The industry has witnessed some tectonic changes over the last few years: on prem to cloud to multi-cloud, BI to AI to GenAI, and data warehouses to data lakes to data lakehouses, to name a few. This constant evolution coupled with the ever-increasing demands of the business makes platform thinking crucial in order to ensure a future-proof infrastructure. As companies race to advance their AI strategies, Dell has seen a gravitational pull towards a modern data architecture that can create high quality data to feed AI and generate high quality outcomes. Join this session to learn about how the Dell Data Lakehouse, powered by Starburst, is the modern paradigm for this new era. You’ll learn about the investments Dell is making in data, analytics, and AI, why Dell and Starburst partnered up on this solution, and how it enables a tremendously powerful yet open and flexible data architecture.

Synthetic identity fraud has been a constant challenge for the financial industry. By creating believable, fake identities, criminals can defraud banks, launder money, and hide ill-gotten gains. 87% of organizations have extended credit to synthetic customers, including 54% who applied for it, and 53% to whom the institution itself offered the credit in the first place. Online banking and digital transactions make it easy to hide automation.

But AI is poised to take this fakery to an entirely new level. Automation lets fraudsters open accounts, make deposits, and perform believable, human-like transactions that make them hard to identify. Combined with generative capabilities that make perfectly deceptive words and imagery, we're on the verge of an identity arms race.

In this session, Deduce's Ari Jacoby will explore how AI is transforming finance, making it harder than ever for financial institutions to discern fake customers from genuine ones. He'll discuss what this post-truth era means for society as a whole, and offer tips and best practices for businesses to counter the impact of AI-equipped bad actors.

For at least a decade, we have established best practices for handling data and making sure it is delivered when needed and is correct. But with the explosion of LLMs and Transformer models, not all of those best practices apply anymore. In this talk, you will learn which best practices still make sense in this new Machine Learning world, and what new strategies you need to adopt to make sure that your LLMs can train properly and return inference in a reliable, timely and *accurate* manner.

In today's complex data & AI ecosystem, organizations prioritize crafting a data strategy designed to democratize access to data, to power their analytics engines. But how much of the data is truly usable? How do you rectify data gaps to ensure AI analytics and insights are grounded in reality without bias?

Referencing a collaboration between Women in Data® and the National Health Service (NHS) in the UK, in this session you will learn how a robust data strategy is critical to effect change and improve patient outcomes in the uniquely sensitive area of Healthcare.

In this talk, we'll navigate the complex terrain of AI data origins—text, audio, images, and beyond—highlighting challenges in attribution and copyright that echo the music industry's evolution from Napster to Spotify. Drawing parallels with how early digital platforms changed the landscape of music consumption and artist compensation, we'll explore potential pathways for establishing fair compensation models in AI. This talk aims to uncover how creating legitimate frameworks for compensating rights holders can foster an ecosystem where innovation exists alongside the rights and recognition of artists.  

Generative AI has triggered a new era of innovation and collaboration: the Age of Artificial Generative Innovation. AI systems now turn all types of content, from written materials to scientific data, into Copilots that help creators and innovators. These systems use specialized knowledge to break through old limits, sparking fresh ideas and partnering directly with engineers, scientists, and creators. From creating new drugs to inventing new materials, they speed up the innovation cycle. Systems of AGI fundamentally transform how we interact with and communicate knowledge, and how we apply this knowledge. In this talk I will illustrate how AI streamlines and accelerates innovation, reshaping how we work together with machines to invent the future.

At Airbnb Payments, integrating AI with blockchain can transform our services by enhancing transaction efficiency, security, and personalization. AI analyzes blockchain data, identifying potential financial anomalies and reconciling transactions immediately. 

It also optimizes blockchain operations, increasing transaction speed and reducing AWS usage.

Perhaps most importantly, AI also supports predictive analysis in for our users, creating more personalized experiences using blockchain's secure data. AI's analysis of individual preferences on blockchain enables personalized payments advice, such as how to price a listing or what payment methods to utilize. 

Overall, this integration leads to more efficient, secure, and tailored financial services, revolutionizing the industry with data-driven decision-making.

Data quality is the most important attribute of a successful data platform that can accelerate data adoption and empower any organization with data-driven decisions. However, traditional profiling-based data quality and counts-based data quality and business rules-based data quality are outdated and not practical at the scale of petabyte-scaled data platforms where billions of rows get processed every day. In this talk, Sandhya Devineni and Rajesh Gundugollu will present a framework for using machine learning to detect data quality at scale in data products. The two data leaders at Asurion will highlight the lessons learned over years of crafting the advanced state of data quality using machine learning at scale, as well as discuss the pain points and blind spots of traditional data quality processes. After sharing lessons learned, the pair will dive into their implemented framework which can be utilized to improve the accuracy and reliability of data-driven decisions by identifying bad quality data records and revolutionizing how organiations approach data-driven decision making.

