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A dataset is good enough for AI only when it is fit for the specific job you want it to do. Start with the decision, users, population, operating conditions and consequences of mistakes; then set evidence-based quality requirements for that context. There is no universal data-quality score or cutoff that makes a dataset safe for every model.
Evaluate the data in six layers—completeness, uniqueness, consistency, timeliness, validity and accuracy—then add checks for representativeness, relevance, sufficiency, provenance and lawful use. Separate training data from evaluation data, test performance for important subgroups, and keep monitoring after each update or transformation.
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Start with the AI use case, not the spreadsheet
Before calculating a single percentage, write a short purpose statement. It should identify:
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- Users and decision: Who will rely on the output, and what action will follow?
- Population: Which people, organizations, products, documents or events should the data represent?
- Operating conditions: Where and when will the system run, and what input quality, language, device, channel or workflow will it encounter?
- Time period: Which historical period is relevant, and how quickly can the underlying reality change?
- Error consequences: Which false positives, false negatives or other failures are tolerable, and which could cause financial, safety, legal or discriminatory harm?
The same records can be suitable for one purpose and unsuitable for another. A complete customer table might support aggregate reporting but be unfit for a fraud model if it lacks disputed transactions. A collection of clean product photos might train a classifier for studio images but fail in a warehouse with glare and occlusion. Define the intended use and population before deciding what good enough means.
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Turn the purpose into measurable requirements
For each required field or record, state the minimum evidence you need. Examples include a maximum age for events, an allowed range for measurements, a required proportion of labels, or a minimum number of examples for a rare class. Explain why each threshold exists and who approved it. A threshold tied to a high-consequence decision should be stricter, or should trigger human review, than one used for exploratory analysis.
Do not combine unrelated dimensions into one pass score. A dataset can be 99% complete yet systematically wrong for a minority group, or perfectly formatted yet collected without permission. Keep the individual measures visible so a decision-maker can see the trade-offs.
Profile the six core dimensions
Run a profile on the whole dataset and on meaningful slices such as class, geography, time period, source system and collection channel. Record the numerator, denominator, query or rule used, date of measurement and any exclusions.
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Completeness: are the needed records and values present?
Measure both record coverage and field coverage. Ask whether every expected entity or event appears, and whether required values are populated for the records that do appear. Report the pattern of missingness, not just an overall percentage.
Missing values may be intentional, unavailable, refused, lost in a pipeline or absent because a process never captured them. Those causes have different implications. A medical field left blank because a test was not clinically indicated is different from a field that disappears only for one clinic. Keep an explicit missing or not-applicable code when that distinction matters; do not silently convert every absence to zero.
Uniqueness: does each entity appear the expected number of times?
Define the key that should be unique—such as a person, account, order or document—and test duplicates against it. Near-duplicates can arise from spelling, transliteration, changed addresses, repeated uploads or merged systems. Document the matching rule and what happened to suspected duplicates; deleting them without preserving lineage can remove genuine repeat events.
The UK Government Data Quality Framework gives an illustrative calculation: 500 distinct students among 501 records equals 99.8% uniqueness. This is an explanatory example, not an AI threshold or a population statistic.
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Consistency: do values agree?
Check relationships within a record, across records and between source systems. Examples include an end date preceding a start date, a status that conflicts with a payment flag, totals that do not equal their parts, or two systems assigning different birth dates to the same entity. Normalize units, time zones, encodings and category names before comparing them, while retaining the original values for audit.
Consistency is not the same as truth. Two databases can repeat the same erroneous value. Compare conflicts with a designated source of record or with domain rules, and preserve the decision used to resolve them.
Timeliness: is the data current for the decision?
Measure the age of each record at the moment it will be used, the lag between an event and its arrival, and the update frequency. A dataset collected last month may be stale for fraud detection but timely for a demographic study. Conversely, waiting for a more complete batch can make a rapidly changing signal less useful.
Check for gaps, backfills and clock errors. If labels arrive later than features, ensure the training process does not accidentally use information that would not have been available at prediction time.
Validity: do values obey the allowed rules?
