We’ve all been there – a user comes to you, concerned that the numbers ‘don’t look right.’ And more often than not, after digging deeper, you discover an issue in the pipeline. It’s frustrating, to say the least.
I’ve tried to mitigate this problem by creating jobs that sample the data lake against the source regularly. I’ve also set up checks to monitor the time of last ingestion and compare volume against historical averages. But despite these efforts, something always seems to slip through the cracks.
The Problem with Manual Data Quality Validation
Manual data quality validation is time-consuming, prone to errors, and takes away from more critical tasks. But what’s the alternative? Breaking out the SQL editor to eyeball the data manually is not a sustainable solution.
The Cost of Enterprise Data Quality Tools
Paying seven figures for an enterprise data quality tool might not be a viable option for many of us. So, what can we do instead?
Building a Data Quality Framework
Here are a few strategies to help you build a data quality framework that minimizes the need for manual validation:
- Data Profiling: Understand your data distribution, identify outliers, and detect anomalies. This helps you catch issues early on and prevent them from propagating downstream.
- Data Lineage: Track the origin, movement, and transformation of your data. This provides visibility into the data pipeline and helps you pinpoint issues more efficiently.
- Automated Testing: Implement automated tests to validate data quality at each stage of the pipeline. This ensures that errors are caught and flagged early, reducing the need for manual intervention.
- Monitoring and Alerting: Set up monitoring and alerting systems to detect anomalies and notify the team of potential issues. This enables swift action to prevent data quality problems from escalating.
Conclusion
Building a data quality framework takes time and effort, but it’s essential to avoid manual data quality validation. By implementing these strategies, you can reduce the likelihood of errors, increase efficiency, and free up more time for critical tasks.
Further reading: Data Quality Framework