Dinesh Thangaraju
Head of AWS Data Platform, Amazon Web Services

As organizations race to deploy AI agents across their enterprises, they're encountering a critical bottleneck: fragmented knowledge bases and siloed agent capabilities that prevent comprehensive, cross-domain insights. This presentation explores how to address this challenge through a federated approach to enterprise agentic AI infrastructure. This session examines three fundamental challenges facing enterprise AI adoption:

The Knowledge Fragmentation Problem: Organizations are independently building knowledge bases for specific use cases, leading to duplicated effort, inconsistent data parsing strategies, and context drift as source documents evolve. Each team creates their own chunking, embedding, and retrieval mechanisms without a unified architecture—resulting in AI agents that cannot access cross-functional domain knowledge or maintain accuracy at scale.

The Agent Collaboration Gap: Today's AI agents operate in isolation within their domains. When business leaders ask complex questions spanning customers, pricing, services, and operations, they receive fragmented answers requiring manual synthesis. Without standardized agent-to-agent communication mechanisms, authentication frameworks, or centralized agent registries, organizations cannot deliver the holistic insights that drive strategic decision-making.

The Governance and Innovation Paradox: Enterprises need to enable rapid experimentation while maintaining security, compliance, and user experience standards. Traditional centralized approaches stifle innovation; purely decentralized approaches create chaos. The challenge is building federated frameworks that guide discovery, assessment, incubation, and graduation of AI solutions without creating bottlenecks.

This presentation introduces a three-pillar architecture for enterprise agentic AI:

Unified Federated Knowledge Base: A bottom-up approach where domain teams create specialized knowledge bases that integrate into an organization-wide ecosystem through standardized ingestion pipelines, interfaces for vector databases and knowledge graphs, and evaluation frameworks for accuracy and relevance.

Cross-Domain Agent Collaboration: Technical mechanisms enabling AI agents to discover, authenticate, and communicate with each other—transforming isolated data points into comprehensive business intelligence that spans organizational boundaries.

Federated Innovation Framework: Secure sandbox environments with structured governance models that accelerate development time by 50% while maintaining enterprise standards for security and user experience.

Attendees will learn:

• Practical patterns for building federated knowledge architectures that eliminate redundant development efforts
• Technical approaches to agent-to-agent communication, including authentication, authorization, and service discovery
• Governance models that balance innovation velocity with enterprise security and compliance requirements
• Implementation strategies for co-owned initiatives requiring commitment across multiple data organizations

This session is essential for Chief Data Officers, enterprise architects, and data leaders navigating the transition from siloed AI experiments to scalable, federated agentic AI ecosystems that deliver measurable business value.

Elena Alikhachkina
Chief Data and AI Officer, TE Connectivity

Danette McGilvray
President and Principal Consultant, Granite Falls Consulting, Inc.

Have you ever heard (or thought) the following: 

• What we really care about is AI – what’s the big deal about data quality?
• We know our organization has a data quality problem, but where do we start?

Data quality really is the secret sauce that brings together all things data with what is really important to our organizations such as AI, customer satisfaction, efficiently providing products and service, managing risk, etc. Data quality is the underlying flavor that makes everything work better and taste so good, but too many people just can’t put their finger on it.  Join us as Danette McGilvray shares key ingredients to preparing a delicious dish of high-quality data that will satisfy the appetite of what you care about in your organization – because everything depends in some way on data.

Robert Abate

Robert Abate will present a summarization of the publication of “CDO First 90 Days” best practices in a presentation. The presentations purpose is to enable and empower CDO’s with the primary requirements of the position and establish a cadence with business decision makers.

This presentation, discussion and audience participation should provide for a number of benefits and this discussion will include the following topics:

• First 30 Days Best Practices
• First 60 Days Best Practices
• First 90 Days Best Practices

Attendees Will Learn:

• Challenges and considerations of a starting CDO
• How to utilize key concepts learned over 10 years by CDO’s
• What does the future hold in store?

Douglas Laney
Data, analytics and AI advisor, reseacher, and author

The autonomous organisation represents the next frontier in business transformation, moving beyond advanced analytics, piecemeal AI, and single-function automation. We are on the precipice of true organizational “self-driving” capabilities which will redefine business models and economies. While many enterprises still struggle with basic AI implementations, leading organisations are poised to introduce intelligent systems that can sense, decide, perform, interact, and adapt across entire business functions at digital speed.

