About the Product Demo
DATAVERSITY Demo Day is a curated virtual showcase designed to help data professionals efficiently explore tools and solutions that support modern data management practices.
In response to growing demand from our community, we created a vendor-driven experience that brings you direct access to live product demonstrations, use cases, and Q&A sessions, all from solution providers shaping the future of data governance, quality, architecture, and AI.
Whether you’re researching new technologies, comparing options, or staying informed on what’s next, Demo Day offers a clear, time-efficient way to evaluate the tools that can move your data strategy forward.
Each session is focused, informative, and built to help you make smarter, faster decisions.
Session 1: Informatica Demo: From Data and AI Chaos to Trusted Business Outcomes
Businesses are rapidly adopting AI, but projects without strong data and AI governance foundations often face challenges scaling from initial adoption to production. Informatica will demonstrate a practical path from risks and complexities to trusted, repeatable results: inventory your data, control how it’s used, deliver certified data for AI, and observe continuously.
Session 2: AI Is Moving Faster Than Our Governance Systems—So What Do You Actually Need: Metadata Management or a Governance Platform?
AI adoption is accelerating at a pace most governance systems were never designed to handle. Models are trained, data is shared, and insights are generated in near real time—while many organizations still rely on governance approaches built for slower, more static data environments.
This creates a critical question: Is a metadata solution enough, or do you need a full data governance and intelligence platform?
In this session, we’ll challenge common assumptions about metadata-first strategies and explore where metadata delivers value—and where it falls short as AI scales. We’ll examine why metadata alone often stops at visibility, while modern governance platforms extend metadata into automation, enforcement, and continuous control.
You’ll learn how organizations are:
-
Preserving and reusing existing metadata investments without disruption
-
Deciding when metadata is sufficient—and when it becomes a constraint
-
Moving from documentation to execution with data quality scoring, lineage, secure data sharing, and AI-ready governance
Through a live demonstration of watsonx.data intelligence, we’ll show how metadata can remain the foundation—while governance intelligence ensures data is trusted, compliant, and usable at the speed AI demands. This session isn’t about replacing what you have.
It’s about understanding what’s required when AI outpaces governance—and choosing the right approach for your environment.
When you interact with IBM, this serves as your authorization to Dataversity or its vendor to provide your contact information to IBM in order for IBM to follow-up on your interaction. IBM’s use of your contact information is governed by the IBM Privacy Policy
Session 3: Accelerating AI-Ready Data With Data + AI Observability
Traditional approaches to data quality aren’t cutting it in the agent era. Data reliability incidents are missed and take too long to resolve, putting highly visible AI projects at risk of being delayed or derailed.
See why Gartner calls data observability one of the core capabilities for AI-Ready data.
Learn how to:
- Detect incidents before your stakeholders— with no-code validations and automated anomaly detection that scales. Understand common monitoring strategy best practices.
- Avoid alert fatigue — and ensure your team responds to alerts with urgency by getting the right alert to the right team alongside automated impact analysis.
- Resolving issues fast— and experience how the Troubleshooting Agent can automatically identify the root cause of data quality issues in less than two minutes.
- Build data trust— by capturing and surfacing data health of your data products with the SLAs your organization cares about.
- What world-class teams do differently — get real-world examples of how leaders like Calendly scaled reliable data operations with Monte Carlo.
