What is data quality?
Data quality is how well your data fits its purpose. Good data is accurate, complete, consistent, timely, and free of duplicates. In marketing, poor data quality leads to wrong reports and bad decisions about budget and strategy.
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What makes data good
Accuracy means values are correct. Completeness means required fields are filled in. Consistency means the same thing is recorded the same way everywhere, such as one name for each campaign.
Timeliness and uniqueness matter too. Stale records and duplicate leads both distort the picture.
Where quality problems come from
Broken tags, inconsistent UTM naming, duplicate form submissions, spam leads, and mismatched definitions across tools are all common causes. Manual data entry adds more.
How to protect it
Agree on naming rules and metric definitions, validate data when it's collected, and audit your tracking regularly. Automated checks and anomaly alerts catch problems sooner than a monthly review.
Common questions
Why does data quality matter in marketing?
Budget, bidding, and strategy decisions rely on your numbers. If the numbers are wrong, the decisions built on them will be too.
How do I check my data quality?
Compare totals across systems, test that key events fire, look for missing or duplicate records, and review naming consistency in campaign data.
What's the difference between data quality and data governance?
Data quality is the condition of the data. Governance is the set of rules and responsibilities that keep it in good condition.
Can bad data affect ad platform performance?
Yes. Bidding systems learn from the conversion data you send, so inaccurate or missing conversions can steer them toward the wrong audiences.
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