May 1, 2024
4 minute read
Data auditing is a critical process for ensuring the accuracy, reliability, and integrity of data within an organization. It involves examining data to identify errors, inconsistencies, and anomalies, and taking steps to correct them. This process is essential for ensuring that data-driven decisions are made on the basis of accurate and reliable information.
Why Learn Data Auditing?
There are many reasons why one might want to learn about data auditing. Some of the most common reasons include:
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Find a path to becoming a Data Auditing. Learn more at:
OpenCourser.com/topic/hrtm4x/data
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Data Auditing.
Provides a comprehensive overview of data auditing standards and regulations. It is written by the International Organization for Standardization (ISO), the international standards organization.
Provides a comprehensive overview of data quality assessment, covering topics such as data profiling, data cleansing, and data standardization. It is written by W.H. Inmon, a leading expert in the field of data management.
Provides a practical guide to data governance, covering topics such as data strategy, data architecture, and data security. It is written by Jill Dyché, a leading expert in the field of data governance.
Provides a comprehensive overview of data auditing, covering topics such as data quality, data governance, and data security. It is written by Greg Nelson, a leading expert in the field of data auditing.
Focuses on the use of data auditing for fraud detection. It provides guidance on how to identify and investigate fraud using data analytics.
Provides a comprehensive overview of data warehousing, covering topics such as data modeling, data integration, and data analysis. It is written by Ralph Kimball, a leading expert in the field of data warehousing.
Focuses on the specific challenges of auditing data warehouses. It provides guidance on how to assess the quality of data in a data warehouse and how to identify and mitigate risks.
Focuses on the challenges of auditing big data. It provides guidance on how to assess the quality of big data and how to identify and mitigate risks.
Provides a non-technical introduction to data auditing, making it accessible to readers with no prior knowledge of the topic. It is written by Stephen R. Wilson, a data auditing expert with over 20 years of experience.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/hrtm4x/data
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