Join this unique interactive session blending the analog art of puppetry with digital data storytelling. Through video clips, behind-the-scenes anecdotes, special guests, and the world premiere of new content, follow the CDO (Chief Dog Officer) and the IT Bee as they battle legacy opposition, CAT-sultants, and ANT-alysts in their quest for a data-driven utopia. This entertaining and insightful presentation offers humor, creativity, and actionable takeaways to transform the way you think about data management and inspire you to tell better data stories at your organization.

Top Takeaways:

• Analog Meets Digital: Discover how puppetry can convey complex digital concepts in a relatable and engaging way.

• Entertaining and Educational: data narratives come to life through a unique storytelling approach.

• Real-World Reflections: puppet adventures reveal the challenges of modern data management.

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.

The next big innovation in data management after separation of compute and storage is the open table formats. These formats have truly commoditized storage, allowing you to store data anywhere and run multiple compute workloads without vendor lock-in. This innovation addresses the biggest challenges of cloud data warehousing — performance, usability, and high costs—ushering in the era of the data lakehouse architecture.

In this session, discover how an AI-powered data lakehouse:

• Unlocks data for modern AI use cases

• Enhances performance and enables real-time analytics

• Reduces total cost of ownership (TCO) by up to 75%

• Delivers increased interoperability across the entire data landscape

Join us to explore how the integration of AI with the lakehouse architecture can transform your approach to data management and analytics.

In the past, a central data team handled data management. However, challenges arose with the rise of the modern data stack, leading to the demand for Data Mesh and data product management. Today, more organizations are attempting to enable self-service data management, but there’s no clear solution. This presentation will show how an analytical franchise model can help you manage data yourself with your current stack. It’ll also talk about what’s been done and how AI can make data management better in the future.

It’s a widely held belief that MDM programs are big, disruptive, risky, and prone to failure. While these things may have been true for some companies in the past, providing meaningful business value through the launch (or relaunch) of an MDM program can be done in under 90 days – if you take the right approach. Come listen to former Gartner MDM analyst Malcolm Hawker as he describes the keys to launching an MDM program in under 12 weeks:

- Taking an MVP (minimum viable product) approach to your MDM program

- The importance of choosing the right MDM implementation style 

- How to gain executive alignment and sponsorship 

- Staffing / resourcing an MDM program for speed

- Other practical lessons from the MDM school of hard knocks 

If you’re having trouble getting an MDM program off the ground, or if your existing program is failing to deliver business value, then you won’t want to miss this presentation from the leading expert in the field of Master Data Management. 

In the journey "From Data Mess to Data Mesh," an internal data marketplace is essential for transforming disorganized data into a cohesive, discoverable, and accessible resource. By centralizing data assets, it ensures seamless data discoverability and findability. Moreover, it upholds robust data governance and orchestration, maintaining compliance and quality. Join me to explore how an internal data marketplace can streamline data management, foster a data-driven culture, and drive organizational efficiency.

Main covered points:

• What is an Internal Data Marketplace? 

• Why is it Different from Existing Vendor-Based Marketplaces? 

• Real example of a Data Marketplace 

• Steps to Build a Data Marketplace 

• The main Architecture behind building your own Data Marketplace

Everyone wants to take advantage of AI but to truly do so, the data must be made ready for use. Every data team has been asked to make their data ready for use by AI. But what does that actually look like in practice? How do you know if you're there? And how do you get there if you're not? This session will explore how AI is changing data management, share best practices when using AI for data management, and provide a glimpse into the future of how data consumption might look in 5 years.

As a follow-up from my previous talk 'Rethinking MDM with Data Mesh', in this talk we will explore how to tackle complexity in Data using DDD principles. I will discuss the technical foundations required to support managing data in a federated world, where we acknowledge that data exists as part of a large ecosystem and having it in different formats, solving different problems, is desirable.

In this session, we'll explore how to translate all the latest data management trends: actionable data governance, generative AI, semantic ontologies, knowledge graphs, metadata management, into the business language and demonstrate the value of data initiatives quicker. For business users it's not just about keeping up with the latest tech trends. It's about the fundamental goals of lowering costs, increasing profitability and reducing time to value.

Travel Management Companies (TMCs) are specialist travel providers that manage the business travel requirements of corporations of all sizes. They book and manage air travel, hotel stays, car hire, rail and other trip components for companies with varying budgets, traveller populations, policy requirements and service expectations, and deliver value in being able to control travel spend through policy management (e.g. class of travel), profile management (e.g. individual preferences), traveller tracking and delivering centralised booking and invoicing solutions.  

A standard, linear model of data provision exists ubiquitously in the corporate travel industry whereby TMCs provide reporting to analyse and consolidate spend and adjust policy. Data is provided in standard formats, with little focus on specific customer questions and travel policy management is retrospective and antiquated. 

