This presentation provides a look at the multiple Hype Cycles that are pertinent to AI initiatives. This would include: Hype Cycle for AI, generative AI, data science and machine learning platforms, analytics and business intelligence, and possibly others.

Data architects are increasingly tasked with provisioning quality unstructured data to support AI models. However, little has been done to manage unstructured data beyond data security and privacy requirements. This session will look at what it takes to improve the quality of unstructured data and the emerging best practices in this space.

Traditional approaches and thinking around data quality are out of date and not sufficient in the era of AI. Data, analytics and AI leaders will need to reconsider their approach to data quality going beyond the traditional six data quality dimensions. This session will help data leaders learn to think about data quality in a holistic way that support making data AI-ready.

With new pressures coming from AI, CDAOs must be agile in implementing and enabling business innovation. But, with quick adaptation, inevitably comes stress on traditional D&A delivery processes and practices. Join this interactive session to lean how to: clarify strategies to support business innovation, design adaptable and scalable delivery models and collaborate with business units to ensure value of D&A capabilities and services.

D&A leaders scaling generative AI training and inference must navigate new technologies including GPUs and AI processors, both on-premises and in the cloud. In this session, we provide a pragmatic framework to map GenAI model sizes to infrastructures across on-premises and cloud environments.

Data and analytics leaders expect their data and analytics investments to deliver business results. But unless they address data, analytics and AI risk, their initiatives will fail, leading to higher business risk exposure. This session will help D&A leaders target three key areas where better data and analytics risk practices will yield better business results.

Asking your colleagues how analytics can help them often results in blank stares, defensiveness, or wildly incoherent suggestions involving AI. This session will show you how to work with your colleagues to pinpoint how you can help, identify the most helpful capabilities to build, and explain how to measure your impact.

Data ecosystems, built on data fabric design and infused with AI, promise an integrated, cost effective, and operationally simple approach to varied data management challenges. However, they don't yet always deliver on that promise. This research explores the maturity of various ecosystem components and provides a guide for D&A leaders and others looking to invest in data foundations for competitive differentiation.

D&A value is not possible without data storytelling that offers a better way to engage communication findings than just BI reporting or data science notebooks. Join this session to know about the fundamentals of data storytelling and how to fill the gap between data science speakers and decision makers. It further discusses how to tell the best data storytelling and how to upscale data storytelling for future in landscape of GenAI.

CDAOs and AI leaders are grappling with two crucial questions: 1. What public cloud provider should we choose for AI and GenAI initiatives, and 2. how do we assemble the right cloud architecture to scale and deploy AI more effectively?
This session compares public cloud AI and Generative AI architectures from AWS, Azure and GCP and provides insights on their points of differentiation.

D&A leaders must develop DataOps as an essential practice to redefine their data management operations. This involves establishing business value before pursuing significant data engineering initiatives, and preventing duplicated efforts undertaken by different teams in managing the common metadata, security and observability of information assets within the data platforms.

D&A leaders face challenges in identifying and engaging with data stewards to advance D&A governance efforts. This session will provide actions that leaders can take back to their organizations and begin to implement.

AI is having a huge impact, but is not the only thing with societal, technological, and organizational implications driving change in data and analytics. We examine trends in areas such as complexity, trust, and empowerment facing leaders and teams as they make decisions in all aspects of their bet-the-business D&A strategy.

Data and analytics leaders and their data engineering teams are tasked with evaluating and selecting data integration tools. However, there are many options, which can be confusing. This session will explain the various types of data integration tools and technologies available in the market, and help you select the right data integration tool for your needs.

In this session, we will explore cost transformative vendors such as DeepSeek and MistralAI’s impact on AI development, highlighting the potential to enhance enterprise innovation at a price/performance that is unprecedented. Data, analytics & AI leaders can bring their questions on how they can capitalize on new training and inferencing paradigms in AI, the future of AI model development and the impact of reasoning models on a more autonomous future in AI.

Lakehouse has become a cornerstone in managing large and heterogeneous data by providing capabilities that simplify organizational data architecture, unify data assets, and help streamline, harmonize, and enhance data processes, operations and governance. Lakehouse provides high value to the organization, reduces technical debt, and prepares the organization for new frontiers like AI.

Let's kick this interaction off with some inputs from a Gartner expert and let the discussion evolve on its own. Join this session to offer your input to the data fabric and data mesh debate. Be prepared to challenge or reinforce positions from the market and discuss them with peers and a Gartner expert, who will cover these positions and their alternatives.

AI systems can have significant impact in organizations decisions and product offerings. This has triggered a complex regulatory landscape and related organizational guidance to ensure responsible and ethical implementation of AI systems. This session provides the technology framework supporting the processes and policies for a reliable AI system that is fair, safe and secure.

Creating a business case for GenAI is hard. GenAI initiatives produce outcomes that range from everyday to game-changing and cannot be described by a single currency of value. Gartner has identified three business case types and recommends CIOs create a value portfolio that encompasses the variety of GenAI investments enterprises make. Come to this session to learn how to frame your GenAI business case and align executive expectations on value creation from GenAI.

Heads of analytics & AI face many challenges such as keeping afloat, staying the course or racing ahead in the race for AI superiority. This session will focus on the top fears, risks, objections and hurdles leaders of Analytics & AI face and most importantly, how to handle them.

CDAOs are making investments daily. Perhaps you're looking to grow your team, or maybe making a technology investment to support GenAI, or another investment where you need to build buy-in and gain funding. This workshop will help you develop personal influence skills while also building a strong story for investment.

You've likely encountered human resistance to your data and analytics governance initiative, which can be frustrating because what you are doing seems like common sense. The very people you think your governance initiative will help can derail or block it. This session explores the people challenges encountered, and lessons learned on the journey to governance failures and successes.

The enormous potential business value of AI is not going to materialize spontaneously. AI leaders should guide their organization toward an era in which AI is not only creating tangible business value but goes beyond to become a critical competitive differentiator and industry disruptor.

It's now easier than ever for less technical users to access, manage and analyze data without needing help from IT. But, self-service data management isn't always straightforward, and there are plenty of pitfalls, like data quality issues, skills gaps and governance concerns. This session will cover practical ways to make self-service data management work.

Join this roundtable of trailblazers and thought leaders for peer discussions and lively exchanges on advancing women in IT leadership today. Women attendees are invited to explore visible opportunities and challenges, and to uncover and share best practices for successfully navigating the path to IT leadership.