Validate data types, formats, ranges, enumerations, units and conditional rules. Reject or quarantine impossible dates, malformed identifiers, invalid coordinates and values outside a physically plausible range. A value can be validly formatted while still being wrong in the real world: a date such as 2035-01-01 may pass a parser even when the event happened in 2025.
Version validation rules with the data. Changing a category list or measurement unit can alter historical results, so retain the rule set used for each profile.
Accuracy: does the value describe reality?
Accuracy requires a trusted reference, a second measurement, a reconciliation process or informed domain review. Sample records for manual verification when a full audit is impractical, and report the sampling method and confidence limits. Distinguish transcription errors from outdated but once-correct values.
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Validity and accuracy answer different questions: validity asks whether a value follows the rules; accuracy asks whether it is true. Never claim real-world correctness from a format check alone.
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Produce a missingness report by field and by relevant subgroup. Include counts, percentages, first and last occurrence, and whether missingness clusters by source, time, location, device, language or outcome. Compare records with and without a value to see whether the absence itself predicts the target or reflects access to the process that generated the data.
Classify each important problem as accidental, systematic, intentional or unknown. Then choose a remedy that preserves meaning:
- Correct a verified transcription or unit error and retain the original plus an audit note.
- Obtain the value from an authoritative source when the collection process omitted it.
- Use an explicit missing category when absence carries information.
- Impute only with a method justified by the use case, and mark imputed values so they are not mistaken for observations.
- Exclude records only when the exclusion rule is defensible and its effect on representation is measured.
Re-profile after every remediation. Filling blanks can improve completeness while introducing bias or artificial certainty; deduplication can improve uniqueness while erasing legitimate repeated events.
Check whether the data represents the people and conditions of use
Representativeness is an AI-specific question: does the dataset cover the cases on which the model will be used, in the proportions and conditions that matter? Compare the data with the intended population across relevant attributes, locations, time periods, languages, product types, severity levels and operating environments. Document known exclusions rather than implying broad coverage.
Look beyond the overall average
Calculate label and feature coverage for important groups, then evaluate model errors separately for those groups when legally and ethically appropriate. Overall accuracy can hide poor performance for a small but consequential population. Disaggregated evaluation can reveal that a data-cleaning rule, sensor, annotator pool or sampling decision affects one group more than another.
Set representativeness requirements from the consequences of error. A small group may need targeted sampling or a separate acceptance criterion even when it is a small share of production traffic. Do not infer sensitive attributes from unreliable proxies merely to produce a fairness table; use a lawful, documented measurement plan.
Match operational conditions
Compare training examples with production conditions: camera quality, background, network connectivity, document layout, accent, seasonal effects, workflow changes and adversarial behavior. A random split of historical records does not guarantee coverage of future conditions. If the deployment population changes, treat that as a new suitability assessment.
Establish provenance, governance and lawful use
A technically tidy table is not evidence that the data is appropriate or legal to use. For every dataset, record:
- Owner and accountable quality lead.
- Original source, collection method, sampling frame and consent or other legal basis where relevant.
- Transformations, joins, filters, labeling instructions, deduplication and imputation steps.
- Schema, definitions, units, code lists, metadata and known limitations.
- Creation date, last update, expected refresh schedule and retention period.
- License, privacy restrictions, intellectual-property terms, access controls and acceptable-use boundaries.
- Profile results, unresolved defects, incident history and the version used for each model.
Preserve lineage from a model feature or label back to its source record. If a contractor, public repository or partner supplied the data, make responsibilities for corrections and deletion explicit. Recheck permissions when the use changes; authorization for analytics does not automatically cover automated decisions or model training.
Prepare training and evaluation data correctly
Keep evaluation examples separate from training data when you want to measure generalization. Deduplication, augmentation, preprocessing and feature engineering must not allow test information to leak into training. Maintain a locked test set for final assessment and a separate validation set for tuning, with access limited to the people who need it.
The evaluation set must represent the target population and operating conditions, including difficult and rare cases that matter to the decision. For document extraction from international forms, evaluation guidance calls for documents from every targeted country; testing only one country cannot establish performance for the others.
