Why Attend? This session will equip you with the foresight and practical knowledge to integrate GenAI into your strategy successfully. You'll gain knowledge from real-world examples, helping you to embrace the next phase of AI development confidently.

• Look ahead to the future of Generative AI as we discuss emerging trends and new possibilities including autonomous agents and interactive AI. 

• We'll discuss how GenAI will continue to shape our world and what to expect in the coming years. 

• Get practical advice on how businesses can integrate GenAI into their operations, train their teams, and navigate data privacy and responsible AI.

• We'll share real-world success stories from industries like healthcare, finance, and entertainment, illustrating how GenAI is revolutionising these fields and making a significant impact on our daily lives.

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.

In a rapidly evolving landscape, the ability to pivot, reskill, and transition is not just advantageous—it's essential. 

This panel discussion, hosted by Uma Parekh - Senior Associate at Kubrick, will delve into the transformative power of career redirection and skills transference in the era of data-driven innovation. Uma, who transitioned from a successful career at a 'Big 4' consultancy to lead teams building next-generation technology, brings first-hand advice through telling her story. 

Joining Uma are representatives from Esure and the Metropolitan Police, organisations at the forefront of integrating data-centric strategies within their industries. 

Together, they will explore how organisations can effectively harness the potential of reskilling their existing workforce, the challenges for professionals transitioning into new domains, and the critical role that transferable skills and diverse experience play in driving innovation. 

This discussion will provide valuable insights for individuals and businesses alike, aiming to stay competitive and innovative as we move further into the world of AI. 

Artificial Intelligence has transitioned from a niche concept to a widespread force shaping the business world's landscape. Streaming and AI integration have emerged as crucial drivers in this digital transformation era, focusing on the dynamic and real-time facets of data flow to generate contextually relevant predictions.

Businesses across diverse sectors increasingly adopt AI technology to optimise operations, stay competitive, and augment user experiences. However, AI's true potential only unfolds when applied to the right data sets, at the right moment, and within the appropriate context. In this session, Italo will discuss how AI and Streaming can work together to provide the latest and freshest data, be it about our customers, your business, or the market to your business.

Even as data teams remain lean in 2024, data engineers are still expected to swiftly deliver data for various use cases. Adding new data sources and updating existing ones consumes nearly half of a data engineer's time, hindering your organization's data and AI-led goals. Rivery's modern data platform solves this issue across all your data sources with an innovative blueprint and generative AI. Join this session to learn how to overcome unscalable data pipeline challenges and unlock the benefits of all of your Data.

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.

Many organisations know of the importance of data culture, especially when undertaking a digital transformation (I.e cloud transformation). And the ?holy grail? of getting it right is often well stated. But what about the bad, and the ugly as well as the good? And what does that look like when you are talking about an organisation the scale of Lloyds Banking Group? This talk is intended to draw back the curtain behind our data culture journey here at Lloyds (though not making it all about us) as a way to truly highlight some of the pitfalls, successes and approaches we have and are taking on our data culture journey.

Manual data processing can be time-consuming, prone to errors, and diverts attention from creative data applications. Contrary to common belief, data platforms do not have to be built by people with 10+ years experience nor do they take months to build - what if I tell you that you could do it in weeks with low- to no-code?

Totally Plc’s Data Vault implementation has provided a foundation for data driven decision making and improved engagement with the business’ data professionals. Learn how this initiative has helped build trust and transparency in the company’s data usage across business units and also how it has supported the efficient alignment, migration and integration of changing business systems.

Join this session to discover how DataStax Astra DB can boost productivity, deploy GenAI apps in minutes, and transform customer experience. We’ll showcase an advanced semantic search use case on vectorising entire videos with specific timestamps and use natural language processing to find precise moments from the Olympics. Learn about the open-source model that runs locally, making this powerful tool both accessible and free. Additionally, explore hybrid search capabilities to integrate multiple videos into a single collection and streamline processes by only loading embeddings and metadata. Perfect for enhancing content management and delivering exceptional user experiences.

