Reach 100% data quality score
A step-by-step guide to improve data quality
Best practices for effective data quality improvement
This eBook will teach you how to measure data quality with KPIs.
Measuring the data quality over time improves trust in the data by business and data science teams. The source of data quality anomalies can be easily identified and the root cause can be diagnosed.
Data quality issues are commonly categorized by data quality dimensions, such as completeness or accuracy. When a data quality issue is detected, it should be reviewed and assigned to a data engineering team or an external party to be fixed.
Managing the whole process around the identification, assessment, fixing and revalidation of data quality issues needs coordination between data quality teams, data engineering teams, and data science teams.
This eBook shows a proven data quality process for organizing the data quality team and measuring data quality over time.
Download the eBook to learn how to:
- Set data cleansing goals.
- Conduct an iterative data cleansing project.
- Measure data quality across multiple dimensions, such as accuracy, validity, completeness, consistency, currency, or timeliness.
- Detect and respond to data quality problems in the future.
- Detect problems in data pipelines.