This presentation explores the challenges and evolution in data and technology, emphasizing the growth of data sources, user concurrency, and distributed ownership. It delves into the strategic significance of a unified analytics tier to enable a Phase 2 of virtualization. Key components like an Entitlement Service, Data Catalog(s), MALT, and Data Contracts are discussed and what has been learned forcing those technologies and patterns to also evolve. This presentation underscores the importance of aligning with business outcomes, tracking the influence on Total Cost of Ownership (TCO), and establishing or improving a governance strategy as the organization matures.

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.

This session will explore why culture can make or break business transformations. People, process and technology equally shape the culture of an organization, and in turn impact how value propositions are framed and operationalized.

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.

It’s a tale as old as time: a data migration that was supposed to take months turns into years turns into something that no longer has an end date—all while going over budget and increasing in complexity every day. In this session, Gleb is going deep on the methods, tooling, and hard lessons learned during a years-long migration at Lyft. Specifically, he'll share how you can leverage data quality testing methodologies like cross-database diffing to accelerate a data migration without sacrificing data quality. You should walk away with practices that will allow your data team to plan, move, and audit database objects with speed and confidence during a migration.

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.

In today's data-driven world, organizations face the challenge of not only harnessing the power of data but also ensuring its responsible and effective use. This panel discussion will delve into the critical components of embedding data governance and data literacy into the fabric of organizational culture. Data governance forms the foundation of a robust data strategy, encompassing policies, processes, and frameworks to ensure data quality, integrity, and security. However, effective governance requires more than just frameworks; it necessitates a cultural shift where data stewardship is ingrained into every aspect of organizational operations. Moreover, data literacy is paramount in enabling individuals across an organization to effectively interpret, analyze, and derive insights from data. By cultivating a culture of data literacy, organizations empower employees to make informed decisions, driving innovation and growth. This panel will explore strategies for fostering a culture of accountability, collaboration, and trust around data practices driving sustainable success in today's dynamic business landscape.

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.

A Chief Privacy Officer, a Computer Scientist, and a Lawyer walk into a bar … and the bartender is a Chief Data Officer. 

Artificial intelligence is rapidly transforming our world, and with it comes the risk of unintended consequences. AI can infringe on privacy, create and perpetuate bias and discrimination, negatively impact economic opportunities, and harm customers. AI can spread disinformation and perpetuate deepfakes, affecting democratic processes and participation. Companies and institutions face reputational, cultural, economic, legal, and regulatory risks if they don’t identify, address, and govern AI’s potential harm to individuals and society throughout the AI lifecycle. 

This panel discussion among data experts will explore the importance of creating and maintaining AI systems that are human-centric, accountable, transparent, explainable, and privacy enhanced.

Slow query engines are forcing users to copy data from open data lakehouses into proprietary data warehouses to achieve their desired performance, but this results in a complex, costly ingestion pipeline that undermines data governance. In this talk, we will dive into the latest developments in data lakehouse querying, why you should avoid using proprietary data warehouses for accelerating queries, and how enterprises like Trip.com are unifying their SQL workloads directly on open data lakehouses.

Our efforts to obtain – and maintain – a little privacy in our lives have been a subject of concern and conversation in the United States since 1890. A famous article that year, titled “The Right to Privacy,” called out how new technology and business processes threatened what is also called “our right to be let alone.” The concerns and conversations have evolved since then to also encompass the right to privacy over how the data about our personal lives is collected and used. 

Today, collecting and using our personal data is ubiquitous. We are recorded by dashcams and surveillance cameras for safety and security. We use our biometric data to conveniently open phones and enter buildings. We provide intimate details of our lives so connected devices can help us stock refrigerators, brush our teeth, drive cars and more. This session will provide a quick overview of data privacy, with tips for governing the use of personal data in order to comply with laws and meet expectations. 

Most IT organizations face a constant balance between delivering approved projects (the top-of-mind, important tasks that management wants to launch) and fixing urgent problems (the ones that break systems in unexpected ways.) But there's a third bucket of issues—the long-languishing, forgotten, often boring tasks that turn into technical debt.

