The data landscape is changing fast, and for many organizations the biggest driver is growth in analytics and AI/ML. The days of limited and predictable data interaction through BI reports and dashboards are giving way to not only formal data science but also democratized, “citizen” data science and analytics modeling. Organizations are also deploying data-driven applications that use embedded analytics for faster human decisions and automated actions. It is no longer uncommon to ingest petabytes of varied data in cloud data lakes and data lakehouses. Some organizations are setting up data fabric and data mesh architectures for distributed data environments.
This panel discussion will focus on overcoming challenges and developing the best data strategy to support analytics and AI/ML. Topics we will discuss include:
- Provisioning data to accelerate analytics and AI/ML value
- Improving trust in data quality and completeness for analytics and AI/ML
- Data governance, regulatory adherence, and explainability requirements
- Overcoming distributed data silo and fragmentation challenges, including in hybrid multicloud environments
- How to reduce costs for analytics and AI/ML data management