stakeholders care about. Check its coverage and quality independently, rather than assuming that a dataset suitable for training is automatically suitable for testing.
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When evaluating generalization, keep test examples out of training data. If examples overlap or information from the test set influences training, measured performance may not represent performance on genuinely unseen cases. Maintain a clear account of which data was used for training and which was reserved for evaluation.
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Include the range of inputs the system is intended to handle. Google Cloud’s documentation for document extraction gives a concrete example: when extracting names from international forms, the test set should include documents from all targeted countries. The broader lesson is to match test coverage to the stated deployment scope; a narrow test set cannot substantiate performance across conditions it does not contain.
- Confirm that evaluation examples correspond to the target population and use conditions.
- Check for overlap with training examples and record how the sets were separated.
- Review whether relevant groups and input types appear in sufficient variety for the conclusions being drawn.
- Where useful, inspect evaluation outcomes by relevant group or condition as well as in aggregate.
- State the limits of what the evaluation can support when important cases are absent or poorly represented.
Turn the assessment into a decision
Use a decision record rather than an unexplained “pass” label. For each dimension that matters to the task, write down the requirement, evidence, result, known exceptions and rationale for accepting or rejecting the risk. Distinguish blockers from limitations that can be managed. A dataset may be acceptable for a low-impact exploratory analysis but not for a consequential operational decision; the use statement should make that distinction visible.
| Decision status | When it may fit | What to record |
|---|---|---|
| Suitable for the stated use | Relevant requirements are met and remaining limitations are understood and acceptable for the intended context. | The use, checks performed, results, residual limits, approving owner and date. |
| Suitable with conditions | A manageable issue remains, and a specific control or restriction can keep use within acceptable bounds. | The condition, responsible person, remedy or safeguard, and how compliance will be checked. |
| Not suitable yet | A material gap, error, permission question or coverage problem remains unresolved. | The failing requirement, evidence, remediation needed and criteria for reassessment. |
| Not suitable for this use | The dataset cannot support the intended population, decision or operating conditions, or a necessary constraint rules out the use. | The reason and any narrower use, if one is supported by the evidence. |
For every threshold, state who chose it and why. The rationale should connect the measured issue to the model’s consumers, affected population and possible consequences—not to an assumed industry-wide standard. Where evidence cannot establish accuracy or coverage, make that uncertainty part of the decision instead of treating an unmeasured dimension as a pass.
Keep quality checks active through the data lifecycle
A dataset’s quality can change when it is updated, moved, transformed or reused for a new purpose. UK Government’s Data and AI Ethics Framework says, “Maintaining data quality is a continuous process.” Put that principle into practice by assigning an owner, retaining the rules and results, and deciding when checks must run again.
Reassess after a change that could affect what records mean or which cases are present: a new source, revised collection process, altered transformation, changed schema, new data period or different AI use. Automated checks can help detect missing fields, invalid values, duplicates or unexpected shifts, and a dashboard can make results visible. Alerts still need context and an accountable person who can investigate them; a changed count is a signal to interpret, not a quality verdict by itself.
- Document the baseline: Save the dataset version, use statement, definitions, checks, thresholds, results and known limitations.
- Assign ownership: Name the people responsible for data changes, quality review, decisions and remediation.
- Monitor relevant changes: Choose checks that can detect the risks identified for the use, rather than collecting metrics with no decision attached.
- Investigate and act: Trace alerts to their cause; correct the data, restrict the use, update the documentation or reassess suitability as appropriate.
- Re-evaluate on reuse: Repeat the purpose-and-risk assessment when the task, population or operating conditions change.
A quality review is most useful when it leaves a clear trail: what was assessed, against which use, by whom, with what result, and what happens next. That record lets future users judge whether the evidence still applies to the data version and AI purpose in front of them.
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