Take a step back from the Top Ten lists and join Saks' Veronika Durgin as she digs through the Bottom Ten: Neglected data engineering tasks that will come back to haunt you. This ""forgotten bucket"" can always be deferred, but the more you wait, the more time you'll spend on unplanned activities. And there are a variety of lenses with which you can look at it to better understand its impact on the organization, including the hidden costs of built-versus-buy, the need for a single definition of ""done"", identifying unexpected business dependencies; finding real data to conduct meaningful tests; and the environmental impact of your data.

The data mesh framework, first introduced in 2021, provides a more dexterous and valuable approach to data management by increasing accessibility for teams, partners, and other stakeholders. In this session, Annalect’s Chief Technology Officer, Anna Nicanorova, and Director of Data Engineering, Santhosh Swaminathan, will share how their organization — the data and analytics division of Omnicom Group — was able to simplify the implementation of data mesh and unlock numerous benefits — namely, the ability to facilitate seamless collaboration and drive greater operational efficiency. 

In this session, Jordan Morrow will explore the impact of integrating data and AI strategies with business goals, highlighting their importance in driving organizational success. He will emphasize the significance of imbuing data professionals with business acumen and the need for fostering data and AI literacy across the organization. Jordan will focus on strategies for data professionals that not only enhance their understanding business combined with the application of data and AI but also effectively generate real business value.

Large Language Models like the GPT, Gemini, Gemma and Llama series are rapidly transforming the world in general and the field of data science in particular. This talk introduces deep-learning transformer architectures including LLMs. Critically, it also demonstrates the breadth of capabilities state-of-the-art LLMs can deliver, including for dramatically revolutionizing the development of machine learning models and commercially successful AI products. This talk provides an overview of the full lifecycle of LLM development, from training to production deployment, with an emphasis on leveraging the open-source Python libraries like Hugging Face Transformers and PyTorch Lightning.

Companies need reliable information about the future to make decisions in the present. Strategic plans and budgets rely on good guesses about what the future holds. But typical approaches to planning are often rarefied and insulated from the real world. There's an approach developed in Switzerland and Southern California that holds real promise: Gamification.

Games are inherently competitive—which means the player with the optimal strategy prevails. That optimization can be applied to forecasting and prediction, yielding significantly better results. David Savlowitz offers a peek into gamified forecasting, a convergence of competitive gaming, investment strategy, data science, and human psychology that enables accurate forecasts.

In this session, David will explain how competitive approaches can involve a wider group that includes economists, data scientists, and non-technical staff; and how tournament models where players are rewarded for accurate predictions that maximize return on investment and minimize forecasting error.

Operating data lakes over object storage poses challenges: testing ETL changes, staging pipelines, ensuring best practices, debugging, and tracking data usage for ML reproducibility. Enter lakeFS—an open-source data version control tool transforming object storage into Git-like repositories. Learn how lakeFS enables unified workflows for code and data, providing benefits like faster development and error recovery. Join us to explore lakeFS and harness the power of data as code for your team's success.

As the field of data analytics continues to progress and expand, the role of semantic layers in harnessing the power of AI is becoming increasingly crucial. The incorporation of context and constraint is essential to optimizing the potential of Language Model Models (LLMs), which requires a more structured and specialized approach. While traditional methods have made strides in providing some context through prompt engineering and knowledge graphs, semantic layers offer unparalleled clarity and efficiency in bridging the gap for LLMs. To further unfold the narrative on semantic layers and their transformative impact on AI-enabled analytics, we invite you to a thought-provoking session with Artyom Keydunov, Cube's CEO & Co-founder.

This session explores data fabric and data mesh architectures and the common need for a Data Center of Excellence, including standardized practices, resources, compliance policies and security standards. By establishing a DCoE, organizations can navigate the complexities of data compliance and drive business growth in today’s competitive landscape.

Explore T Baker Smith's journey showcasing the transformative impact of spatial data integration through internal successes and client solutions utilizing FME. This session illustrates spatial data's pivotal role in digital transformation, bolstering operational efficiency, and fostering competitiveness across industries. Explore how embracing this frequently underestimated asset can revolutionize decision-making and operational strategies, underscoring its indispensability for achieving holistic digital and operational advancement.