Record the construction logic, class distribution, time boundary and exclusion rules for each split. Test temporal generalization when deployment predicts future events, and test by site or source when the model will encounter new sources. Report confidence intervals or uncertainty where sample sizes permit, and show errors by relevant group rather than publishing only one aggregate metric.
Understand the key trade-offs
| Decision | What improves | What can worsen | How to decide |
|---|---|---|---|
| More complete data | Coverage and statistical power | Added noise, stale records or biased imputation | Trace missingness causes and verify whether new records match the target population. |
| Faster, fresher data | Responsiveness to current conditions | Less time for validation, late labels or unstable distributions | Balance latency against the cost of acting on an unverified value. |
| Strict validity rules | Predictable formats and safer pipelines | Discarded edge cases and reduced coverage | Quarantine exceptions, review their meaning and monitor who is removed. |
| More accurate labels | Clearer learning signal | Higher annotation cost and slower release | Prioritize labels whose errors change the decision or harm a group. |
| Overall performance | Simple headline metric | Hidden subgroup failures | Pair aggregate results with disaggregated metrics and minimum group requirements. |
| Larger test set | More precise estimates and rare-case coverage | More collection cost and leakage risk | Size it for the decisions and groups that must be evaluated, then lock it. |
Use illustrative percentages carefully
The UK Government Data Quality Framework illustrates completeness with 294 recorded emergency-contact telephone numbers among 300 students: 294 ÷ 300 = 98%. It illustrates uniqueness with 500 distinct students among 501 records: 500 ÷ 501 = 99.8%. Both figures explain how to calculate a dimension; neither is a recommended acceptance level for an AI dataset. Your required level depends on the task, population and consequences of error.
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Turn the assessment into a decision
At the end of profiling, write a suitability statement that a model owner and risk owner can sign. Include the intended use and population, measured results by dimension, representativeness findings, provenance and legal review, evaluation-split design, open risks, thresholds and the remediation owner.
| Decision | Use when | Required action |
|---|---|---|
| Approve | Evidence meets the use-specific requirements and remaining risks are accepted. | Record the version, approval date, owners and monitoring triggers. |
| Approve with controls | Known limitations are bounded and can be managed with human review, restricted scope, subgroup monitoring or a shorter refresh cycle. | Document each control, trigger and person responsible for intervention. |
| Hold | Important evidence is missing, evaluation does not match deployment, or a defect has an unclear impact. | Collect evidence or remediate before training or release. |
| Reject for this use | The population, provenance, rights or quality cannot support the decision, even if the data may suit another purpose. | Prevent reuse under the rejected purpose and record the reason. |
Make quality a lifecycle responsibility
Quality can change when data is refreshed, moved between systems, transformed, relabeled or reused for a different population. The UK Government Data and AI Ethics Framework states, “Maintaining data quality is a continuous process.” Treat that as an operating requirement, not a one-time certification.
Automate repeatable checks for schema, null rates, duplicates, ranges, distribution shifts, referential integrity and freshness. Send alerts to an owner who can interpret context; an alert without an accountable response is not governance. Keep dashboards linked to the exact dataset and rule versions so a later reader can reproduce the result.
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Frequently Asked Questions
How do I know if my data is good enough for AI?
Define the intended task, population, operating conditions and consequences of error first. Then compare measured quality and representativeness with thresholds justified for that use; there is no universal pass percentage.
Does missing data make a dataset unusable?
Not automatically. Determine why values are missing, whether missingness is systematic, and how each remedy changes coverage and bias. Some uses can tolerate or model missingness; others require collecting the field.
What data quality checks should I run before training a model?
Profile completeness, uniqueness, consistency, timeliness, validity and accuracy; test representativeness and provenance; investigate error causes; and verify that evaluation data is separate and matches deployment conditions.
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Compare records, labels and operating conditions with the intended population across relevant groups, places and time periods. Evaluate errors disaggregated by those groups and document exclusions, uncertainty and any mitigation.
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