PrimaryBid is on a mission to improve the global IPO market by allowing frictionless participation to more investors than ever before. Central to achieving this mission is the ability to deliver real-time data analytics to a range of audiences. Join us as we discuss our journey to building a best-in-class embedded analytics solution. From dissecting what it means to be best-in-class in 2024, through to identifying constraints and choosing the right technology partners - we?ll provide a how-to, and how-not-to, on creating a premium analytics experience. In an industry where speed, aesthetics, reliability, security, and governance are paramount, discover how we optimize across all dimensions. The session includes a comprehensive overview of our progress to date and a live demonstration showcasing our product in action.

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. 

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 our data community, we tend to use a lot of technical jargon that is meaningless to business executives seeking outcome-oriented solutions. Instead of your business cases getting shuffled into technology budgets, bring your AI initiative to the forefront by focusing on business priorities and value. Data mesh, data fabric, data lakehouse projects and others have failed to do this, and have taken a toll on the rigor required to make your AI case. In this session you will learn to flip the script - talk value first, educate and provide data literacy to your executive team and stakeholders, and make your AI solutions a reality in record time, with the right level of investment.

For over three decades we have been powering people and businesses to think and behave differently. In this session, we will share our insights into how you can build the right data culture, by SEEing the value of data through three key pillars: Sponsorship, Education, and Embedding. Detail ? Data helps us to make better, more informed decisions ? and the role of data should not be considered as an ?add-on? to existing capabilities but something that underpins all of those capabilities. Organisations need to understand that this is not just an incremental, evolutionary shift that gives better access to data and richer visualisation ? but something truly transformative when a strong data culture is embedded, alongside elements such as predictive analytics and artificial intelligence. We will discuss with attendees: The importance of adopting a ?shift left? mindset to the use of data in understanding problems, and the designing, developing, testing and operating of solutions. The criticality of investing in culture, which has a disproportionately positive impact on the success of data transformation programmes. The success which can be achieved by following the SEEing model: ~ Sponsorship means demanding better data in order to make better decisions from the Board downwards, and equipping sponsors of business and change programmes to seek and know how to use the right data to deliver better outcomes. ~ Education means helping people understand the value that data can give them; driving demand for data and helping people to see that it should be a foundation in everything they do. ~ Embedding means making data experts integral to teams, in a similar way that DevOps brought operational staff into development teams, which helps to build up understanding and trust between data experts and data users/beneficiaries, increasing domain knowledge in data experts and data/analytics knowledge in the rest of the team. The session will conclude with Q+A.

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

Join Scott Gamester as he challenges the outdated promises of legacy BI and self-service analytics tools. This session will explore the key issues that have hindered true data-driven decision-making and how modern solutions like Sigma Computing, Databricks, and Snowflake are redefining the landscape. Scott will demonstrate how integrating these platforms empowers business analysts, driving innovation at the edge and enabling AI-enhanced insights. Attendees will learn how these advancements are transforming business empowerment and fostering a new era of creativity and efficiency in analytics.

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.

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.

Your world is filled with an ever-changing landscape of tools that create and use metadata. Each tool is useful, but independent, unable to share and link what it knows to information from other tools. The result is a disconnected story throughout your data and AI operations, making it hard to know where data came from, how it can and should be processed; leading to uncertainty in the trustworthiness of your AI results.

Using open source software from the Linux Foundation, (including Egeria, Open Lineage, Unity Catalog) we will share a simple approach to incrementally link and govern these tools to create end-to-end lineage, provenance and information sharing along your tool chains.

Let's Get the Basics Right Data is everywhere but how much of it is communicated effectively? Lots of work goes into the curation of data sets only for it to fall at the last hurdle as the key insights are lost through poor visualisation and storytelling. This session will cover some of the foundations of data storytelling and data visualisation. Think is it as a to do list that will help you ta-dah your stakeholders.

Discover how a leading optics & photonics manufacturer is revolutionising its technical support by implementing an AI chatbot, resulting in significantly reduced response times and improved accuracy, ultimately enhancing customer satisfaction and operational efficiency.

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.