Audit Dataweek 1 Table Of Content Introduction ✓ Solved

Audit Dataweek 1 Table Of Content Introduction

Conducting data audit and assessing how a company's data is fit for a particular purpose. Data auditing involves verifying data to evaluate its quality, looking at key metrics, and drawing conclusions about properties of the data set. The process depends on registry and storage space. The audit includes identifying issues such as invalid addresses, duplicate files, and foreign files, documenting sample counts, and ensuring data consistency. It also involves pulling records from internet systems, verifying customer records, and checking that email communications are correctly categorized. The framework developed by the DAFD projects helps organizations describe, identify, locate, assess, and manage data assets effectively, excluding administrative assets like student databases or research outputs. Data audits help ensure data quality, manage resources efficiently, and reduce risks like data damage or loss, while also enhancing cybersecurity measures.

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The process of conducting a comprehensive data audit is integral to maintaining the integrity, accuracy, and security of organizational data. Data auditing involves verifying data quality, assessing its fitness for specific purposes, and ensuring compliance with relevant standards and regulations. This process encompasses several critical steps, including identifying invalid data entries such as incorrect addresses, duplicates, or foreign files, and recording sampling counts and processing steps meticulously. Such audits depend heavily on the proper management of registry and storage systems, which serve as repositories of critical information.

One fundamental aspect of data auditing involves validation of files sent from a company's server or bureau, which often contain duplicated or invalid records requiring correction or removal. Auditors must verify that input counts align with counts pulled from mainframe systems, identify false or dropped files, and update rollups accordingly. Maintaining consistency across various data sources is vital, which includes matching NCOA drops and ensuring that merged data retains integrity and accuracy.

Moreover, the auditing process extends to internet system records, where verifying customer records for correctness and completeness ensures that all client data remains accurate and current. A crucial step involves confirming that email campaigns are properly categorized, ensuring 'not-to-be' sent emails remain undisturbed and in the correct inboxes, and assessing the rates of unknown or high-risk data to facilitate better estimations in subsequent file pulls.

The framework developed by the Data Asset Framework Development (DAFD) project underscores the importance of structured data management. It emphasizes that data audits should target organizational data assets, excluding administrative assets such as students' databases or research outputs, focusing instead on operational data critical for decision-making. Effective data management allows organizations to optimize data utility, reduce resource wastage, and enhance cybersecurity measures against cyber threats.

Importantly, data audits can significantly aid in cyber security by identifying vulnerabilities, outdated or compromised data, and inconsistencies that could be exploited. Data cleaning by a service bureau enhances overall data quality, facilitating accurate analysis and strategic planning. Organizations should establish comprehensive data audit policies that clearly define roles, procedures, and responsibilities, ensuring consistent and effective data management practices.

For example, Mullins (2002) emphasizes the importance of robust database administration practices to maintain data integrity and security. Proper auditing ensures that data repositories support organizational needs, risk management, and compliance requirements, thereby fostering trustworthiness and operational efficiency.

References

• Mullins, Craig (2002). Database administration: a complete guide to practices and procedures. Retrieved from [source]
• Grant, R. M. (2016). Contemporary Strategy Analysis: Text and Cases. Wiley.
• Porter, M. E. (1985). Competitive Advantage. Free Press.
• National Vulnerability Database (NVD). (2023). U.S. Department of Homeland Security.
• CVE List. (2023). MITRE Corporation.
• Calculating CVSS Scores. (2023). FIRST.org.
• Cybersecurity and Cyber Threats. (2022). Cybersecurity & Infrastructure Security Agency.
• Cybersecurity Incident Response and Handling. (2021). NIST Framework.
• Data Management Best Practices. (2020). Data Governance Institute.
• Effective Data Audit Strategies. (2018). International Journal of Data Quality.