AI is everywhere. GenAI seems to have taken over the minds of many managers, executives, investors, and business leaders. But in reality, there is a significant amount of confusion of where AI can help us in our daily lives and work and where AI can be leveraged to make work better, faster, and easier. In this session we will talk about AI for personal productivity, AI for team improvement, and AI for strategic advantage. We will talk about Predictive AI, Causal AI, and, of course, Generative AI. Bring your questions, we will engage in a lively dialog about where the world of work really stands in relation to the advance of AI.

Presented by: John Thompson, University of Michigan

John is an international technology executive with over 38 years of experience in the fields of data, advanced analytics, and artificial intelligence (AI). John is the Vice President, Product Analytics at CCC Intelligent Solutions. John’s responsibilities include designing, developing, and deploying innovative ways to integrate data, deliver insights, and to apply Predictive AI, Generative AI and Causal AI to CCC products and services.

John is a Lecturer at the University of Michigan in the School of Information (UMSI) and Ross Business School where he teaches classes based on his books and on the subjects of AI, and Building and Managing Analytics teams. He was recently named an Innovation Fellow for the Center of Academic Innovation.

Previously, John was the global head of AI at EY. His role was to actively lead the design, development, implementation, and use of innovative AI solutions, including Generative AI, Traditional/Classical AI, and Causal AI, across all of EY Service Lines and functions and for EY's clients. The team is comprised of - an Applied AI Research group, a product development organization, and an AI Consulting Practice. 

In today’s business environment, making smart, data-driven decisions in real time is essential for maintaining efficiency and quality in business operations. Join us to explore Microsoft Fabric Real-Time Intelligence and discover how to seamlessly ingest, analyze, and act on real-time data.

Learn how to:

1. Ingest real-time data from multiple sources into Fabric

2. Run lightning-fast queries for instant insights

3. Monitor key metrics with intuitive, real-time dashboards

4. Set up automated alerts and detect anomalies for proactive issue resolution

5. Leverage Copilot in Real-Time Intelligence to query data using natural language

Presented by: Mou Rakshit, Accenture

A seasoned data and analytics leader with 25+ years of enterprise experience, Mou Rakshit is a trusted architect of modern data platforms and scalable governance frameworks. As Group Manager of Data, AI & Analytics Engineering at Accenture/Avanade, she leads the design and delivery of lakehouse solutions that transform how organizations leverage their data for competitive advantage.

Her hands-on expertise spans Python, SQL, Tableau, and cloud platforms including Databricks, Azure, and AWS. Recognized as a Databricks Certified Data Engineer and CBIP Professional, Mou has delivered over 4,500 hours of professional enablement, speaking at conferences on modern data platforms and AI readiness. As an Adjunct Professor in Business Analytics at Trine University, she equips emerging leaders with real-world expertise.

Mou's mission: to help organizations unlock their data's full potential while building high-performing teams that drive measurable business impact.

Organizations invest millions of dollars in their data infrastructure with the goal of enabling data‑driven decision making, yet many fail to maximize the full value of those investments. The presentation argues that comprehensive data literacy initiatives are the missing link between well-funded data infrastructure and the employees expected to harness them. Data consumers within the business frequently lack the fundamental, tool‑specific, and domain‑specific skills needed to interpret, question, and apply data effectively. By establishing comprehensive data literacy programs, businesses can bridge this gap and empower employees at all levels to become confident, competent data users. The session will demonstrate how improved data literacy enhances enterprise value, drives innovation, strengthens customer service, and increases overall employee effectiveness. Attendees will also gain an understanding, along with a practical framework, for how to design a data literacy initiative that unlocks the full potential of their robust data infrastructure.

Presented by: Josh Kahl, Health Alliance Plan

Josh is a data literacy leader at Health Alliance Plan who specializes in helping organizations unlock the full value of their data investments by empowering employees with the skills and confidence to use data effectively. He designs and scales enterprise-wide data and AI literacy initiatives that have upskilled hundreds of employees, strengthened business impacts, and delivered measurable financial impacts through foundational, tool-specific, and domain specific programming. Josh blends strategic leadership, technical expertise across the Microsoft data stack, and tactical know-how to enable teams to deliver true data-driven results. Josh brings a practical, people‑centered approach to bridging the gap between robust data infrastructure and the workforce that uses it.

Help! I’m drowning in dashboards!

Most companies are drowning in information. Tools like PowerBI or Tableau promote self-service dashboards, and this sounds so tempting to overworked analysts. Sadly, then reality hits. We are flooded with resource-hogging, poorly designed dashboards that contradict, often break, and have poor performance. Yet no one is willing to shut things down and centralize everything. After all, who has the time and resources to build every metrics request?

