Data governance can contribute local optimizations to a company's value chain, such as better data discovery via a data catalog, or quality-monitored and cleansed data sets. From a 30,000 ft data strategy view, it is even more desirable to connect the dots for business objects frequently reused among business processes and make them available as governed, quality-controlled, easily accessible data products. The speaker successfully launched a Data Governance program in a company traditionally ranking metal higher than data and will share experiences on the ongoing data product journey.

Elsevier is a leading provider of quality scientific data to the global research sector. We are all too aware that high-quality, well-structured data is the cornerstone of any data-driven product – particularly relevant as we are caught in the disruptive excitement of the Gen AI wave. We mustn’t lose sight of the role good data plays – garbage in garbage out is as applicable now as ever.

The generation and availability of high-quality data relies on good data governance and the adoption of FAIR (Findable, Accessible, Interoperable, Reusable) data principles, including ontologies. Our semantic technology stack and domain expertise helps drive this adoption. Structured data, such as ontology-tagged text and Knowledge Graphs can be the bedrock of explainable GenAI solutions such as we are seeing in the arena of scientific search.

The Good, the Bad and the Ugly Amy is a Senior Data Solutions and Integration Manager at Bay Wa r.e. Her responsibility was enabling Data Governance, Data Products and Data Mesh. The challenge was building a unified data decentralization framework for dozens of organizations that historically used different stacks, metrics, and processes. Data Mesh is a complex concept, and every organisation views it differently. Amy will share the framework she had implemented for which her team gained leadership buy-in. She will discuss what Amy?s team managed to execute, what they've achieved, and what's on their roadmap. She will also share her learnings from this exciting journey, including securing buy-in from different business units. At 'Journey Building Data Mesh: The Good, The Bad, and The Ugly,' Amynwill focus on: Why Data Mesh, and when it is the right time to start prioritizing it? How did they implement data contracts at the scale, and what is the current progress? What Amy?s team would do differently today on their journey to Data Mesh.

Join Experian, Sainsbury’s, The Nottingham, UST and British Business Bank discuss how better data quality and better data governance leads to improved AI. Hear real business examples of how AI is being implemented and the lessons our panellists wished they’d known sooner. Also learn key takeaways on how to have a better Data Governance strategy and why having trust in your data is more important than any new emerging technology.

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.

Join this session to discover how Natwest approaches governance, security & privacy controls, focusing on how they've designed & implemented a framework that balances control with agility. We will dive into NatWest's journey to establish a robust foundation for federated governance using a hub-and-spoke model. Attendees will gain insights into how NatWest enables a scalable governance structure that empowers individual teams while maintaining centralised oversight. Additionally, NatWest will share their roadmap for building a modern data architecture, guided by Data Mesh principles, that ensures flexibility, scalability, and alignment with the evolving needs of the organisation. This session is a must-attend for those looking to modernise their data strategies with a focus on governance and architectural innovation.

In today's rapidly evolving digital landscape, companies must adapt their approach to Data Governance to remain competitive. With the proliferation of data and the increasing reliance on advanced technologies like AI and machine learning, to remain effective Data Governance needs to evolve and adapt.

Join Nicola as she shares key learnings for her Data Governance journey and how we have to adapt our approach to Data Governance to work with the evolving environment we operate in.

AI is changing our work and personal lives, offering unprecedented opportunities in almost every arena. However, many organizations risk undermining their AI-driven projects by neglecting the need to unify, protect, and improve their data from the outset. Join this session to see first-hand examples of how feeding different data sets into a custom Large Language Model (LLM) can impact outcomes and learn how to build your foundation of high-quality, fully governed data today.

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

Enterprises who deploy data observability report fewer and shorter incidents due to data quality issues. However, deploying data observability widely within an enterprise can be daunting, especially for teams who have experienced a heavy lift when rolling out other data governance technologies. This talk will review the top challenges enterprises will face when pursuing a data observability initiative, and a mix of process and technology solutions that can mitigate them to speed up time to value so data governance teams can show business-facing results quickly.

From its founding in 2023, Skyscanner has leveraged analytical data to optimise business and traveler experiences. And with more than 110 million monthly users resulting in 30+ billion analytical data events per day, Skyscanner is an expert at managing data at scale.

Join Michael Ewins, Director of Engineering at Skyscanner, to learn how his team develops and executes data strategies centered on their core principles of data reliability, trust and rapid data-driven decision making. Michael will dive into the challenges his team faces navigating complex lineage, strategies for effectively combating data incidents, how they simplified their analytics infrastructure for a more practical approach to data governance, and their success in implementing impactful ML and AI business-critical use cases.

