Elena Alikhachkina
Chief Data and AI Officer, TE Connectivity

TBD

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

TBD

Dr. John R. Talburt
Distinguished Professor of Information Science, UA Little Rock

TBD

Robert Abate
Senior Advisor, CDOIQ

TBD

Stuart Madnick
Professor & Founding Director, Cybersecurity at MIT Sloan (CAMS)

TBD

Douglas Laney
Research and Advisory Fellow, BARC

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.