2024 is the year of the AI agent. But what are AI agents and how are they different from traditional chatbots we all know? In this talk, we'll dive into how AI agents work and what makes them different from legacy chatbots. Listeners will leave with a good understanding of AI agent architecture and their newly unlocked capabilities.

In this executive debate, leading industry analyst Mike Ferguson welcomes leaders from premier software companies to discuss key topics in data management and analytics. Panelists will debate the impact of Generative AI, the implications of key industry trends, how best to deal with real-world customer challenges, how to build a modern data and analytics (D&A) architecture, data and AI governance and sharing, and on-the-horizon issues that companies should be planning for today.

Attendees will learn best practices for data and analytics implementation in a modern data-driven enterprise from seasoned executives and experienced analysts in a packed, unscripted, candid discussion.

Innovation is much more about people than algorithms and systems. Our panel will explore the criticality of cultural transformation in driving successful technology, data, and AI initiatives.

From fostering curiosity and experimentation to addressing resistance and promoting inclusivity, the session navigates the interplay of cultural dynamics and implementation success.

Our panel will share insights into strategies, challenges, and best practices for cultivating a culture that embraces change, empowers individuals, and drives organizations to reap the benefits that continued technology transformation can deliver.

The majority of AI risk discussion has been about how to safeguard privacy and address algorithmic bias. Less discussed – but equally important – is how to manage AI cybersecurity risk in today’s regulatory environment, where technology far outpaces regulation.

At present, laws and regulations provide for only general security requirements (i.e., they don’t necessarily account for the unique cybersecurity risks posed by AI). Given the slow pace at which new laws and regulations are adopted, it is likely that regulators will stretch existing requirements to cover all things algorithmic. 

Companies, therefore, need to be ready to defend their AI-related security practices now. They can start doing that by adopting an AI risk management approach that encompasses not only physical and cyber security measures but also the procedural and personnel aspects of such measures.

In this session, Proskauer's Michelle Ovanesian and Marc Palmer offer a view from the frontlines of data security, privacy, and the law in the rapidly expanding field of artificial intelligence.

Many organizations strive toward data-driven, yet most of them struggle to get relevant insights to the right stakeholders in a timely manner, resulting in a reactive rather than proactive approach. Given a world of fast-paced data intake and customer expectations of real-time, drawing insights and making decisions faster are critical paths to building timely and contextual relevance with customers. Join Murli Buluswar, Head of Analytics - US Personal Bank at Citi, to learn how delivering preemptive solutions though leveraging next generation technologies increases operational efficiency. Murli will discuss how to use conversational Generative AI to increase democratization of intelligence and reduce the friction between data, insight, decision, and outcome.

Join us for a dynamic discussion featuring a Chief Data Officer (CDO), Vice President of Data Science, and Technical Industry Lead as they delve into the real-world challenges of implementing AI and machine learning within Fortune 500 companies and large-scale government entities. This panel will shed light on the common pitfalls, the harsh realities faced, and practical strategies for overcoming these obstacles. Don't miss this opportunity to gain valuable insights from industry experts on navigating the complex landscape of AI/ML application in high-stakes environments.

This presentation covers the critical transition from traditional analytics to the strategic integration of AI into business processes, from the compelling reasons for adoption to the practical implementation steps. The 'What' segment introduces the foundational elements enabling the AI revolution and the business processes ripe for disruption. Addressing the 'Why', we highlight the responsible adoption of AI with accurate, reliable data as the backbone of any AI-driven initiative. This section underscores the need for solid data benchmarks and testing to measure AI's effectiveness, to ensure that AI implementations lead to tangible, positive outcomes, and to alert leaders to issues early. The 'How' section provides actionable insights on implementing AI to scale data-driven decisions effectively. With real-life examples, this section covers best practices for data management, technology integration, and strategies for fostering a culture that embraces data and AI. Attendees will learn about scaling AI-driven analytics, including considerations for data security, privacy, and ethical AI use.

As Generative AI becomes increasingly relevant in our everyday lives, many businesses are trying to figure out how this technology can be used to leverage their data. But while its power can be applied to any organization with a large amount of customer and industry data, the application of this new technology has been highly uneven across sectors.

