About the Product Demo
DATAVERSITY Demo Day – Data Quality / DataOps is a curated virtual showcase designed to help data professionals explore the tools and solutions shaping modern data quality and DataOps practices.
AI agents now act on data faster than anyone can review each decision. When a record is wrong, the error travels downstream into AI models and dashboards before anyone catches it.
This Demo Day brings together solution providers tackling the problem from different angles: data quality for AI agents, data observability across pipelines, and real-time identity resolution. In each session, you’ll see the technology in action through a live product demonstration.
Whether you’re building trusted data foundations for AI or tracking down silent data failures, Demo Day offers a clear, time-efficient way to evaluate data quality tools and see how they could fit your environment.
Every Demo Day session is built to help you make smarter, faster decisions about data quality.
Sessions at DATAVERSITY Demo Day – Data Quality / DataOps
Session 1 · Informatica from Salesforce: Data Quality for AI Agents: Building the Foundation for Autonomous AI
In the agentic era, AI systems don’t just read data, they act on it. An agent making thousands of decisions an hour inherits every flaw in the data beneath it. A duplicate record, a stale address, or a mismatched identifier no longer surfaces as a bad report. It compounds into bad actions at machine speed, and no one is reviewing each one before it lands.
Data quality underpins trusted AI. AI-powered profiling, rule-based standardization, validation, fuzzy matching, and continuous monitoring help ensure agents work from data that is accurate, complete, consistent, and trustworthy across cloud and on-premises sources. Data quality automation continuously identifies anomalies, enforces quality rules, and supports remediation as data moves through the enterprise, rather than waiting for issues to surface downstream.
See how modern data quality capabilities help detect, diagnose, and address data quality issues before they impact AI outcomes, and walk away with a practical blueprint for building trusted data foundations for AI and autonomous agents.
Session 2 · Datadog: Ensure Trust in Your Data From Pipelines to Prompts
Bad data doesn’t announce itself. It shows up as a broken dashboard, a customer complaint, or an AI model quietly producing wrong answers. By the time your team finds out, the damage is already done and trust erodes before you can fix it. As AI raises the stakes on reliable data, the cost of getting this wrong keeps climbing.
In this session, we’ll show you how Datadog Data Observability helps teams detect, resolve, and optimize data quality and pipeline issues before they cascade downstream to AI models, BI dashboards, and customers. You’ll see how ML-powered anomaly detection surfaces silent data failures early, how end-to-end lineage helps you pinpoint root cause and blast radius in minutes, and how jobs monitoring keeps pipelines healthy and costs under control, all in one place.
Session 3 · Senzing: Solve Identity Chaos in Real Time: No Rules, No Tuning, No Black Box
This isn’t just a data quality session. It’s a look at the Identity Intelligence layer your data quality program is missing.
You’ve been working to dedupe, standardize, and build the golden record. But one customer, patient, or counterparty still shows up as five different records across five different systems, all disagreeing with each other. That’s not a hygiene problem; it’s Identity Chaos.
This session will show you exactly how this gets solved: Continuous Identity Intelligence — a real-time, explainable layer that resolves who’s who and who’s connected to whom, so fraud detection, customer 360, and compliance screening finally work from the same ground truth.
You’ll see how Senzing:
- Resolves messy, real-world data with zero rules written — no match-rule authoring, no threshold-setting, no configuration before we start
- Self-learning, self-correcting — as new records arrive, earlier conclusions are revised on the fly, no waiting on a batch job to catch up
- Identifies non-obvious relationships — hidden connections, like shared addresses, phones, and employers, that link entities, not just match records
- Explains the decision, not just asserts it — why that match was made, why a similar-looking record wasn’t, traced back to the source records, not a black box
Where Online Conversations Become Real-World Connections
Join us this November in Providence, Rhode Island, for DGIQ + AIGov 2026 – where the insights you hear online turn into hallway conversations, meaningful connections, and collaborative breakthroughs.
