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John Smith
Data Scientist, USA
Cloud-Ready Data
Digital Transformation
Data Governance


How to implement a data quality and observability solution

In this interactive workbook, you will learn:

  1. How to implement a data quality solution to suit the unique needs of your organization

  2. How to successfully plan and execute the primary considerations for your data quality architecture

  3. How other businesses have successfully enabled data quality adoption


We understand that a successfully implemented data quality architecture can significantly improve team productivity, scale your data quality operations, and optimize total cost of ownership for your organization. This workbook would enable you to plan for the crucial considerations and guide you through the nuances of implementing a robust data quality architecture.


In this workbook, we introduce you to the technical considerations you need to take into account during your data quality implementation, including the following:

  1. Capacity planning and scalability

  2. Security and composability

  3. Ease of use and adoption