Cross-Checking and Auditing
Auditing and cross-checking are systematic techniques used to verify data reliability after preparation or integration. They support confidence in analysis by validating results, confirming consistency across sources, and providing evidence that data outputs are fit for their intended purpose.
Technique Overview
Cross-Checking and Auditing Definition
Auditing and cross-checking refer to structured processes used to validate data by reviewing outputs against defined rules, expectations, and source systems. Rather than identifying individual data quality issues, these techniques focus on verifying consistency, completeness, and traceability, ensuring analytical results can be trusted and confidently used for decision making (Batini et al., 2009; ISO, 2018).
Cross-Checking and Auditing Description *
* The full technique overview will be available soon. Contact us to register your interest in our business management platform, and learn all about Cross-Checking and Auditing.
Business Evidence
Strengths, weaknesses and examples of Cross-Checking and Auditing *
* The business evidence section is for premium members only. Please contact us about accessing the Business Evidence.
Business Application
Implementation, success factors and measures of Cross-Checking and Auditing *
* The business application section is for premium members only. Please contact us about accessing the Business application.
Professional Tools
Cross-Checking and Auditing videos and downloads *
* The professional tools section is for premium members only. Please contact us about accessing the professional tools.
Further Reading
Cross-Checking and Auditing web and print resources *
Cross-Checking and Auditing references (4 of up to 20) *
- Batini, C., Cappiello, C., Francalanci, C. and Maurino, A. (2009). Methodologies for data quality assessment and improvement. ACM Computing Surveys, 41(3), pp.1–52. doi:https://doi.org/10.1145/1541880.1541883
- Gordon, S.C., Samii, C. and Su, Z. (2025). Data-NoMAD: A Tool for Boosting Confidence in the Integrity of Social Science Survey Data. doi:https://doi.org/10.48550/arXiv.2501.14651
- Houston, L., Probst, Y. and Humphries, A. (2015). Measuring Data Quality Through a Source Data Verification Audit in a Clinical Research Setting. Studies in health technology and informatics. doi:https://doi.org/10.3233/978-1-61499-558-6-107
- ISO (2018) ISO 19011:2018 Guidelines for auditing management systems. Geneva: International Organization for Standardization. Available at: https://www.iso.org/standard/70017.html
* The further reading section is for premium members only. Please contact us about accessing the further reading.
Learn more about KnowledgeBrief Manage and how you can equip yourself with the knowledge to succeed on Cross-Checking and Auditing and hundreds of other essential business management techniques