Speaker Bio

Georgia O'Callaghan

Georgia O'Callaghan

Sr Principal Analyst
I am a Senior Principal Analyst with the Analytics & AI team, lead by Carlton Sapp, specializing in business intelligence (tooling, self-service architectures, data literacy, self-service governance, etc.) and feature stores (a tool used in ML model development).
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Monday, 22 May, 2023 / 09:30 AM - 10:15 AM BST
Gartner Opening Keynote: Lead for Purpose. Connect With Trust. Make an Impact.

Data & analytics leaders can deliver increasing value to their organizations. This session will uncover new research and trends which will enable you to find new powerful ways to connect with stakeholders, address skills shortages and build trust to overcome cultural resistance to change. Learn how to attract and retain a world-class D&A team and lead with purpose to ensure the data-driven transformation required for success.

Monday, 22 May, 2023 / 12:30 PM - 01:15 PM BST
Ask the Expert: Governing Self-Service Analytics

Organizations that lack a governance framework for self-service can struggle to maintain control as work becomes chaotic, siloed and inconsistent. This session will allow you to engage with a Gartner expert who will dive into the capabilities and features of popular A&BI tools and provide actionable advice on how these can be leveraged to govern data and analytics within self-service initiatives.

Monday, 22 May, 2023 / 12:30 PM - 01:00 PM BST
How to Rationalize and Consolidate Analytics Tooling

Many organizations today are experiencing expanding portfolios of analytics tooling. Tools with overlapping capabilities create opportunities for data duplication, siloes and inconsistencies in metrics. Organizations should simplify their analytics architecture by performing a rationalization and consolidation exercise. This reduces the burden on IT to support multiple tools and govern analytics.

Tuesday, 23 May, 2023 / 12:00 PM - 12:30 PM BST
Technical Insights: The Logical Feature Store: Managing Data for ML

Productionizing ML models remains a significant challenge for enterprises as they scale ML operations to hundreds or thousands of models. These organizations benefit from implementing a logical feature store to manage data for ML. Logical feature stores accelerate time to production and promote feature reusability, reproducibility and reliability.

Join us to hear from Gartner experts and thought leaders.