Summary
In this chapter, we discussed the critical role of high-quality data, providing a solid foundation for analytics, machine learning, and informed decision-making. To ensure data quality, organizations implement a series of checks and measures at various stages of the data pipeline:
- Data entry/ingestion: Data sources are validated to ensure accurate and consistent data capture, primarily overseen by data engineers
- Data transformation: Quality checks are incorporated into the transformation layer to maintain data reliability and accuracy, typically managed by data engineers
- Data integration: Checks prevent data quality issues from propagating and support confidence in integrated data, involving data engineers and data scientists
- Data consumption: Quality data input is vital for analytics and machine learning, impacting user trust and competitive advantage, and is driven by data scientists and analysts
These quality checks ensure that data adheres to...
