In business today, data governance for generative AI is crucial for ensuring data accuracy, consistency, and security; fundamental aspects for informed decision-making.
As businesses rely on data and AI for innovation and competitive advantage, robust data governance is essential to safeguard information and uphold ethical standards.
In this article, we tackle some of the presentations at the ninth annual Data Innovation Summit! From becoming a data-driven company and enhancing data quality through data contracts, to the impact of generative AI on data-driven innovation and harnessing data to enhance business value, we cover it all!
1. The Journey to a Data-Driven Company
In a presentation by Daniela Kato from Zalando SE, Daniela dives into the components of successful governance programs based on her experience with three implementations! Daniela sheds light on the vital need for clear governance structures and policies as the foundation of effective data management. Receive guidance on mastering metadata management, essential for accurate data utilization across organizational functions!
This talk explores both the foundational importance of data ownership and metadata in effective governance, as well as different governance models and their scalability. Drawing from her experience, she highlights various frameworks tailored to different organizational sizes and complexities. Attendees gain insights to determine which model aligns best with their needs and growth trajectories. Daniela also examines how governance models affect metadata management. This approach enhances current data practices and sets the stage for resilient data architectures.
2. Data Contracts for Data Quality in Large Enterprises
Join us for a panel discussion on enhancing data quality and governance through data contracts! This panel dives into the crucial role of data contracts as formal agreements. These agreements streamline data exchange across organizational boundaries. Panelists explore how data contracts act as a cornerstone for robust governance frameworks.
They also address challenges and simplify data management while fostering collaboration between data creators and users. Attendees gain insights into implementing best practices for creating and integrating data contracts within complex data ecosystems.
3. Data Governance for Generative AI: Future of Data-Driven Innovation
How should we think about data governance in the new Generative AI reality? First, good news! Data professionals can leverage existing data privacy and governance principles and tools to address the challenges posed by generative AI.
Discover the future of data in Generative AI with Inna Weiner from Google! In her talk Data Governance for Generative AI, she explores how existing data privacy principles can address challenges of Generative AI. Learn practical techniques like anonymization, deliberate data decay, and differential privacy to protect personal information. All while maintaining data accuracy and utility!
Inna covers vital principles for protecting user data in AI systems: deploying models to data sources, sandboxing Large Language Models (LLMs), and controlling LLM access. Gain insights into common and AI-specific privacy challenges! This session is crucial for data professionals and AI practitioners navigating Generative AI’s ethical complexities, ensuring compliance and fostering trust through responsible data practices.
4. Harnessing Data to Enhance Business Value
Explore data governance in the quest to maximize business value through data and AI at the session titled How Data Governance is Critical in Harnessing Data to Enhance Business Value! In today’s digital era, businesses rely heavily on data and AI for competitive advantage and growth. This presentation, led by Melecio Valerio from Maya, Philippines, delves into effective data governance practices! These practices enable organizations to extract valuable insights, inform strategic decisions, and overcome challenges. Challenges such as data quality issues, technical integrations, and ethical considerations in AI deployment!
Key takeaways include strategies for competitive data leverage, best practices for integrating data and AI, and guidance on data-driven transformation. The session emphasizes prioritizing Data Transformation over Digital Transformation and the need for robust data governance for sustainable success. It offers insights for data management, AI implementation, and strategic planning, ensuring ethical standards and regulatory compliance.
Add comment