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Clean data for the AI task you actually intend to run—not for an abstract idea of a “perfect” dataset. First establish what the data represents and how it was collected; then profile it, investigate defects before changing anything, apply documented corrections, and validate the result against the task. Formatting a table neatly cannot fix biased sampling, unreliable labels, or measurements that do not mean what a model assumes they mean.
What counts as good data for an AI task?
Data quality is fitness for a stated purpose. A dataset can be complete and consistently formatted yet be unsuitable for a particular model because it is stale, irrelevant, unrepresentative, or measured in a way that does not match the intended use. NIST’s research-data framework describes quality across dimensions including accuracy, completeness, currency, relevance, consistency, reliability, presentation, and accessibility (NIST Research Data Framework).
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Before editing, write down the AI task, the target or output, the unit being analyzed (such as a person, transaction, or reading), when a prediction would be made, and what decisions the output may influence. For each field, specify expected type, units, valid range or categories, whether it is required, how fresh it must be, and what key should make a record unique. Ask, as Google’s ML guidance puts it, “What is communicated by the data?” A recorded value is often a proxy for reality, not reality in full; as Ben Jones says in the guidance, “It’s not crime, it’s reported crime.” (Google: Data quality and interpretation)
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Record the source and owner, collection dates and process, earlier transformations, labeling method, and how updates are made. Check whether the population and time period represented fit the system’s intended users and deployment setting. Instruments may have limits; people may round values or apply categories inconsistently. These issues can create systematic errors that survive every format and range check.
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Also establish whether the source is trusted for this use, what licensing applies, and whether it contains sensitive information that requires access controls or other protections. For time-dependent tasks, find out when each value became available: a later correction or update must not silently appear in a historical example if it would not have been known at prediction time. Google’s guidance on preparing machine-learning data emphasizes understanding collection and curation, not just cleaning fields (Google Cloud: Preparing and curating your data for machine learning).
Profile the dataset before changing it
Run descriptive summaries and checks against the quality rules you defined. Keep a baseline so you can compare the source with the cleaned version. Census Bureau editing guidance includes missing-data, duplicate, outlier, skip-pattern, range, and valid-value checks (U.S. Census Bureau: Statistical Quality Standard C2).
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- Missing and placeholder values: Count nulls, blanks, and sentinel values such as 0, -1, or 9999. A sentinel may mean “not observed,” not a real measurement.
- Duplicates: Check repeated keys and records, but first define what makes two rows the same entity or event.
- Types, formats, and values: Find invalid types, inconsistent spelling, unexpected categories, impossible ranges, mixed units, and logical contradictions between related fields.
- Freshness: Identify stale records and fields updated on different schedules.
- Distributions and anomalies: Review unusual values, noise, and shifts in distributions. Rarity alone does not establish that a value is wrong.
- Labels and representation: Look for patterns in missingness or labeling and ask which groups or cases are absent or disproportionately represented.
Google’s data-analysis guidance describes examining data before drawing conclusions (Google: Good Data Analysis). Profiling tells you where to investigate; it does not by itself tell you which records to delete.
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Investigate defects before correcting them
Missing values
Find out why a value is absent and whether the absence itself carries information. A field may be missing because a question was not asked, a device failed, or a person chose not to answer; those causes have different implications. Depending on the task and why data is missing, options include retaining nulls, excluding affected records or fields, or imputing values from available information. Do not substitute zero automatically: it may be a valid measurement and can falsely change both the meaning and distribution of the field. If you impute, assess the effects on distributions and group representation and document the method.
Duplicates
Repeated rows can be accidental copies, but they can also represent valid repeat measurements, events, or updates. Define the key based on the underlying entity and the AI task, then resolve collisions with an explicit rule. Do not deduplicate solely because two rows look alike if the task depends on repeated events.
Outliers and unusual values
Check an anomaly against the instrument, collection process, and other evidence before treating it as an error. Google’s ML guidance describes NASA processing software that discarded extremely low ozone readings because the system assumed measurements could not fall below a threshold. Measurements made by Joe Farman, Brian Gardiner, and Jonathan Shanklin at the British Antarctic Survey indicated a seasonal ozone hole. The lesson is not to preserve every extreme value; it is that an untested cleaning assumption can erase a real signal (Google: Data quality and interpretation).
Make corrections that you can justify
For each defect, record what you observed, evidence about its cause, the action taken, which rows or fields changed, and the expected effect. Keep raw data immutable where practical and create a versioned cleaned dataset. Standardize a spelling, type, or unit only when the intended canonical form is known. Remove a row only for a documented reason that makes it invalid for the task—not simply because it is unusual.
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For each correction, consider whether it could alter the task’s signal, group representation, or the chronology of observations. Keep an audit trail that lets another person understand what changed and why. NIST’s AI Risk Management Framework treats validity, reliability, and related trustworthiness characteristics as concerns to assess in context, rather than as properties guaranteed by tidy formatting (NIST: AI Risks and Trustworthiness).
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Validate the cleaned data and its fitness for use
Repeat the original checks after transformation and compare before-and-after summaries. Confirm that required fields, schema, ranges, categories, uniqueness rules, and freshness expectations still hold. Review whether imputation, filtering, or standardization changed important distributions or removed meaningful cases.
For time-dependent tasks, preserve the order of events and the values that were available at each prediction point; using later updates in training examples can make them unrealistic. Then evaluate the model on data that reflects intended deployment conditions and document limits on how well results may generalize. Microsoft’s training-data design guidance discusses data suitability for AI workloads and temporal considerations (Microsoft: Design Training Data for AI Workloads on Azure).
Keep quality controls in place as data changes
Newly ingested or inference-time data should be treated as untrusted until checked. Monitor for stale fields and shifts from expected distributions, define when an update or retraining review is needed, and assign an owner for the rules and auditability. Preserve versioned metadata for both the parent dataset and the subsets used to train or evaluate a model. Microsoft’s AI risk assessment guidance also emphasizes managing risk across the AI system, including its data and use (Microsoft: AI Risk Assessment for ML Engineers).
If you use a data-quality platform, compare support for your data types and sources, coverage of completeness, uniqueness, validity, consistency, freshness and custom rules, lineage and audit records, privacy controls, and integration with your ingestion and ML evaluation pipelines. Microsoft Purview documents configurable data-quality rules in Unified Catalog; availability and supported checks depend on the platform and setup (Microsoft: Create Data Quality Rules in Unified Catalog). Google’s People + AI Guidebook also addresses data collection and evaluation as parts of responsible AI work (People + AI Guidebook: Data Collection + Evaluation).
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