Drawing parallels with the evolution of autonomous driving, Laney will define the seven levels of agentic AI – from early-stage chatbots to full business self-awareness and execution – and share new technology providers and business executives can prepare for this imminent inevitability.

Peter Aiken
Associate Professor, Virginia Commonwealth University
Founding Director, Anything Awesome

Everyone wants to use data to add value to their organizations.  The really important question is:  how can organizations achieve more effectively data practices?  This has been difficult to correct because, to quote Einstein:  The significant problems we face cannot be solved at the same level of thinking we were at when we created them.  Poor data education has led to naive understanding that, when combined with a technology-first, bias has prevented the vast majority of organizations from making tangible progress.  This, in spite of significant investments in hype such as big data science.  Before attempting data improvements, organizations must resolve flawed decision making about data issues.  The  briefly takes senior executives through a data awareness journey, transforming their thinking about data.  It provides an opportunity to better understand the kind of people and process decisions that will most speed-up your organization's ability to better leverage data.  Many examples illustrate the material.

Derek Strauss
Founder, CEO & Principal Consultant
Gavroshe

Transitioning the CDO from a Cost Center to a Profit Center

In the modern business landscape, organizations are inundated with vast amounts of data generated from customer interactions, operational processes, and external partnerships. Despite this abundance, many struggle to convert this data into actionable insights and tangible financial gains.

AI-driven Knowledge Governance is emerging as a transformative framework, utilizing the Data Liquidity Protocol (DLP), which revolutionizes data governance by embedding cryptographic proofs directly into data products. This ensures that every data product is verifiable, secure, compliant, and monetization ready.

DLP is unique because it binds five essential cryptographic proofs to every data product:

• Proof of Integrity: Ensures data provenance and quality.
• Proof of Ownership: Provides enforceable control over data assets.
• Proof of Security: Implements layered cryptographic protection.
• Proof of Compliance: Embeds privacy-preserving proofs to comply with regulations.
• Proof of Value: Measures ROI from every data use case.

By combining Knowledge Governance with DLP, organizations can automate compliance, manage risk, enhance data quality, unleash innovation, and unlock new revenue streams. This synergy allows organizations to transform raw data into valuable, marketable assets without exposing sensitive information or violating regulatory standards.

Tom Davenport
Distinguished Professor
Babson College

Increasing numbers of CEOs are declaring that their companies are "all in on AI." Tom Davenport has researched this concept and described it in a book by the same name. In this presentation and discussion he will describe the attributes of companies that are highly committed to AI, discuss all-in approaches to analytical, generative, and agentic AI. and present several leading examples of such firms. He will also describe the attributes of AI leadership--both for CEOs and CDAOs--and provide examples of successful ones.

Tom Redman
The Data Doc, President
Data Quality Solutions

By now everyone knows that AI succeeds or fails on the quality of answers/inferences/predictions it returns.  (Poor) data quality  also gets in the way of day-in, day-out work and good decision-making.  The usual response is to try to find the errors and clean them up.  It is time-consuming, expensive, and frustrating.  And plenty of errors leak through.  No wonder people don’t trust data.

Fortunately there is a better way:  Pro-actively finding and eliminating the root causes of all those errors!  Saves time and money.  Builds trust.  And people like the work!

In this workshop we’ll craft the business case for data quality by comparing “clean-up” versus “eliminating root causes.”  The goal is to help participants grow increasingly intolerant of bad data and start addressing the issues pro-actively.

Mark Ramsey
Managing Partner
Ramsey International LLC

In the era of exploding demand for Generative AI, organizations face unprecedented challenges and opportunities in modernizing their data strategies. This session will explore how a robust data foundation—encompassing governance, quality, integration, and analytics—is essential to unlock AI’s full potential. Dr. Ramsey will share his insights on the immediate actions Chief Data Officers should take to transform data practices, build scalable and trustworthy data architectures, and leverage AI-driven innovations. Attendees will learn how to stay competitive by aligning their data strategies with the rapid growth of Generative AI, ensuring operational efficiency, new revenue streams, and sustained leadership in a data-driven organization.