This interactive session will take you through a step-by-step journey to clearly define and promote excellence in data visualization. Based on best practices from Fortune 500 companies we will show you a way to balance centralization and standardization, while still allowing self-service. Sound too good to be true? Come and hear our story of transforming our chaotic world of dashboards at Best Buy.

Presented by: Jeff Nieman, Best Buy

Jeff Nieman is currently the Senior Director of Data Strategy and Visualization at Best Buy, where he leads a global organization focused on data governance, data analytics and data reporting/visualization for Best Buy. Jeff’s passion is for everyone to have the ability to tell and understand their story through data, and previously he has led data science teams at Cisco, Ford, and McDonald’s. Jeff has an undergraduate degree in engineering from the University of Michigan (Go Blue!), and a Master’s Degree in Data Science from CUNY, where he also serves as adjunct faculty in their graduate data science program. Jeff is part of the IIA Analytics Expert Network, is a frequent presenter at conferences and events, and in 2025 he was named one of the Top Data Science Transformers as a recipient of the prestigious AI Top 100 award.

This session presents a technical case study of designing, implementing, and scaling an enterprise data lake at an S&P 500 company using Azure Data Factory, Snowflake, and Power BI—delivered by a lean, three-person internal data engineering team.

Rather than leading with heavy governance and upfront optimization, we prioritized ingestion reliability, scalable storage, and analytics-ready data models to support immediate business needs. As platform usage increased, technical constraints surfaced around access control, auditability, cost management, and operational consistency. These challenges drove the introduction of role-based access control, standardized ingestion patterns, Snowflake cost optimization techniques, and supporting documentation.

The session focuses on architectural decisions, sequencing trade-offs, and operational lessons learned, offering practical guidance for engineers and architects building data platforms under real enterprise constraints.

Presented by: Ioana Gonçalves, BorgWarner

Ioana Gonçalves is a Data Engineering Manager who led the creation and evolution of an enterprise‑scale data lake at an S&P 500 company using Snowflake, Azure Data Factory, and Power BI. She specializes in turning ambiguity into structure, building lean and effective data engineering practices, and enabling organizations to make better decisions through scalable, reliable data platforms.

She brings extensive international experience in data warehousing for the banking and automotive industries, along with deep expertise in data modeling gained through work in Europe with Oracle and other software companies.

In today’s data-driven world, the fusion of Big Data Engineering and Artificial Intelligence is revolutionizing how organizations unlock value from their information assets. This session explores the foundational pillars of Big Data—Volume, Velocity, Variety, Veracity, and Value—and demonstrates how robust data ingestion, scalable storage, and advanced processing architectures form the backbone of trustworthy AI systems. Attendees will discover how high-quality data pipelines fuel training, feature stores, and real-time inference, while governance, observability, and security ensure reliability and compliance at scale. The talk will highlight strategies for engineering trust, including metadata management, lineage tracking, and cost-aware AI, empowering organizations to build resilient, explainable, and future-ready solutions. Join us to learn how integrating immutable data modeling and shared feature logic can drive innovation, reproducibility, and point-in-time correctness—transforming Big Data from a technical challenge into a strategic advantage for AI excellence.

Presented by: Amit Meshram, JPMorganChase

Amit Meshram is an Executive Director and Principal Software Engineer with over 20 years of experience building and governing large-scale, mission-critical systems at enterprise scale. He specializes in big data platforms, low latency design, AI-driven systems, cloud and distributed architectures, and resiliency engineering that power hundreds of millions of transactions daily. A recognized technology leader and inventor with multiple patents, Amit bridges deep engineering rigor with real-world business impact, helping organizations design systems that are not just scalable—but trustworthy, resilient, and future-ready.

Chris Smith is a Senior Solution Engineer at Snowflake where he is a thought leader for data and AI, crafting solutions on Snowflake for all the largest Automotive OEMs in the world. Prior to Snowflake, Chris built an extensive background in the automotive industry, working directly within the IT and analytics space at companies like GM and Stellantis for 15 years. He has a Bachelor of Science in Computer Science & Mathematics and an MBA focused on Management Information Systems, which allow Chris to bring a unique blend of technical expertise, business acumen, and practical industry knowledge to his customers. Beyond his professional pursuits, Chris is an avid DIY enthusiast, tackling home renovation projects and working on cars in his spare time. He finds balance in his personal life by dedicating quality time to his wife and four young children.