Data Quality Monitoring and Validation

When data moves in and out of a Data Lake or a Cloud, the IT and the business users are faced with the same question- is the data trustworthy?

Automating the 13 essential data validation checks will immediately engender trust in the Cloud and Lake.

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Data Quality Monitoring and Validation Key Takeaways

  • Every step data moves, errors get compounded and it takes 10x effort to fix it.

  • Achieving data consistency and reliability requires organizations to continually monitor the data quality.

  • Assessing accuracy is the first step in managing data sets to allow for appropriate data-driven decisions.

  • Conduct tests to build a baseline for identifying gaps within your data assets to improve data quality.

  • A successful data validation program has to be scalable and it must enable error-free data pipelines. It must embody the following:
    • Autonomously validate data pipelines with AI/ML
    • Create trusted data pipelines by automatically calculating objective Data Trust Scores for every data asset.
    • Cut the current rates of data rework efforts by 90% and still increase data accuracy