Join Jamie Underwood and Andy Hannah for a transformative deep dive into the Responsible Data Revolution. In this session, they'll explore the crucial intersection of innovation and advanced analytics, delving into the legal aspects surrounding it. With Jamie's expertise in navigating the intricacies of intellectual property and Andy's deep entrepreneurial and analytical experience, we'll uncover the ethical and legal considerations that arise in the era of big data and AI. 

From privacy concerns to intellectual property rights, they'll discuss the evolving landscape of data governance and responsible innovation. Gain insights into strategies for leveraging data ethically and responsibly while maximizing its potential for transformative innovation. This session promises to equip you with the knowledge and tools to navigate the complex terrain of the data revolution responsibly.

The Federal Government's approach to AI Policy, Governance, and Security is evolving to address this dynamic landscape of artificial intelligence and the explosion of Large Language Models. The goal is to develop a framework with the aim of reducing risk to people and the planet in the development, procurement and use of AI and the comprehensive strategies employed to formulate and implement policies ensuring responsible AI use. Emphasizing robust governance frameworks, organizations navigate ethical considerations, privacy concerns, and potential biases. Security protocols are paramount, safeguarding against cyber threats and ensuring the integrity of AI systems. Leveraging this multifaceted approach will help to foster innovation while prioritizing transparency, accountability, and the public interest. The Federal Government's commitment to shaping a secure and ethical AI landscape underscores its pivotal role in technological evolution.

In this session, author, entrepreneur, and Data Universe chair Alistair Croll takes a step back from the fringes of technology to look at the big picture of innovation. Building up from first principles, he'll examine the Civilization Stack on which the modern world operates, and what that means for emerging technologies and societal adoption.

In this session, analytics expert and renowned AI author David Boyle will take the audience on a fascinating journey into applied AI. We'll explore how language models like ChatGPT are revolutionizing analytics, sharing dozens of practical examples of how AI is transforming data interpretation, decision-making, and communication.

We'll explore using these tools in planning analytics to generate hypotheses and analytical strategies. David will demonstrate GPT-4's Code Interpreter executing complex data tasks like cleaning and analysis, allowing attendees to see firsthand see the iterative nature of working with AI: How it can write code, perform analysis, and refine outputs based on feedback.

Finally, we'll look at bringing insights to life. AI can turn complex findings into compelling stories for decision-makers. It can generate visualizations, interpret results, and translate technical terms into actionable business insights. This helps close the gap between data teams and leadership by serving as a mediator.

This session will be far more than a simple demonstration of AI's capabilities, but instead offer a roadmap for harnessing AI's power to revolutionize your work. Join David to consider the broader implications of AI in decision-making and how to integrate these tools into your workflows.

In this session, Chad Sanderson, CEO of Gable.ai and author of the upcoming O’Reilly book: "Data Contracts," tackles the necessity of modern data management in an age of hyper iteration, experimentation, and AI. He will explore why traditional data management practices fail and how the cloud has fundamentally changed data development. The talk will cover a modern application of data management best practices, including data change detection, data contracts, observability, and CI/CD tests, and outline the roles of data producers and consumers. Attendees will leave with a clear understanding of modern data management's components and how to leverage them for better data handling and decision-making.

GenAI can look deceptively easy when it comes to showing a cool demo, but can prove incredibly hard to productionalize. This session will cover the challenges behind industrializing GenAI applications in the enterprise, and the approaches engineers are taking to meet these challenges. Attendees will get to take a look under the hood to see how Data Engineering and Integration techniques can help us go from simple demos to production grade applications with consistently high quality results.  

We will explore how Retrieval Augmented Generation (RAG) workflows go from naive to advanced. Techniques discussed will cover a typical GenAI application flow with topics including multiple and hybrid models, refined data processing, data security, getting transparency in results, combining structured and unstructured data, and putting it all together to get high performance and cost effective outcomes. Attendees will leave the session with a framework to understand proposed solutions from their teams and ask the right questions to test if a solution can become industrial-grade.

Project Nessie is an open-source project that provides a Git-like approach to version control for data lakehouse tables. This makes it possible to track data changes over time and revert to previous versions if necessary.

In a lakehouse environment, catalog versioning is essential for ensuring the accuracy and reliability of data. By tracking changes to the catalog, you can ensure that everyone is working with the same data version. This can help to prevent errors and inconsistencies.

Project Nessie can be used to implement catalog versioning in a lakehouse environment. This can be done by creating a Nessie repository for the catalog and then tracking changes to the repository using Git.

This presentation will discuss the benefits of using Project Nessie for catalog versioning in a lakehouse environment. We will also discuss how to implement catalog versioning using Project Nessie.

