We’ve officially moved past the era of “data for data’s sake.”

Today, the goal isn’t just to collect more. It’s to make smarter decisions, faster. Whether you’re building machine learning models, enabling self-serve dashboards, or fine-tuning your product analytics, one thing is clear: your AI and BI are only as good as the data behind them.

At MDS Fest 3.0, our AI, Analytics & Business Intelligence track is all about what happens after the data lands. How teams transform raw inputs into usable insight. How models become trustworthy. How organizations democratize data, without losing control.

🎫 Registration for MDS Fest is open, and every single session is free. Get your ticket here. 🎫

Moving from the wild west to actual workflows

There’s been a massive shift in how companies think about AI and BI. It’s no longer about siloed efforts or one-off dashboards. It’s about building systems that are:

✅ Repeatable
✅ Scalable
✅ Governed
✅ Designed for action

This track brings together the people doing just that: turning analytics into alignment, AI into advantage, and dashboards into daily drivers of the business.

AI is not magic

If you’ve ever watched a model fall apart because the data was incomplete, unlabeled, or misunderstood, you’re not alone.

This track goes deep on the workflows, processes, and tooling required to make AI actually work, and work at scale. That includes better lineage, stronger context, tighter feedback loops, and shared understanding between data producers and consumers.

It’s not about AI hype. It’s about AI that performs, because the foundation is solid.

Who should tune in?

If you’ve ever found yourself thinking, “We have the data—why don’t we trust it?”—this track is for you. We’re calling all practitioners, analysts turning noise into narratives, data scientists who are fighting for clean inputs, ML engineers trying to scale good ideas, and product teams relying on dashboards to drive outcomes.

Agentic AI with open source data architecture

A talk by: Andrew Madson, Head of Evangelism & Education, Tobiko Data

Agentic workflows are changing how data teams operate. This session explores how to integrate open-source tools with AI agents to power dynamic analytics systems. Learn how agentic AI can complement modern data architecture to drive automation and flexibility.

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Democratizing analytics via self service tooling

A talk by: Bhargavi Reddy Dokuru, Senior Data Engineer, Netflix & Shruti Khanna, Manager of Data Science and Engineering, Netflix  

Visualization tools have made data more accessible—but real self-service requires more than dashboards. This talk shares how organizations can empower users with intuitive, governed analytics tools that drive adoption, reduce friction, and maintain data integrity.

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When data serves data: How data teams could use AI agents to be more productive

A talk by: Christophe Blefari, Data Engineer & Co-Founder, nao Labs & Claire Gouze, CEO and Co-Founder, nao Labs

AI agents are changing the way data teams work internally. This session explores how intelligent agents can automate documentation, generate queries, and accelerate analysis—allowing data professionals to spend less time chasing answers and more time creating value.

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Where product analytics fails: Measuring invisible experiences

A talk by: Dori Wilson, Head of Data, Recce

Traditional product analytics assumes visible, trackable events—but what about the unseen friction points? This talk dives into how teams can identify and measure "invisible" experiences to create more holistic, human-centered product insights.

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AI agents for data analytics

A talk by: Dylan Watt, Founder, Kadre LLC

AI agents are all the rage—but how can they enhance analytics? This session outlines how agents can automate insight generation, surface deeper patterns, and accelerate workflows, all while keeping humans in the loop.

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Measuring the user experience and the impact of design

A talk by: Maggie Wolff, Data Scientist, Box

When is a design decision actually working? This talk bridges UX and analytics by introducing methods to quantify user experience impact. Expect frameworks for measuring qualitative feedback and identifying behavior-driven design success.

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BI-as-code and the new era of GenBI

A talk by: Michael Driscoll, CEO and Co-Founder, Rill Data

BI-as-code is reshaping analytics engineering—and generative BI is up next. This session walks through how GenBI enables analysts to build, test, and deploy dashboards and insights with version control, automation, and AI-powered suggestions.

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Using analytics to improve the quality of audits

A talk by: Monica Kay Royal, Founder, nerdnourishment

Audit departments often rely on manual, retrospective reviews. This session explores how analytics can improve audit quality through continuous monitoring, pattern detection, and anomaly identification—making compliance more proactive and insightful.

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Data-logues with AI: Prompting my way to productivity

A talk by: Rakhi Reddy, Analytics Engineer, Gitlab

Prompt engineering isn’t just for language models—it’s becoming a skill for data professionals too. This talk shows how prompting AI can streamline everyday workflows, from documentation to query writing, making data work faster and more fun.

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Impact of generative AI on risk management in financial services

A talk by: Ritu Sharma, Lead Data Scientist, JPMorgan Chase

Generative AI brings both promise and risk to finance. This session explores how financial institutions can balance innovation with security, using governance frameworks to evaluate, monitor, and mitigate AI-related risks at scale.

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Join us at MDS Fest 3.0

Join us at MDS Fest from May 5 - 9 to explore the cutting-edge of data-driven strategies, and take home actionable insights that could redefine the future of your career.

Get your tickets at mdsfest.com today.