Our Keynote Panel brings together three Gold Medal Olympians to discuss how they overcame personal challenges and use data to achieve success at the highest levels of sport.

Moderated by Clare Balding, the conversation will delve into how data analytics has transformed their training and competition strategies. They’ll share insights on how data is used across different sports to optimize performance and gain a competitive edge. The discussion will highlight the balance between analytical approaches and the instinctive, experiential aspects of competition.

Attendees will hear inspiring stories of triumph over adversity and gain a deeper understanding of how data is driving success in elite sports today. 

This session offers valuable perspectives on the future of sports analytics and its impact on athletic performance.

This session looks at the ever-increasing demand for data and AI, the current challenges slowing development and how companies can overcome these challenges and shorten time to value using generative AI and open tables like Apache Iceberg. It also looks at how this approach makes it possible to transitioning away from siloed analytical systems to a modern data architecture where multiple teams can create reusable data products across multiple clouds and op-premises environments using generative AI in Data Fabric and share that data across multiple analytical workloads. 

This session explores Gemini's capabilities, architecture, and performance benchmarks. We'll delve into the significance of its extensive context window and address the critical aspects of safety, security, and responsible AI use. Hallucination, a common concern in LLM applications, remains a focal point of ongoing development. This talk will highlight recent advancements aimed at mitigating the risk of hallucination to enhance LLMs utility across various applications.

Snowflake had a big challenge: How do you enable a team of 1,000 sales engineers and field CTOs to successfully deploy over 100 new data products per week and demonstrate every feature and capability in the Snowflake AI Data Cloud tailored to different customer needs?

In this session, Andrew Helgeson, Manager of Technology Platform Alliances at Snowflake, and Guy Adams, CTO at DataOps.live, will explain how Snowflake builds and deploys hundreds of data products using DataOps.live. Join us for a deep dive into Snowflake's innovative approach to automating complex data product deployment — and to learn how Snowflake Solutions Central revolutionizes solution discovery and deployment to drive customer success.

In today’s rapidly evolving technological landscape, the integration of data within organisations is not just a trend but a necessity. This panel discussion will explore how data literacy and the adoption of a data-driven culture can act as catalysts for significant organisational change. We will delve into the roles of Chief Data Officers, Chief Innovation Officers, and Chief AI Officers, examining whether history is repeating itself with new and emerging roles. The discussion will be punctuated by shifts in technology capability and will address whether AI is a true catalyst for organisational change.

Join us to explore the essentials of crafting an effective data strategy. This session features real-world success stories, practical implementation insights, and discussions on the many components required for data success. If you're eager to drive business growth, make data-driven decisions, and gain a competitive edge, don't miss this session. It's your opportunity to gain the knowledge, tools, and inspiration needed to master the art of data strategy and propel your organization toward data-driven success! Key Takeaways: ? Practical Implementation: Discover actionable steps and best practices for implementing a data strategy within your organization. Learn how to align your data initiatives with your business goals. ? Data Strategy Essentials: Get a comprehensive overview of the core components that make up a successful data strategy ? Q&A and Networking: Pose your burning questions to our expert and connect with fellow attendees to expand your professional network and share insights.

In many scenarios where marketing campaigns are run across multiple channels and selective markets/audiences, established techniques for measuring incremental benefits such as randomised control trials are not feasible. So how can we inform decision makers on the performance of their marketing campaigns? I'll walk through how BBC Studios have used Synthetic Control groups across geographic holdout regions to measure the outcomes of our marketing campaigns across the world, how they work, how we have implemented them and best practices to apply in your businesses.

Roche, is one of the world’s largest biotech companies, as well as a leading provider of in-vitro diagnostics and a global supplier of transformative innovative solutions across major disease areas. Over the past few years, they’ve undergone a migration to the cloud, adopted a modern data stack and implemented data mesh in order to double down on improving data reliability.

Join the data team at Roche to learn how they’ve leveraged data observability to support their sociotechnical shift to data mesh. They walk through their multi-year data observability journey, digging into how they implemented Monte Carlo in a global organization. They’ll also share their approach to data mesh at Roche and deep dive into a current use case. 

When was the last time you performed a mathematical operation on an email address? Or multiplied a credit card number by a passport number?

It's absurd, this would be an insane thing to do, yet we keep storing sensitive customer information in our data warehouses, risking PII exposure, as if we need to perform operations like this. This forces our data teams to act as data police, controlling access—a job that's the definition of “not fun” and quickly becomes unmanageable. Companies are then faced with the difficult choice of either locking down all access or granting over-privileged access, neither of which is ideal.

But what if there's a better way?

In this talk, we'll explore how leading tech companies with the largest amount of customer data solve this problem. We'll look at the architectural patterns they use to balance data security and usability, and how these solutions can free our data teams from their policing duties.

In a world where Artificial Intelligence is the new normal, interpersonal skills like critical thinking, persuasion and emotional intelligence will sit alongside the traditional skillset of the data leader as businesses are now scaling and monetising their AI initiatives. 

Organisations must ensure that their leadership is balanced to avoid bias and ensure relevance to the customer, and the leader of the future will be the linchpin to ensure that the opportunity from AI is realised. o how should businesses nurture emerging leaders to ensure that they are developing and retaining top talent in an age of acute skills shortage and salary inflation? 

