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Cleaning data means finding and handling errors, missing values, duplicates, and inconsistencies so a dataset is suitable for a specific use. Data analysts commonly do the profiling and cleaning, but people who understand the data’s meaning—such as data stewards or subject-matter experts—may need to review ambiguous decisions. There is no universal owner: responsibilities depend on the organization, dataset, and intended use.
What data cleaning involves
Cleaning is a quality-improvement step: assess a dataset, identify problems, and correct, remove, flag, or otherwise handle them in light of what the data will be used for. IBM describes it as identifying and correcting errors and inconsistencies in raw data to improve its quality. The goal is not to make every value look ordinary; it is to make the data dependable enough for its intended analysis or operation.
Common issues include:
- Duplicates: repeated records that may represent accidental copies—or, depending on the data, legitimate repeated events.
- Missing values: blank, null, or unavailable fields that may need investigation, an appropriate treatment, or an explicit flag.
- Inconsistent formats: for example, dates entered in different formats or values that use inconsistent naming conventions.
- Invalid or syntactically incorrect entries: values that violate defined rules or cannot be interpreted as intended.
- Irrelevant records and structural errors: data that does not belong in the analysis, or a layout that prevents records and fields from being used reliably.
IBM’s overview of data cleaning describes profiling, standardization, deduplication, missing-value handling, outlier assessment, and validation as parts of the work. The appropriate choices depend on the dataset and its purpose: IBM’s guide to data cleaning.
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A value can be wrong for one purpose and useful for another. Before changing it, establish what the fields represent, how the data was collected, and what decisions or analysis will rely on it. IBM’s guidance on dirty data emphasizes understanding sources, collection, lifecycle, relationships, requirements, and intended use before correcting records: IBM’s overview of dirty data.
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Example: inconsistent dates
If a date field contains both 04/05/2025 and 2025-05-04, the first value is ambiguous: it could mean April 5 or May 4. Standardizing the column without confirming its locale or source convention could silently assign the wrong date. The person who knows how the source system records dates may need to clarify the meaning; the analyst can then apply a consistent format and check the results.
Example: an unusual value
An outlier should be investigated, not automatically deleted. It could be a data-entry error, a rare but genuine event, or a meaningful anomaly. Depending on the analysis, it may be retained, corrected, removed, or flagged. The decision should reflect evidence about the value and its relevance—not simply the fact that it is far from the average. IBM likewise advises assessing outliers in context: IBM’s data-cleaning guidance.
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The CRISP-DM 1.0 guide frames cleaning as bringing data quality to the level needed by the selected analysis techniques. It also recommends recording decisions and actions and considering how cleaning transformations may affect analysis results. In other words, “clean” means fit for a defined purpose, not stripped of every surprising observation: CRISP-DM 1.0 guide (2000).
Cleaning, preparation, transformation, and validation
These activities are related, but they are not interchangeable:
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- Profiling examines a dataset to understand its structure and identify quality issues. It helps determine what needs attention before changes are made.
- Cleaning addresses quality problems, such as inconsistent values, duplicates, missing fields, or invalid entries.
- Transformation converts or structures data for use, such as standardizing formats or reshaping fields. A transformation may support cleaning, but it can also prepare otherwise valid data for analysis.
- Validation checks whether the result meets requirements and is ready for its intended use. It should confirm that changes worked and did not introduce new problems.
Cleaning is often one part of broader data preparation. IBM describes a final review to check readiness for analysis or visualization, while CRISP-DM stresses documenting the effects cleaning decisions may have on results.
Who usually does the work?
Data analysts commonly profile, clean, and transform data as part of turning raw information into analysis and reporting. Microsoft’s data analyst career profile includes these responsibilities alongside understanding stakeholder requirements, modeling data, and producing insights. Its PL-300 study guide also includes evaluating data and resolving inconsistencies, unexpected or null values, and quality problems.
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That does not mean analysts should make every judgment alone. A common collaboration pattern is for analysts to identify and implement changes, while a data steward or subject-matter expert reviews decisions that depend on business meaning, source-system behavior, or data rules. Other data-management professionals may also contribute. The exact division of work varies by organization and data use.
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For the analyst role and its responsibilities, see Microsoft Learn’s data analyst career profile and the PL-300 study guide.
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- Define the intended use and requirements. Identify which analysis, report, or operational task will rely on the data and what quality rules apply.
- Profile the data and investigate its origins. Inspect representative records, field structure, relationships, collection practices, and known source conventions.
- Choose a treatment for each issue. Correct values only when there is sufficient evidence; otherwise, consider retaining, flagging, excluding, or handling them according to the use case.
- Record consequential choices. Document what changed and why, particularly where a choice could affect analysis results.
- Validate the output. Check that requirements are met, intended values remain intact, and the cleaned data is ready for its next use.
Cleaning improves data quality, but it cannot guarantee that data is perfect or appropriate for every future question. A dataset judged ready for one analysis may need additional checks when its purpose or requirements change.
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