There is a mad rush these days to embrace the power of AI inside every enterprise. But, through a decade of research, Jack Phillips' research has shown that a firm's data and analytics maturity hold the answer as to how ready a firm is to leverage AI. There is no "free lunch" when it comes to AI adoption -- the foundational elements of high quality data, a data-driven culture, and understanding the right business problems must all be in place

Mr. Phillips will walk through IIA's globally accepted methodology for measuring data and analytics maturity, and share industry and company benchmarks to allow you to estimate where your company stands today. Most importantly, Mr. Phillips will open the playbook around what the highest-performing analytical competitors are doing differently than the rest. 

We're moving into uncharted territories of emerging technologies, poised to revolutionize data science. In this panel discussion, we'll unravel the mysteries of the technological fringes and explore their imminent impact on mainstream adoption.

Our panel of experts will discuss a future where these technologies are ubiquitous, and how to prepare for the transformative shifts in daily life and industry. Let's peer beyond the horizon, contemplating the dawn of a new era in data science and beyond.

Tech moves fast; the law, not so much. How does a slowly-evolving legal industry govern ever-evolving technology? How do companies (and inventors) protect their AI-related data and intellectual property while not infringing the rights of others? Which type of IP is best suited for AI products? Copyrights? Patents? Trade secrets?

Join Jamie Underwood, a seasoned trial lawyer with over two decades of experience resolving IP issues in high-stakes cases, as she provides you with an overview of the benefits, drawbacks, and open questions to consider when making IP-related decisions about AI and the data supporting it.

In a classic cart before the horse scenario, many companies have jumped at leveraging Generative AI and other AI technologies. However, most of those same companies haven't completed the core work of building a reliable & secure foundation that provides data accessibility, analytics speed, and ensures data quality. The resulting risk for leaders is overinvestment in AI programs that may not have accurate & secure data access, further exposing the business to harm. It is a case of slowing down to speed up - ensure the foundation is solid before you build the house. In this talk by Starburst CEO Justin Borgman, and Head of Partner Solutions Architecture, Data & Analytics - AI/ML, Subodh Kumar from AWS, you'll learn about the essential data foundations for AI success. The foundation, the plumbing, and the framing that will set businesses up for AI success.

This is a wake-up call for the data management profession. People are at the heart of everything we do in data management; there are real people behind every dataset and algorithm. We know that poorly managed, low-quality data hurts the bottom line, but it can also turn a regular day into a nightmare for employees, not to mention the far-reaching impacts on our customers and society. In this session, Tony Mazzarella will share his experience and provide actionable insights to help data leaders reimagine data strategy and governance and pivot towards a more inclusive and people-centered approach where data works for people, not the other way around!

Join Netflix' Kishore Banala for a deep-dive into strategies for building custom analytics solutions. From data collection approaches, to the nuances of real-time vs batch data movement, to leveraging columnar data stores tailored for analytical use cases, Kishore will provide practical examples and case studies such as identifying unique site visitors. We'll conclude with a look at the art of visualizing data with open-source tools that empower you to create compelling visualizations. Come along for insights and suggestions drawn from practical experience in building large scale analytics solutions.

Data is the linchpin of competitive advantage. This session takes you on a deep dive into the revolutionary best practices of enterprise AI data pipelines. We'll unpack the complexities of data integration from multiple silos, orchestrating data to GPUs, and building fast and efficient infrastructure for AI. We'll explore innovative strategies for data governance, real-time processing, and seamless orchestration across disparate systems.

Learn how industry leaders are leveraging these practices to drive decision-making, enhance customer experiences, and catalyze digital transformation. Join us to empower your enterprise with the architecture that will lead tomorrow's Enterprise AI landscape, and walk away with a practical blueprint for constructing resilient, scalable, and efficient Enterprise AI architectures.

The opposite of progress is congress, as the saying goes. There's widespread belief that increased governance slows down innovation and makes teams less productive. But is this true? LexisNexis' Emili Budell-Rhodes offers an alternate perspective: By partnering between technology teams and legal staff at the outset, everyone benefits.

The audience will learn how to create robust feedback loops and a joined-up lifecycle for innovation, delivering new features and products that are compliant from the outset.

If everything goes the way that the experts say it will, Generative AI will eventually be a core part of nearly every piece of technology we use. But before that happens, some organizations will likely make their current products worse, as they try to integrate GenAI functionality in their existing services. Join Benn Stancil, Field CTO at ThoughtSpot, to strategize which Generative AI dreams are worth chasing, what foundations you need to build and leverage AI, and how to take advantage of these AI developments while avoiding major disasters. 

