If you come from a data-driven organisation, data quality is probably an integral part of your business processes. But how sustainable and efficient are your data quality processes?
If you work in a dynamic environment where new data or data sources are added regularly, then manually managing data quality via spreadsheets and coding is just not an option. It is slow, inefficient and expensive. Automation is the future of data management, and metadata plays a crucial role in it. Whether you are building an advanced data management system, such as a Data Fabric or a Data Mesh, or just starting out with Data Quality Management, using a metadata-driven Data Quality system is the most practical approach.
Download the white paper for a comprehensive overview of AI-driven, metadata-driven Data Quality Management and understand the essence of automated data quality management:
- Three reasons why manual data quality is not scalable in today’s data-intensive business environments.
- How automated Data Quality works and what its benefits are.
- The four components of a metadata-driven DQ system.
- Why combining active metadata with a unified data management platform can future-proof your organisation’s DQ processes.
- The key points you can share with relevant stakeholders in your organisation.
