All day Workshop: Introduction to Building Data Foundations: From Messy Data to Scalable Analytics

This is a SPECIAL All-Day workshop and networking event that will walk participants through the core building blocks of modern data foundations, from architecture and modeling to shared definitions and AI-ready design. This is a uniquely exciting opportunity for those looking to understand and modernize their data infrastructure in finding new ways at building trust in your data across your organization.

This event is supported by the Malloy open-source project and its contributors (Google's Looker Cofounder: Lloyd Tabb and Michael Toy). This event is also supported by https://credibledata.com/context engine.

This interactive workshop is designed for people who work with data, rely on data, or are responsible for data outcomes. ex. Leaders impacting Data Strategy, Data Product Managers, Data Project Managers, Data Engineers, Aspiring Data Persons, Data Analysts, Data Scientists, Data Strategy Roles, Data Program Managers, Non-Profit Technical Teams or Data Teams, etc.

Why attend this event?

This event is important because most organizations don’t struggle because they lack data, they struggle because they can’t trust it.

One dashboard says revenue is up, another says it’s down. Teams spend meetings debating numbers instead of making decisions. Reports break when systems change. AI tools and Copilots promise insights but produce answers no one feels confident acting on.

This workshop breaks down (in plain language) why these problems happen and how modern organizations are able to design data foundations to avoid them. You’ll see practical examples of how inconsistent definitions, poorly designed reporting layers, and missing shared context lead to confusion, rework, and stalled initiatives.

By the end of the day, you’ll understand how data should be structured so the same numbers mean the same thing everywhere, new reports can be built without starting from scratch, and analytics and AI can be safely layered on as your organization grows without constant firefighting.

8:30 am – 3:00 pm; Format: Day workshop with structured networking;

Catering: Coffee on arrival + light lunch provided

Agenda: 

• 8:30 – 9:00 Coffee & Networking (30 min) Open Seating

• 9:00 – 9:15 Welcome & Context (15 min)

• 9:15 – 10:15 Modern Data Architecture Fundamentals (45-60 min) Dil Mustafa

• 10:15 – 10:30 Networking Break (15 min)

• 10:30 – 11:30 The Data Gold Layer Fundamentals & How to Compose It + Workshop (60 min) Suraj Lamgaday

• 11:30 – 11:45 Networking Break + Seat Rotation (15 min)

• 11:45 – 12:45 Data Semantic/Self-Service Design and Background (60 min) Miles Garvey

• 12:45 – 1:15 Light Lunch & Networking (30 min)

• 1:15 - 1:45 Semantic Design Workshop 

• 1:45 - 2:45 Data Design and ROI for AI Understanding (60 min) Kyle Nesbit

• 2:45 – 3:00 Interactive Group Session (15 min)

• 3:05 – 3:15 Ending Remarks (10 min)

Miles Garvey is a modern data strategist with cross-sector experience spanning public institutions, consulting, and high-growth private companies. With a background rooted in analytics engineering and data infrastructure, he has helped organizations evolve from fragmented reporting cultures into insight-driven, self-service ecosystems. Miles brings a holistic understanding of how data works in the real world. He speaks frequently about the evolving role of analytics teams, the future of data ownership, and how organizations can adopt AI-native infrastructure.

Most recently in the private sector, he led analytics and data platform initiatives at G2, where he built scalable data pipelines, implemented semantic layers, and AI-integrated analytics. Currently, Miles runs his own data advisory consultancy spanning larger institutions.

Dil Mustafa Dil Mustafa is a Data and AI architect and former data leader at Best Buy, focused on building scalable data platforms and warehouses that support reliable analytics and decision-making.  He specializes in designing end-to-end data architectures (from ingestion to modeling and semantic layers) helping organizations move from fragmented reporting to trusted, reusable data.

Dil has led 20+ data and AI initiatives, improving data consistency, reducing duplication, and enabling teams to work from a shared foundation. He emphasizes clear modeling, strong governance, and practical, maintainable design.  Based in Canada, he is currently an Enterprise Data and AI Architect at Datavise Consulting Inc., with previous experience at WestJet and Cenovus Energy.

Suraj Lamgaday  is a Data Lead at G2, where he leads the implementation of the company’s Golden Layer data architecture. He has helped build multi-million-dollar lines of business using data, developed full-stack analytics products for institutional investors, and partnered closely with business teams to turn raw data into trusted decision systems.

Prior to G2, Suraj overhauled enterprise data architecture and semantic layers at a top ten U.S. law firm and previously worked at JPMorgan Chase, contributing to Golden Layer initiatives across Asset Management and Commercial Banking. His work centers on building scalable, business-ready data foundations that organizations can rely on.

Kyle Nesbit is the Founder & CEO of Credible, a platform for engineering and delivering shared meaning across enterprise data. He’s a technical founder and engineering leader focused on semantic modeling, context systems, and AI-first developer workflows.

Prior to Credible, Kyle spent 17 years at Google, where he worked on high-performance distributed systems, machine learning in ads, and AI-powered analytics. He helped build core infrastructure behind BigQuery and later worked on bringing generative AI capabilities into Looker following Google’s acquisition. He also spent time with Gradient Ventures, Google’s AI-focused venture fund, gaining perspective on what makes AI systems scalable, reliable, and investable.

At Credible, Kyle is pioneering Engineering Meaning: treating business intent, definitions, and relationships as first-class, versioned, testable assets and delivering that meaning in context to analytics, applications, and AI systems.