The Tool Desk
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The 15 issues below are a practical guide, not a definitive list attributed to any one author. For each, the goal is to correct the immediate problem and reduce the chance it returns.
How to approach data quality issues
Start with the affected decision, report, or process. Data is fit for one use and inadequate for another: a missing optional note may not matter, while a missing identifier can make a record impossible to match. Assess the data against accuracy, completeness, consistency, relevance, and timeliness, then prioritize defects by their effect on real consumers. The ScienceDirect overview of data quality issues emphasizes that problems extend beyond incorrect and missing values.
- Define the expected result. Identify who uses the data, what they need it to contain, and what failure looks like.
- Profile the affected fields and records. Measure missing values, duplicates, invalid values, mismatches, and unexpected changes before editing anything.
- Trace the data backward. Check collection, source applications, transfers, transformations, joins, and reporting. A bad result may originate in processing logic or inconsistent definitions rather than the original entry.
- Prioritize by impact and recurrence. Consider the importance of the affected data, how often the defect occurs, who relies on it, and the cost of remediation. An unused archive may need a clear limitation notice rather than a costly cleanup.
- Correct with documented rules. Preserve original values or an audit trail where the domain requires it, and provide a way to review exceptions.
- Prevent recurrence near the source. Add appropriate input checks, system contracts, transformation tests, monitoring, ownership, and an exception path.
- Communicate remaining limitations. Tell downstream users what is still incomplete or uncertain so they can interpret results safely.
Automated validation and cleansing can help, but rules must reflect the field’s real meaning. A rule that rejects legitimate exceptions can create a new data-quality defect.
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15 common data issues and how to fix them
1. Missing data
A required field is blank, so a process cannot identify, classify, or act on a record. Make genuinely necessary fields required at collection and explain why they are needed. Do not fill unknown values with a plausible-looking default: that turns missingness into misinformation. If a value is legitimately unavailable, represent that state explicitly.
2. Incomplete records
A record exists but lacks enough fields to serve its intended use. Define the minimum information required for each workflow, train people on collection procedures, and check completeness at the point of entry. Avoid demanding fields that are not needed; unnecessary requirements can encourage fabricated answers or workarounds.
3. Duplicate records
The same real-world entity appears more than once, splitting its history or inflating counts. Use stable identifiers where appropriate and define matching and merge rules. Automatically merge only when the match is sufficiently certain; route ambiguous cases to review, and retain a record of what was combined.
4. Incorrect values
A value is present but does not reflect reality—for example, a transposed digit or an impossible date. Apply field-level validation and, when available, compare against a trustworthy reference. Define a correction path for legitimate exceptions rather than letting a rigid rule silently block valid records.
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Equivalent values are represented differently, such as dates written in multiple formats or names with inconsistent punctuation. Choose an unambiguous standard for storage and exchange, normalize data at a defined point in the pipeline, and preserve the original form when it is needed for display or audit.
6. Inconsistent identifiers and code sets
Systems use different identifiers, labels, or category codes for the same entity or concept. Agree on shared definitions and authoritative identifiers, then maintain explicit mappings for legacy codes. Test those mappings as systems change; a syntactically valid code can still refer to the wrong thing.
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7. Outdated information
Some facts change over time, so a once-correct address, status, or contact detail may no longer be useful. Identify which fields decay, how quickly they matter for the intended use, and who is responsible for refreshing them. Set refresh or expiry procedures accordingly, and distinguish unknown or unverified information from current information.
8. Irrelevant data
A field or record does not support the decision or process for which it is being used. Clarify the purpose of the dataset and remove or exclude information that does not serve it, while checking whether another consumer depends on it. For old or unused records, documenting their limits may be more proportionate than cleaning them up.
9. Unobserved data
A dataset omits people, events, or conditions that matter to the question being asked. Check how data is collected and who or what is systematically absent. Improve collection coverage or qualify the analysis; do not treat a blank or unrecorded event as proof that the event did not occur.
10. Dirty data
Records contain a mixture of errors, inconsistent entries, stray characters, and other defects that make them hard to use. Profile first to determine the kinds and scale of problems, then apply documented standardization and correction rules. Review uncertain changes and keep a traceable path back to original values where needed.
11. Unbalanced data
Some groups, categories, or outcomes appear far more often than others. Determine whether the distribution reflects reality or a collection or processing bias, and consider how that imbalance affects the specific analysis. Fix the collection cause when possible and disclose the limitation; do not change proportions simply to make a dataset look even.
12. Unstructured or poorly organized data
Information may be difficult to retrieve or compare because it is stored without consistent fields, definitions, or organization. Establish a structure suited to the intended use, with clear field meanings and controlled categories where appropriate. Preserve context that would be lost by forcing complex information into oversimplified fields.
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13. Costly-to-produce data
Some data requires substantial time, specialist effort, or resources to collect. First establish which decisions depend on it and what level of precision they require. Reduce unnecessary collection, reuse reliable existing information where appropriate, and prioritize expensive collection for high-value uses. Do not substitute a cheap proxy without checking whether it answers the same question.
14. Integration and transfer defects
Data may be lost, duplicated, delayed, or changed when moving between systems. Compare inputs and outputs, verify expected record counts, and test joins and mappings. Define system-to-system expectations for field names, types, identifiers, and update behavior; monitor feeds so missing or stale transfers are visible rather than silently accepted. Practical integration and monitoring examples are discussed by OWOX.
15. Transformation and processing defects
Rules that clean, combine, filter, or aggregate data can produce a misleading result even when source records are sound. Test transformations against known cases, validate output fields and counts, and investigate changes in results after pipeline updates. Fix the logic that introduced the defect rather than relying only on repeated downstream cleanup. lakeFS’s data-quality guide describes profiling, validation, and pipeline controls as part of this work.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When to use tools—and when to change the process
Data profiling, validation, matching, cleansing, and monitoring tools can find patterns, standardize representations, flag anomalies, and track feeds. They cannot decide every business definition or resolve every cross-system disagreement on their own. Rick Sherman’s Business Intelligence Guidebook, summarized in the ScienceDirect overview, discusses the limits of cleansing tools and the role of governance.
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Quick Recap
- Use automated rules for clear, repeatable checks such as required fields, allowed formats, and expected pipeline outputs.
- Use human review when a match is uncertain, an exception may be legitimate, or the correction changes meaning.
- Assign ownership for definitions, reference data, exception handling, and recurring quality monitoring.
- Fix collection, interface, or transformation causes where feasible; downstream cleanup alone can leave the defect-producing process unchanged.
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