Profile Data Quality
About this course
Before you analyze data, you need to know if you can trust it. In this course, you’ll learn how to assess the health of your marketing data using practical data profiling techniques. You’ll start by learning how to measure three key dimensions of data quality: completeness, uniqueness, and distribution. Using SQL and spreadsheet tools, you’ll calculate null rates, identify duplicate records, and analyze how values are distributed across key marketing fields like UTM parameters, user IDs, and timestamps. Then, you’ll move from analysis to action. You’ll learn how to interpret profiling results to identify the most important data issues, prioritize fixes based on business impact, and communicate findings clearly to stakeholders. You’ll also learn how to define data quality baselines, setting measurable standards that ensure your data is reliable over time. Through hands-on activities, you’ll work with real campaign datasets to generate profiling reports and develop remediation plans. By the end of the course, you’ll be able to confidently evaluate data quality, document issues, and support better decision-making with trustworthy data. This course is ideal for analysts, marketing analysts, and data professionals who want to improve data reliability and build confidence in their analysis. No prior experience is required, making the course accessible to learners who are new to data profiling and data quality assessment. By the end of the course, you’ll be able to confidently evaluate data quality, document issues, and support better decision-making with trustworthy data.
69/100
CourseAsk score
- What the provider tells you
- 45/45
- Who stands behind it
- 8/35
- How complete the listing is
- 16/20
Scores how much the provider publishes and who stands behind it — not how well it is taught.
What you'll learn
- assess marketing data quality
- calculate null rates and identify duplicate records
- analyze data distribution
- interpret profiling results for business impact
- define and set data quality baselines
Course objectives
- improve data reliability
- build confidence in analysis
- support better decision-making with trustworthy data
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