During his talk, Alex will discuss the value of moving from a batch-based architecture to a real-time, event-driven architecture with Apache Kafka®. He will then explain how you can build valuable data products with Confluent that unlock high-volume performance, data governance, and AI use cases. 

Navigate the complexities of today's digital and data landscape in our panel discussion that underscores the essential role of data governance in the era of accelerating mis- and dis-information. As Big Data ceases to be a buzzword and transforms into the lifeblood of decision-making, governance is elevated from a regulatory compliance requirement to a differentiator and a beacon of trust. This session goes beyond exploration of governance as a data hygiene factor and delves into the relationship between governance, value creation, and elicitation of trust—particularly for decisions steered by AI as we travel into the world of automated risk management and decision making.

As the hype for AI grows, organizations are still wrestling with the fundamentals of data governance. The ambitions of executives and boardrooms to implement next-gen AI use cases hinges on a solid data foundation including cataloging, ownership, and data quality. Join Collibra’s Chief Data Citizen, Stijn Christiaens and Vodafone’s Sr. Data Governance Manager, Fede Frumento, to learn how Vodafone has used data governance fundamentals to increase the scalability and collaboration of GenAI use cases.

As organizations are exploring and expanding on their AI capabilities, Chief Data Officers are now responsible for governing the data for responsible and trustworthy AI. This session will cover 5 key principles to ensure successful adoption and scaling of AI initiatives that align with their company?s business strategy. From data quality to advocating for ethical AI practices, the Chief Data Officer?s mandate has expanded to compliance of new AI regulations. Peggy Tsai, Chief Data Officer at BigID and adjunct faculty member at Carnegie Mellon University for the Chief Data Officer executive program, will provide insights into the AI governance strategies and outcomes crucial for cultivating an AI-first organization. Drawing on her extensive experience in data governance and AI, this session will be an invaluable guidance for all participants aiming to adopt industry-leading practices.

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.

All companies claim to be well Data-Governed (how could they claim anything else?), but when we dig deeper, we start finding many gaps, integrations, and especially governing real-time data, which has been a weak point in the new architectures. In this talk, we will explore the challenges we see, typical situations, and the available tools to step up and manage with confidence all data lifecycles from generation to consumption, accelerating quality and increasing value.

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.

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.

Data quality issues due to lack of clear data definition ». Example of unclear surface areas definitions.

• Solution: Set up data governance & organization to support it. Implement a data catalog & the choice of DataGalaxy
• Results: Data Catalog usage within Cofinimmo. Efficiency gains and risk reduction thanks to clear data definitions and data quality improvement.

Condé Nast is one of the world's most renowned media companies that creates and distributes content with a footprint of over 1 billion consumers in 32 territories through print, digital, video, and social platforms. At the heart of Condé Nast is data to better understand and delight our customers. The company’s data governance experts play a critical role in maintaining the highest quality of content and ensuring data integrity, security, and accessibility while optimizing consumer engagement. Condé Nast's data culture promotes and measures data governance alongside data search and discovery, enables data leadership, and focuses strongly on data literacy to maximize return on investment for data programs. Learn from data governance expert, Hilda Sadek, on what it takes to build a transformative, high-growth data culture using data governance.

With the growing need for organisations to become data-driven, Data Governance is still a trending topic in the market, although it is only part of the ultimate answer.

Join this session to hear from Precisely and Entain on what is resonating at the strategic level that empowers teams to become more data-driven.

In this session you will learn:

•Why strategic leaders feel data governance is not enough

•The guiding principles for delivering a successful data program

•How you can break down silos with a one-stop shop for data

Data Observability is the new frontier of modern data management. Leading enterprises rely on Acceldata to ensure data quality, streamline operations, optimize costs, and maintain compliance. Join us to discover how to implement Enterprise Data Observability across on-prem and cloud environments, creating a single source of truth for data leaders, engineers, scientists, and business users. Learn from a seasoned industry expert who has successfully operationalized data governance at petabyte scale and delivers reliable data for AI and analytics initiatives.

Lunar, a leading Nordic digital bank, successfully implemented a data governance framework to enhance data quality and secure C-level buy-in by using SYNQ, a data reliability and observability tool. 

Their framework focuses on data ownership, criticality, and monitoring. Lunar's data team, leveraging tools like SYNQ, ensures high standards against financial crime, personalisation through AI, and reliable reporting. 

They maintain oversight through automated monitoring, use of data products, and a robust ownership model, which enhances data quality and accelerates issue resolution for their reports to executives. 

This approach enables Lunar’s data engineering and data governance teams to work in harmony, and operate efficiently without having to increase headcount.