In this session, Mr. Chi will explore how GenAI can be used to benefit society’s unsung heroes, including teachers, students, and authors. In a panel discussion with experts in publishing and education, he will examine what these sectors can do to thrive in a world dictated by data, and how to mitigate any unwarranted pitfalls.

The next biggest fight in the industry is about building trustworthy AI models that put human interests, lives, and prosperity first. Open source has a long history of accelerating innovation. Given the importance of human feedback and input, it is more critical than ever to level the playing field and ensure accessible pathways for public participation, feedback, and input into open-source AI projects.

Elena Yunusov is the Founder of the Human Feedback Foundation, a nonprofit on a mission to build a safer and more transparent future for AI. In this session she will discuss the path toward ethical AI. Participants will learn about the diversity of human feedback, including ethical, geographical, value-based and cultural aspects. The session will discuss ways to build safer, more accurate open-source AI models for critical applications in deliberative democracy, AI governance, healthcare, and other critical fields.

AI has the power to help your organization disrupt, innovate, generate faster insights, cut costs and increase productivity. But responsible and successful AI use demands high-quality, trusted data and transparent, observed and accessible data intelligence. See firsthand how taking a model to marketplace approach to managing and leveraging your organization’s data can help you gain the footing needed to get the AI results you are desire

Generative AI is a huge opportunity, but the rubber meets the road when going from ideation to testing and deploying. Assurance has leveraged the existing Data Mesh approach to make the deployment of Generative AI solutions both scalable and safe. This allowed the team to focus on this new technology solving Assurance’s business needs while relying on tried and tested data principles. Killian Farrell, Principal Data Scientist at Assurance, will discuss testing and deployment strategies as well as the integration with an existing data lake. When turning a hype into valuable data products, it is clear that a good foundation in data excellence and testing flexibility is key to achieving success.

The core of journalism was trust. For centuries, the cost of a printing press or broadcast license limited who could create news, but the rise of the Internet has turned everyone into a publisher. From fake news to hallucinations, modern journalism faces an existential threat, not only from a flood of unreliable sources but also from motivated actors bent on applying the latest technologies to inflame political polarization.

Is there a way to save the 4th Estate on which democratic society relies? In this talk, DM Radio's Eric Kavanagh suggests that the foundation of every story must be data with a proven, verifiable lineage. The carefully curated fusion of real-world transactional data and generative AI will radically transform the news industry, triggering a much-needed Renaissance in world-class journalism—but only if we use embeddings and open-source code to ensure accountability and transparency.

The concept of time and money spent on proof of concept (POC) has dramatically changed with generative AI. Since POCs can now be conducted immediately and at a low cost due to generative AI, businessmen and companies wanting to utilize generative AI in their operations should get started right away.

Join us as we go from zero to insights in 15 minutes. Alex will build an entire analytical report, from SQL query to python to data visualization. We’ll cover the basics of a modern data notebook, some of the technical AI Magic behind the scenes, and show how hundreds of customers accelerate time to insight with Hex.

In this fireside chat, Mike Ferguson—Europe’s leading industry analyst in data management and analytics—talks to Rob Thomas, Senior Vice President and Chief Commercial Officer at IBM on what IBM is doing to help companies get maximum business value from data and AI.

The discussion will explore what IBM sees as the key things needed to become successful with AI. This includes talking about embracing hybrid cloud computing to support data and deploy AI anywhere; dealing with the challenge of distributed data estate; and exploring integrated data and AI technology stacks and AI assistants that help companies tear down data silos, share data, and quickly build and integrate AI, augmentation, and automation into every part of their business. Finally, it will also explore how IBM is helping accomplish this while maintaining end-to-end data and AI governance.

The Data Engineer's role amidst the rise of big data, cloud computing, and AI-driven analytics has shifted. This panel chat explores the ever-changing landscape of essential skills and the automation of outdated ones. With a myriad of architectural options available, we'll dissect how organizations navigate the complexities to tailor solutions to their specific needs. Let's unravel the intricacies of building scalable data systems, pinpointing common breakpoints and strategies for efficient scaling. Come along as we delve into in constructing the foundation of the data-driven future.

The use of AI has grown exponentially and reports projecting that AI could add $15 trillion to the global economy by 2030. This speed and innovative ways corporations, governments, consumers, and students are using AI is outpacing the ability to establish regulations to safeguard its use. This panel will explore how ethics can, and should, frame the use of AI by different parties. The discussion will span topics such as election concerns, consumer protections, and job loss/creation. The panel will also consider “AI for good” and ethical issues around not using AI if it can benefit humanity.