Governance is difficult for an organization of any size, and many struggle to execute on data management in an efficient manner. At Assurance, the team has utilized Starburst Galaxy to embed ownership within the data mesh framework, completely transforming the way organizations handle data. By granting data owners complete control and visibility over their data, Assurance enables a more nuanced and effective approach to data management. This approach not only fosters a sense of responsibility but also ensures that data is relevant, up-to-date, and aligned with the evolving needs of the organization. In this presentation, Shen Weng and Mitchell Polsons will discuss the strategic implementation of compute ownership in Starburst Galaxy, showing how it empowers teams to identify and resolve issues quickly, significantly improving the uptime of key computing operations. This approach is vital for achieving operational excellence, characterized by enhanced efficiency, reliability, and quality. Additionally, the new data setup has enabled the Assurance team to simplify data transformation processes using dbt and to improve data quality monitoring with Monte Carlo, further streamlining and strengthening our data management practices.

Step into the dynamic world of data governance, business operations, and artificial intelligence (AI), where the unsung hero—metadata—takes center stage. Just like the perfect sandwich relies on clear definitions of its ingredients, this talk unveils the indispensable role of metadata in defining and organizing data.

George will share captivating real-life stories and examples on how clarity in definitions and metadata not only streamlines operations but also empowers decision-makers with invaluable insights.

Explore the backbone of AI advancement through essential data management tools: the Business Glossary, Data Dictionary, Data Catalog, and Machine Learning Metadata Store. Let's embark on a journey where unified interpretations pave the way for accuracy, efficiency, and success in the data-driven era.

We're in the a Cambrian Explosion of data architectures. In the last two years, dozens of vendors have each championed their own version of ‘the modern data architecture solution’, all claiming to be the future of IT in a data-driven enterprise. The sheer volume of architectures is daunting: Streaming data platforms, data lakes, structured/semi-structured/unstructured data, cloud data warehouses supporting external tables and federated query processing, lakehouses, data fabric, and layers of federated query platforms that offer virtual views of data. All claim to support the building of data products.

No surprise that customers are confused as to which option to choose. 

However, key changes have emerged including much broader support for open table formats such as Apache Iceberg, Apache Hudi and Delta Lake in many other vendor data platforms. In addition, we have seen significant new milestones in extending the ISO SQL Standard to support new kinds of analytics in general purpose SQL. Also, AI has also advanced to work across any type of data. 

What does this all mean for data management? What is the impact of this on analytical data platforms and what does it mean for customers? What opportunities does this evolution open up for tools vendors whose data foundation is reliant on other vendor database management systems and data platforms? This session looks at this evolution, helping vendors and IT professionals alike realise the potential of what’s now possible and how they can exploit it for competitive advantage.

This panel delves into the intricacies of building a cohesive and effective data ecosystem that drives organizational growth and innovation. In this session, our panel of experts will explore the foundational strategies for integrating diverse data sources, tools, and technologies to create a unified system that not only supports but enhances decision-making processes. Attendees will gain insights into best practices for data management, the importance of data governance, and how to leverage data for maximum impact. Discover how to navigate the challenges of data integration and use it to foster a culture of data-driven excellence within your organization.

Join us for an enlightening discussion on transforming your data ecosystem into a powerful engine for strategic advantage.

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.

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.

Join us for an insightful session on the evolving landscape of Data Quality and Observability practices, transitioning from manual to augmented approaches driven by semantics and GenAI. Discover the framework enabling organisations to build the architecture for conversational data quality, leaving behind the limitations of traditional, resource-heavy methods and legacy technology. Learn why context is paramount in data quality and observability, and leave with actionable insights to propel your organisation into the future of data management.

When data is the most valuable asset of your company, protecting it is a non-negotiable. While Information Security professionals are focused on Bad Actors, we have data operations and data governance professionals focused on Bad Data… Are they one and the same? What’s similar and what’s different between the worlds of data integrity and data security?

Drawing from a wealth of experience and real-world challenges, Gorkem will shed light on the pivotal role of data quality in the forefront of information security. We’ll discuss opportunities for early detection, auto-detection, and the establishment of tiered rules to manage and remediate bad data effectively. Learn how proactive governance and observability can transform data management from a reactive stance to a formidable defense mechanism, ensuring the integrity and security of your data ecosystem. 

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 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, 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.

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!

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.