In this presentation, delegates will see how Take2Eton is re-engineering our data landscape. From what started as project to improve our reporting and analytics platform by centralising data using a low code, highly governed, adaptable platform, we have now built a booking app, policy engine, data management app and data consolidation tool from ground up, improving customer interaction with our data at all stages of the lifecycle.

In this flagship Big Data LDN keynote debate, conference chair and leading industry analyst Mike Ferguson welcomes executives from leading software vendors to discuss key topics in data management and analytics. Panellists 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, how to manage, produce, share and govern data and AI, and issues on-the-horizon 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 an experienced industry analyst in a packed, unscripted, candid discussion.

Data practitioners are feeling pressure around the realities and real-life considerations of building out a data stack that can handle the next generation of data problems in addition to today's data challenges. Considerations like minimizing complexity and cost while focusing on scalability and performance are at the forefront of the data world right now, and how this works in a world where LLMs and deep learning are becoming table stakes is paramount. There are questions about data management at this scale, as well as how to fold in legacy infrastructure and architectures. We'll discuss the modern AI data stack in this talk, delving into the realities of building the data ecosystem of the future.

Our panel discussion explores the transformative potential of big data and AI across diverse industries. We will address key technical challenges such as data management and model scalability, along with ethical and privacy concerns, including data utilization and algorithmic biases. Looking ahead, we will discuss future trends and emerging AI breakthroughs. Emphasizing interdisciplinary collaboration, we advocate for diverse teams to ensure fairness and innovation in AI solutions. This dialogue aims to illuminate the complexities and opportunities in big data and AI.

The massive interest in AI solutions has sparked a huge and pervasive wave of AI projects. We are now entering a second phase where the AI projects that have proven value are looking for operational landing places in enterprise environments. This is visible through the big hype for AI data systems like vector databases and feature stores. This phase of AI operationalizing is the hour of databases, which have proven already to be the battle-proof bedrock for data management enterprise environments. 

Postgres is naturally a front runner in this space. AI workloads are entirely tied to data, they start with data, they run on data and they produce data. Join this talk for a walkthrough on popular AI application flows, their strong ties to data and Postgres' strong operational qualities and demonstrate how they form the perfect environment for mission critical AI solutions in an enterprise.

As data continues to grow in complexity, the need for a unified data layer with rich semantic business meaning has become more critical than ever. This session examines the transformative impact of integrating generative AI with a well-structured, unified data layer, emphasizing how this combination unlocks new levels of intelligence and efficiency. By standardizing and contextualizing data across the organization, companies can fully leverage the power of generative AI to drive insights, automation, and decision-making. Explore practical strategies and case studies that highlight how a unified data layer is the key to harnessing generative AI, marking the moment when data management truly evolved. Don’t miss this opportunity to learn how to prepare your data infrastructure for the future.

Struggle to get the investment you need for business-critical data management projects? It can feel like you’re banging your head against a wall to get the business to appreciate its importance. But, as a profession, we need to better explain the foundational role it plays in delivering business outcomes. 

Join Helen Mannion, Chief Data Officer at Specsavers, Paula Rudkin, Partner & Head of Data Management at Knight Frank and Antony Marlow, Director of Data Management at Cynozure as together they aim to create the spark that ignites the fire for how we better engage, communicate and relate to our business colleagues on this topic. 

Expect to take away ideas, different approaches and be inspired to embrace a new way of getting data management the look-in your business needs to enable its success.

In the wake of the Corporate Sustainability Reporting Directive (CSRD), organisations are tasked with enhancing their ESG data management and reporting frameworks. This presentation will guide data leaders through the complexities of CSRD compliance, focusing on the pivotal role of effective ESG data governance. We will explore the three core challenges:

1. Double Materiality Assessments: Evaluating both financial and impact materiality to align with regulatory expectations.

2. Comprehensive ESG Data Collection: Sourcing data across the entire business ecosystem to ensure comprehensive reporting.

3. External Assurance: Ensuring third-party certification of ESG reports, highlighting the importance of data accuracy and governance.

Attendees will gain practical insights into implementing a logical data management approach that addresses these challenges, while also aligning ESG data practices with broader enterprise data strategies.

Are you struggling to gain leadership support, craving stakeholder engagement, and begging for proper funding? Even though you may create analytic Gen AI wonders with your data, it won’t matter unless you explain the value in practical business terms. Join The Data Whisperer’s rollicking and riotous review of current buzzwords and some practical tips to help you bridge the story gap between data and the business. n this session, you’ll learn:

• How to differentiate between a data management narrative and other data storytelling efforts 

• Strategies to secure executive sponsorship and ongoing funding 

• The 3Vs of Data Storytelling for effective Data Management

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.

The quality and usability of data determine the success of data-driven projects, and it has never been more critical to establish an operational pipeline of high-quality data that is both secure and accessible. Once distinct disciplines, Data Governance, Master Data Management, and Generative AI have converged to deliver data that is insight- and AI-ready in record time. Join our experts for practical examples and actionable advice you can use to get started.