If you come from a data-driven organization, data quality is likely an integral part of your business initiatives. But how sustainable and efficient are your data quality processes?
If you work in a dynamic environment where new data or data sources are onboarded on a regular basis, then manual data quality management via spreadsheets and coding is not an option. It’s slow, inefficient, and costly.
Automation is the future of data management, and using AI and metadata is critical. Whether you’re building an advanced data management system like a data fabric or a data mesh, or just starting with DQM, using a metadata-driven, AI-powered data quality system is the most practical approach.
Download your copy of the whitepaper for a comprehensive overview of AI-powered, metadata-driven data quality management.
Understand the essentials of automated data quality management:
- 3 reasons why manual data quality is not scalable in modern-day data-intensive enterprise environments.
- How automated data quality works and its benefits
- The four components of a metadata-driven DQ system
- Why combining active metadata with a unified data management platform can future-proof your organization's DQ processes
- Key takeaways you can share with relevant stakeholders across your organization
