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.
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 1: Mastering Data Quality: Control, Monitor, and Leverage Trusted Data for Smarter Decisions
Reliable data quality drives smarter business decisions and trustworthy AI outcomes. Managing data quality across your ecosystem can be complex, especially without technical expertise. Learn how Informatica Intelligent Data Management Cloud™ (IDMC) empowers you to easily monitor, curate, and control data quality—unlocking its full value without the need for developers.
Session 2: Automate Data Trust: From Assessment to Action at Scale
AI and analytics work only when your data earns trust. This demo shows how Precisely helps you assess, standardize, and strengthen the quality of your most important data—automatically and at scale—so your insights and models rest on a reliable foundation.
You’ll see how to:
- Profile and score data to spot quality risks that hold back AI and analytics
- Recommend and apply rules that improve consistency and completeness
- Normalize and enrich values—including address and location data—to improve accuracy and everyday usability
- Centralize data quality logic so you get transparency, consistency, and reusability across your data domains
Precisely’s data quality and governance agents bring intelligent automation to these workflows, connecting data-quality improvement with policy enforcement so you can deliver explainable, high-quality data for confident AI and analytics outcomes.
Whether you’re training models, delivering insights, or scaling enterprise analytics, this session helps you embed trust at the heart of your data strategy.
Session 3: AI-Powered Data Intelligence: Governing Enterprise Data for Trusted Analytics and GenAI
Unstructured and structured data now power AI-driven decision-making across regulated industries—but most governance programs were not designed for AI-scale access and automation.
As GenAI adoption accelerates, organizations need more than metadata visibility. They need intelligent, governed access to data that is secure, explainable, and compliant by design.
In this session, we’lldemonstrate how IBM delivers a fully managed data intelligence platform that unifies discovery, governance, quality, lineage, and AI-powered automation across hybrid and multi-cloud environments. A live demo will showcase GenAI capabilities including conversational data discovery, text-to-SQL for governance workflows, and MCP tools that provide standardized, secure access to enterprise data.
Attendees will see how teams can accelerate analytics and AI initiatives, automate governance tasks, and enable trusted AI interactions—all accessible through a governed Model Context Protocol (MCP) server that ensures consistent controls, full lineage transparency, and enterprise-grade policy enforcement.
Session 4: 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.
Session 5: AI Doesn’t Know Fit for Purpose: Introducing the Validate-Before-Use Model
AI copilots and autonomous agents now retrieve data dynamically and trigger actions across enterprise systems. But most data quality programs still rely on pipeline checkpoints and downstream monitoring, approaches built for a slower, more predictable world.
In this live demo, Qualytics introduces the Data Quality Context Layer, an extension of the Augmented DQ architecture that embeds governed data quality signals directly into AI reasoning and execution.
Using a validate-before-use model, organizations can evaluate data quality the moment AI systems access it. You’ll see how the Qualytics MCP Server brings quality context into copilots and how the Agentic API enables autonomous agents to enforce data quality thresholds before executing workflows.
What You’ll Learn
- Why traditional data quality models break in AI and agentic environments
- How the validate-before-use model shifts governance to the moment of data use
- What the Data Quality Context Layer provides for AI systems in production
- How MCP and the Agentic API allow copilots and agents to evaluate data quality in real time
