Roxane Edjlali

No data? No AI! No metadata? No data! Gartner codified AI-ready data concepts and crafted the language in mid-2022. Practices have evolved for the four AI-ready data modes and 30 expected data evaluation and preparation steps. In this session, we recap the approach combined with how organizations are using AI data readiness as a foundation to evolve toward standardizing data for production AI.

This multigroup session will focus on questions like: - How should organizations implement AI governance frameworks to manage risk, ethics and accountability? - How can organizations prepare and assess their data, infrastructure and workforce to ensure AI readiness. - What are the best practices for the above questions and what are the challenges and barriers to implementation of those practices?

D&A leaders have a key strategic decision to make over the next few years. What does their strategic and long-term data management platform looks like and where to source it from? There are four options that this session will discuss: utilizing the all encompassing data and AI platform from their cloud service providers, extending their ISV solution providers to enable their data platform, engaging their enterprise SaaS application providers to support D&A use cases, or taking a blended approach.

In this roundtable, D&A leaders will discuss how they are balancing the centralization and decentralization of data management, empowering LOBs to achieve self-sufficiency while leveraging the benefits of a centralized data management function. They will also address how to maintain control over local initiatives and prevent the spread of risky patterns that are misaligned with governance policies.

Data engineering faces increasing pressure to deliver AI-ready data that meets agentic AI requirements. This session explores changes in data engineering functions, practices, architectures and technologies.

No data? No AI! No metadata? No data! Gartner codified AI-ready data concepts and crafted the language in late-2022. Practices have evolved for the four AI-ready data modes and 26 expected data evaluation and preparation steps. In this session, we recap the approach combined with how organizations are using AI data readiness as a foundation to evolve toward standardizing data for production AI.

In this roundtable, D&A leaders will discuss how they are balancing the centralization and decentralization of data management, empowering LOBs to achieve self-sufficiency while leveraging the benefits of a centralized data management function. They will also address how to maintain control over local initiatives and prevent the spread of risky patterns that are misaligned with governance policies.

In this roundtable, D&A leaders will discuss how they are balancing the centralization and decentralization of data management, empowering LOBs to achieve self-sufficiency while leveraging the benefits of a centralized data management function. They will also address how to maintain control over local initiatives and prevent the spread of risky patterns that are misaligned with governance policies.

Data engineering faces increasing pressure to deliver AI-ready data that meets agentic AI requirements. This session explores changes in data engineering functions, practices, architectures and technologies.