Moving Beyond Data Curation to Data Stewardship
AI Data Stewardship is concerned with addressing critical challenges in data management that must be overcome to realize the full promise of AI-driven enterprise IT applications. Stewardship is a holistic process that encompasses more than data governance, which focuses on adherence to corporate policies and government regulations, and data curation, which is the province of data engineers tasked with ensuring that AI data is cleansed, normalized, enriched and organized so that it can be easily consumed by AI models.
AI Data Stewardship is a higher-level concept that requires developing cross-functional, integrated methodologies and processes for:
- Determining which data types are required for different AI applications
- Identifying the appropriate sources for these data types
- Breaking down silos separating and trapping different types of data
- Employing the right mechanisms for collecting and moving data
- Processing both streaming data and data stored in repositories
- Upgrading data warehouse and data lake infrastructure for AI data
Failure to adequately address these challenges will hinder the adoption of AI applications and in particular, the ability of AI agents to automate all kinds of business processes, but especially those that span multiple functional areas. Enterprise IT professionals need vendors, service providers and system integrators to deliver new solutions, based on innovative AI tools and technologies, for realizing this vision.
