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Clean AI data by defining what “good” means for the task, tracing how the data was collected, profiling it for defects, correcting only problems you can justify, and validating the result. Do not treat cleaning as a hunt for unusual values: missing data may be meaningful, duplicates may represent real events, and an extreme measurement may be the very signal a model needs.
What counts as good data for an AI task?
Data quality is fitness for a particular purpose, not an abstract standard of tidiness. NIST’s Research Data Framework names dimensions including accuracy, completeness, currency, relevance, consistency, reliability, presentation, and accessibility. A field may be accurately recorded yet irrelevant to the model’s target; a dataset may be complete but too old for the decision it supports. Set quality rules against the actual prediction or generation task. NIST Research Data Framework
Before inspecting rows, specify the outcome the AI system should produce, the unit being represented (such as a person, transaction, image, or event), when a prediction is made, and what decision may follow. For each field, define expectations for type, units, valid ranges, categories, uniqueness, requiredness, and freshness. These definitions make it possible to distinguish a defect from a value that is simply unexpected.
Trace how the data was collected
A neat table can still carry measurement error, collection bias, inconsistent labeling, or a poor proxy for reality. Identify the source and owner, collection dates and conditions, instruments or processes used, labeling method, prior transformations, and how records are updated. Check whether the data represents the population and time period the AI system is meant to serve. Record licensing and sensitivity information, and determine whether the source is suitable for this use. Google’s guidance recommends asking, “What is communicated by the data?” Google: Data quality and interpretation
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Measurements and labels describe what was observed or recorded, not necessarily reality in full. As Ben Jones puts it in Google’s guidance, “It’s not crime, it’s reported crime.” Reported incidents, diagnoses, clicks, and similar fields can reflect access, reporting practices, or human judgment as well as the underlying phenomenon. That distinction matters when deciding what a model should learn.
Profile the data before changing it
Start with descriptive summaries and checks that can be repeated after cleaning. Review field types, formats, valid categories, ranges, units, logical relationships, missingness, duplicate keys and records, record freshness, distributions, and anomalies. Census Bureau guidance specifically includes missing-data, duplicate, outlier, skip-pattern, range, and valid-value checks in its data-editing standard. U.S. Census Bureau: Statistical Quality Standard C2
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- Missing and placeholder values: Find blanks, nulls, and sentinel values such as 0, -1, or 9999 that may mean “not observed” rather than a real value.
- Duplicates: Check repeated keys and rows, but first define what counts as the same entity or event.
- Invalid values: Look for unexpected types, misspelled categories, inconsistent formats or units, values outside justified ranges, and contradictory field combinations.
- Staleness: Identify records that have not been updated as expected and inconsistencies in update timing.
- Distribution and representation: Examine unusual patterns, labeling differences, systematic missingness, and whether important groups or periods are represented.
- Outliers and anomalies: Flag values for investigation, not automatic deletion. Statistical rarity alone does not establish that a value is wrong.
Google’s data-analysis guidance also recommends examining distributions and potential anomalies before drawing conclusions from data. Google: Good Data Analysis
Investigate defects before choosing a correction
For each suspected problem, record what you observed, evidence about its cause, the proposed action, the affected rows or fields, and the likely effect. Preserve an unchanged source copy where practical and make corrections in a versioned working dataset. Standardize spelling, types, or units only when the intended canonical form is known; exclude a row only for a documented reason, not because it looks unusual. Google Cloud: Preparing and curating your data for machine learning
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Missing values
Find out why a value is absent and whether the absence itself carries information. A blank may indicate a skipped question, an unavailable measurement, a process failure, or a condition that does not apply. Depending on the cause and task, you might retain nulls, exclude affected records or fields, or impute a value from available information. Use a justified method and check whether imputation changes the distribution or the representation of groups. Do not automatically replace missing values with zero.
Duplicates
Distinguish accidental copies from legitimate repeat measurements, separate events, or successive updates. Define a key based on the entity and task, then resolve collisions using a documented rule. Deleting every repeated row can erase real frequency or longitudinal information.
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Outliers and extreme measurements
Check an unusual value against the instrument, collection process, units, and external evidence before deciding whether it is an error. Google describes an example in which NASA processing software discarded extremely low ozone measurements as implausible because of an assumed threshold. Measurements by Joe Farman, Brian Gardiner, and Jonathan Shanklin at the British Antarctic Survey helped reveal the seasonal ozone hole. The lesson is not to keep every outlier, but to test the assumption behind a cleaning rule before it removes a real signal. Google: Data quality and interpretation
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Run the same quality checks again and compare before-and-after summaries. Confirm required fields, schema, allowed values, duplicate handling, and expected freshness. Investigate changes in distributions or group representation introduced by cleaning or imputation.
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For time-dependent tasks, preserve chronology and the values that would actually have been available at each prediction point. Training examples that include later updates can give a model information it would not have had in deployment. Evaluate the model on data that reflects intended operating conditions, and document where the results may not generalize. NIST’s AI Risk Management Framework identifies validity and reliability among characteristics relevant to trustworthy AI systems. NIST: AI Risks and Trustworthiness
Keep data traceable as it changes
Retain source and ownership metadata, versions, and a record of transformations alongside the dataset. Keep metadata for subsets as well as the parent dataset, assign an owner for policy adherence and auditability, and handle sensitive information with appropriate access controls. Treat newly ingested data as untrusted until it has been reviewed; monitor for staleness and drift, and define when updates or retraining should be considered. Microsoft Learn: AI Risk Assessment for ML Engineers Microsoft Learn: Design Training Data for AI Workloads on Azure
If you use data-quality software, compare its supported sources and data types, rule coverage, lineage and audit features, privacy controls, and integration with the existing data and model-evaluation workflow. Microsoft Purview’s Unified Catalog documentation describes data-quality rules as one example; available support depends on the platform. Microsoft Learn: Create Data Quality Rules in Unified Catalog For collection and representation questions, Google’s People + AI Guidebook provides further guidance. People + AI Guidebook: Data Collection + Evaluation
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