Prerequisite: None
Establishing a robust enterprise data management organization is pivotal for success in today's AI-driven business landscape. To efficiently achieve business objectives, you must be able to scale data management capabilities across the enterprise using AI technologies. Despite the familiar journey toward accomplishing data management goals, the advent of AI introduces new challenges in terms of data volume, diversity, and resource constraints. Failure to integrate AI effectively into data management processes impedes organizations from meeting enterprise goals.
This session will outline various options for structuring enterprise teams to optimize AI capabilities in designing data architecture, storage, integration, and governance. We will emphasize the importance of and approaches to secure leadership sponsorship in fostering a data-driven culture within the workforce, breaking down silos between data engineering and analytics teams for collaborative success.
Key takeaways include:
- Critical success factors in designing AI-driven data management functions
- Essential components of an AI-driven data engineering operating model
- Required skill sets for effective data management in the AI era
- Strategies for defining team structures to seamlessly integrate engineering and analytics capabilities, maximizing the potential of AI in data management activities