Evaluate Data Quality for Business Analytics

Most analysis failures are not analysis failures at all-they are data failures that nobody checked for.
Most analysis failures are not analysis failures at all-they are data failures that nobody checked for. Missing values, duplicated records, stale extracts, and metrics defined differently in two systems all produce dashboards that look authoritative and quietly mislead the business. In this course, Evaluate Data Quality for Business Analytics, you'll gain the ability to judge whether a dataset can be trusted to answer a specific business question. First, you'll explore how a dataset's source, collection method, ownership, and lineage shape its reliability, and how to assess it against quality dimensions such as completeness, accuracy, consistency, validity, timeliness, and uniqueness. Next, you'll discover how specific defects-missing values, duplicates, inconsistent definitions, outdated records, and invalid values-distort real business metrics, and how bias, representativeness, privacy, and appropriate-use constraints affect whether data should be used at all. Finally, you'll learn how to assess a business dataset end to end: classifying its issues, tracing their impact on your calculations and conclusions, judging whether the data is sufficient for the question, and recommending whether to proceed, remediate, obtain more data, or disclose limitations. When you're finished with this course, you'll have the skills and knowledge of evaluating data quality and fitness for purpose needed to stand behind the numbers you deliver.
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