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AI Data Quality: Accuracy, Completeness, Consistency, and Timeliness
This episode of the ISACA Advanced in AI Audit (AAIA) exam prep series examines why the health of the information feeding a model matters more than the code that surrounds it. You’ll see how modern systems learn from data, the preparation work required before training, and the standard measurements auditors use to grade whether a dataset is fit for purpose. The discussion gives you a framework for assessing the raw fuel that powers any AI tool you encounter.
What this episode covers
- How modern AI learns — the shift from hand-coded rules to deep learning, and why data health now drives model performance.
- Data profiling — examining raw information to understand its current state and spot gaps or weird patterns.
- Data cleansing — fixing blatant errors, removing noise, and preparing inputs for training.
- Imputing missing data — logically filling in blanks so models do not break on empty spaces.
- The six dimensions of data quality — accuracy, completeness, consistency, timeliness, validity, and uniqueness.
- The auditor’s lens for demanding proof of data health before approving any AI deployment.
Watch the full episode above for the worked examples and detailed explanations of each concept.
Frequently Asked Questions
What are the six dimensions of AI data quality?
The six dimensions are accuracy, completeness, consistency, timeliness, validity, and uniqueness. Accuracy means the data is free from errors, completeness means all necessary fields and records are present, consistency means the data is uniform and standard across datasets, timeliness means it is up to date and available when needed, validity means it adheres to defined business and technical logic, and uniqueness means there are no duplicate or redundant records.
Why is data quality so important for deep learning?
Deep learning systems learn to recognize complex patterns by absorbing massive amounts of data rather than following rigid human-written rules, so a model’s performance is directly proportional to the health of its data. The results from any AI system are only as good as the information it was trained on, like teaching a child to play piano on a keyboard with several broken keys.
What is data profiling and data cleansing?
Profiling means examining your collection of information to understand its current state and evaluate its overall quality, looking for obvious gaps or weird patterns. Data cleansing then fixes blatant errors and removes useless noise, and a major part of cleansing is imputing missing data, which is logically filling in the blanks.
What does imputing missing data mean?
Imputing is a mathematical term for logically filling in the blanks. For example, if a greenhouse thermometer lost power on a Wednesday, you might impute the missing temperature by taking the average of Tuesday and Thursday, safely estimating the missing piece so the system does not crash when it tries to read an empty space.
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Reference: This article is based on concepts discussed in AI Data Quality: Accuracy, Completeness, Consistency & Timeliness.