1. Companies often find their data lakes messy when rolling out enterprise AI tools
2. Chad Sanderson is the CEO of Gable.ai, focusing on improving data quality at scale
3. Data contracts are crucial for ensuring applications maintain integrity with large amounts of data
When companies implement enterprise AI tools, they may encounter messy data lakes that can lead to downstream consequences due to poor data change management. Chad Sanderson, the CEO of Gable.ai, specializes in helping organizations improve data quality at scale. With a background in journalism, Sanderson transitioned into data science and data quality, emphasizing the importance of maintaining data integrity through data contracts.
Data contracts serve as a mechanism for federated data governance, allowing companies to manage decentralized data more effectively. In the past, data architectures were centralized, but with the shift to the cloud, data engineers must now navigate messy data lakes created by rapidly developing applications. The lack of structure and ownership can lead to chaotic situations with severe repercussions for AI models.
Gable, a software engineering tool focused on data complexities, offers a unique solution with code interpretation capabilities. By ensuring clear expectations and SLAs for data usage, organizations can enhance data quality and governance. Sanderson highlights the need for companies to develop a data strategy focused on high-quality, context-driven data to leverage AI effectively.
Regulations like GDPR and CCPA have raised concerns about data quality and security, particularly in the context of AI and generative models. As regulations evolve, companies may opt for internal data solutions and data vendors to ensure compliance and trustworthy data. The concept of data contracts becomes essential in these scenarios to maintain data consistency and reliability for AI applications. Sanderson emphasizes the importance of data curation and management for successful AI deployment within organizations.