And how can future leaders equip themselves with the right skills and networks to build sustainable careers right up to the C-suite? 

Join this panel of experts as they discuss the future of leadership in a world where artificial intelligence is central to decision making and why getting it right is a business imperative.

The data landscape is fickle, and once-coveted roles like "DBA" and "Data Scientist" have faced challenges. Now, the spotlight shines on Data Engineers, but will they suffer the same fate? 

Thistalk dives into historical trends.

In the early 2010’s, DBA/data warehouse was the sexiest job. Data Warehouse became the “No Team.”

In the mid-2010’s, data scientist was the sexiest job. Data Science became the “mistaken for” team.

Now, data engineering is the sexiest job. Data Engineering became the “confused team”. The confusion run rampant with questions about the industry: What is a data engineer? What do they do? Should we have all kinds of nuanced titles for variations? Just how technical should they be?

Together, let’s go back to history and look for ways on how data engineering can avoid the same fate as data warehousing and data science. 

This talk provides a thought-provoking discussion on navigating the exciting yet challenging world of data engineering. Let's avoid the pitfalls of the past and shape a future where data engineers thrive as essential drivers of innovation and success.

Main Takeaways:

● We need to look back on the history of data teams to avoid their mistakes

● Data Engineering is following the same mistakes as Data Science and Data Warehousing

● Learn the actionable insights to help data engineering avoid similar fates

The Dell AI Factory with NVIDIA is a framework to accelerate and de-risk AI adoption and AI powered innovation in the enterprise. Join us to explore how – with this open and extensible end to end solution – we help organisations align the right use case to the most impactful business outcomes.

We will showcase how organisations are leveraging our broad range of capabilities and ecosystem of partnerships, to take advantage of their enterprise data. From the edge, through to the multicloud and private data centre environments, together we’ll explore how to build differentiated and effective business capabilities. 

Everything has changed in the last year with Generative AI entering onto the scene. This means a re-shuffling of priorities and budgets, putting AI-enabled Data & Analytics right back at the top of the agenda. In this session we will discuss: 

• That there is no Generative AI without data – but it has to be the right data 

• The importance of being able to bring together organised and trusted data 

• Why your data integration strategy is the foundation to successfully using AI

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.

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.

Is your data modelling limited by one person or a small team? Does the idea of scaling up the team sound too big of a challenge?

During this presentation, Simon will discuss the evolution of the data model, provide tips on what knowledge and resources are needed to build a data model, explore the common problems when scaling and discuss tooling, and what will help you work smarter, not harder.

This session will help you (or your team) to accelerate the creation of data models.

Other than people, Data is Policing’s biggest asset. With Data being managed differently across Forces how does Policing understand what Data it has and how it is being utilised to support Policing’s objectives? Join Aimee Smith and Kate Boyle to discover how the National Police Data & Analytics Board and PDS successfully delivered Data Maturity assessments across 43 Forces on behalf of the National Police Chief’s Council.

In an increasingly multicultural and globalised world, understanding cultural identity has never been more vital. With more than 200,000 years of human evolution, our capacity for culture has thrived as we journeyed from the equator to every part of the globe. We’ve learned, traded and sometimes battled with a growing and diverse global population. Today, our diversity is more significant than ever, especially as we grapple with the lightning-fast evolution of AI technology and look for solutions to global challenges.

Join us in this session as we draw from historical insights that remain highly relevant for our collaboration in a modern context. We’ll explore the cost of conflicts and the disproportionate investment required to better understand our diversity. What does a nuanced, multifactorial, data-driven approach to cultural diversity entail? And what is the price we pay when we oversimplify methods of measuring and categorising our most precious asset – our multifaceted culture.

Simplify your GenAI journey and unlock the hidden power within your databases. Businesses often feel pressured to adopt new, specialized technologies to stay ahead. However, the power to revolutionize your applications with GenAI may already reside within your current database infrastructure. 

We’ll build understanding of vector capabilities, ease of use/ROI, and how PostgreSQL, enhanced with the pgvector extension, can address 80% of common GenAI use cases, providing a streamlined and cost-effective path to AI-driven solutions.

Join us to demystify the hype around dedicated vector databases and explore how built-in vector capabilities existing databases can efficiently support your GenAI workloads without extra overhead.

Recent advances in artificial intelligence have sparked both wonder and anxiety as we contemplate its transformative potential. To nurture a future where AI is leveraged to the benefit of people and society it is vital to understand the importance of responsible AI practices, guided by principles such as fairness, inclusiveness, and transparency.

In this session we will discuss practical tools and resources for implementing these practices, as well as the role of the Responsible AI and Effects in Engineering and Research.

Every day, banking institution Capital on Tap is calculating thousands of credit scores, directly impacting how their customers receive credit cards or additional lines of credit. Data quality is paramount – incorrect credit scores can set off a wide range of long-lasting financial implications for their customers, which is why the team turned to data observability with Monte Carlo, to improve their data – and credit score – reliability. 

But, as with any new tool in your tech stack, onboarding new processes for key users is just as important as onboarding the tool itself. 

Join this session with Ben Jones and Soren Rehn, to hear why the Analytics Engineering team at Capital on Tap decided to invest in a data observability tool, how their processes play a critical role in maximizing the tool’s value (including a few missteps and recalibrations along the way), and the strategies employed to garner widespread success and buy-in over time.

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.