This session explores the technological advancements of analytics, data science, machine learning, AI, and quantum computing. Beginning with an overview of the historical development and current state of these technologies. LexisNexis' Flavio Villanustre will explore how analytics has transformed industries; how data science extracts valuable insights, how machine learning has moved from simple rule-based systems to today's deep learning models; and how quantum computing promises to solve previously intractable problems at speeds unattainable by classical computers.

Attendees will learn about the benefits and associated risks of each technology: Increases in efficiency, productivity, and innovation juxtaposed against vulnerabilities in privacy, security, bias, and wider societal concerns such as unemployment. These competing issues underscore the need for responsible, ethical research and deployment of such tools.

The need for an executive responsible for an organization’s information assets today may seem obvious. But some organizations still struggle with making a business case for the role. And even existing chief data officers can be confounded about how to formally justify their existence. This session will share eye-popping findings and analyses from Mr. Laney’s study of hundreds of organizations with and without a CDO. 

As any good scientist knows, and any good data scientist should know, most discoveries begin with a hypothesis. We see a lot of surveys about the CDO role but don’t really have much of a point to make or look at the impact a CDO makes. This study examined over 500 organizations to determine how businesses with a CDO operate differently.

 Drawing from the study's conclusions, attendees will learn about the benefits of a CDO, and how having one affects data quality, governance, data democratization and monetization. We'll explore whether having a CDO affects an organization's ability to value its data and how investors perceive it, and look at the career path of CDOs to better understand what makes an actual C-level CDO.

This session will explore the transformative impact of Generative AI on data strategy. It will highlight how GenAI, based on a lakehouse platform, empowers organizations through people, process, and platform. The talk will also delve into how by grounding your strategy with governance in mind you can increase innovation, competitiveness, and productivity, by enabling data-driven decision-making.

Join data nerd and business strategist David Boyle to hear how and where artificial intelligence is driving business data-driven transformation.

Combining more than two decades of experience pioneering data-driven change at global brands like EMI Music, HarperCollins, BBC, MasterClass and Harrods with a crazy 18 months of experience as a leading author and consultant on the role of language models in many of the world’s leading companies, David will share his perspective on the role of AI relative to other forms of data / insight / analytics in business transformation.

Expect fascinating case studies highlighting how AI has redefined business strategies and unlocked new growth. Discover strategies for capitalizing on AI to overcome industry challenges, open new market opportunities, and transform culture and commerce. And hear about a failure or two along the way. 

You'll leave with a new outlook on AI's potential, ready to embrace AI as the driving force for your data strategy.

Increasingly, IT and business executives talk about information as one of their most important assets or "the new oil." But few behave as if it is. Executives report to the board on the health of their workforce, their financials, their customers, and their partnerships, but rarely the health of their information assets. And corporations typically exhibit greater discipline in managing and accounting for their office furniture than their data.

In this session, Mr. Laney will share insights from his best-selling book, Infonomics, about how organizations can actually treat information as an actual enterprise asset. He will discuss why information both is and isn’t an asset and property, and what this means to organizations themselves and the investment community. And he will cover the issues of information ownership, rights, and privileges, along with alternative data challenges and opportunities, and his set of generally accepted information principles culled from other asset management disciplines. 

This session will be beneficial for those looking to help their organization move beyond the trite “data is an asset” or “data is the new oil” lip-service to begin acting that way. You'll learn how to monetize information assets in a wide variety of ways, including a number of real-world examples; how to manage information as an actual asset by applying asset management principles and practices from other asset domains; how to measure information’s potential and realized value to help budget for and prove data management benefits; and how classic microeconomic concepts can be applied to information for improved data architecture & management, and economic benefits.

AI has been with us for years in everyday technologies such as mapping, facial recognition, and cancer detection. But ChatGPT and its cousins made AI "real" to the masses by letting us interact with the AI directly, rather than having it work its magic behind the scenes of an app. Where might this strong AI tailwind take us? Will it push us to AI sentience or will we, as AI’s engineers, be hoisted with our own petard—or will it be a little of both? In this keynote, startup and AI pioneer Jana Eggers will share her observations and advice from delivering large-scale AI-enabled systems from more than 30 years across research, supply chain optimization, search engines, travel, and generalized decision support.

We are in a Great Acceleration—a singularity, not in the capital-S-Kurzwellian sense of robots rising up, but in the one Foucault described: A period of time where change is so widespread, and so fundamental, that one cannot properly discern what the other side of that change will be like.

In this talk, Data Universe chair Alistair Croll charts the course from traditional software to generative AI—software that makes stuff up. He'll discuss its rapid adoption, and some unintended and surprising consequences everyone should understand, exploring inexorable swings between centralized and decentralized architectures.