Ben Jarvis

As organisations accelerate AI adoption, leaders face a growing challenge: how do you balance innovation, security, governance and control? For organisations operating in highly regulated industries, these considerations are becoming central to AI strategy. In this session, Telefónica Tech in partnership with Cosine explore the role of sovereign AI within a modern enterprise AI strategy and how it complements existing investments in public and private foundation models. We'll discuss the challenges organisations face when deploying AI against strict regulatory, compliance and data sovereignty requirements, and why sovereign AI is emerging as a key enabler of trusted adoption. You'll gain practical insight into where sovereign AI fits within the enterprise AI landscape, how it can support secure and compliant AI deployment, and the steps organisations can take today to prepare. We'll also share lessons and insights from Britain's Sovereign AI Model programme with Cosine, helping attendees understand how to build AI capabilities that balance innovation with trust, governance and control.

How can data and AI help turn open crime data into insight officers can act on? In this session, we show how the Databricks Data Intelligence Platform can be used end-to-end to support modern, data-driven policing. Using an open crime dataset, we demonstrate how Databricks Lakeflow simplifies data ingestion, preparation and governance, creating a trusted analytics foundation. We then apply machine learning to identify crime patterns and emerging hotspots, and use Mosaic AI to generate clear, GenAI-driven recommendations focused on prevention and smarter resource allocation. Finally, we bring these insights to the frontline with Databricks Apps and Genie Rooms, enabling officers and analysts to carry out secure, ad-hoc analysis using natural language, no SQL or data science skills required. This session is for data leaders and public sector teams looking to apply AI responsibly to real-world challenges and turn data into decisions that make communities safer. Key Takeaways • Understand how a solid data foundation enables data & AI to come together and enable real benefits • See natural language BI in action to enable self-service • Learn about what needs to be in place to make a data project a success

Fabric provides us with a plethora of tools to process and analyse our data, which provides data engineers and other users with a great foundation to build a enterprise-level data solution on.

How does AI impact data engineers? Learn how AI is having an impact on the way we work whether that be using GitHub Copilot to write code, Power BI Copilot to explore our data or the power of the Azure OpenAI Service in CluedIn to clean up poor quality data.

In this session I will demonstrate how you can implement private link technology to secure your PAAS components across your Data-Platform. Private endpoints are talked about more and more therefore as part of this session I will describe what Private Link technology is. I'll then go on to demonstrate the use cases for Private Link, the considerations that need to be taken into account and some example architectures that can be used when implementing across modern Data-Platforms. I will also show how Private Link can be deployed and implemented through Infrastructure as code to enable you to scale and deploy Private Link in a consistent and secure way.

In this session we will look to understand the components and patterns that are required to set your data platform up for success when defining a high-level architecture. At a high-level we will cover areas from ingestion through to transformation and serving and look to identify the key...

Do your source systems contain duplicate data? Are the business begging you to merge together multiple, disparate sources to provide a single view of entities such as products and customers? Are you struggling to meet these requirements using PaaS services in Azure? If so, then this session is for you. In this session we will explore master data management and the benefits it can bring to your organisation. We'll explore what master data management is, understand the different types / levels of maturity that can be adopted and then dive into a real-world example to understand how we can utilise PaaS services in Azure to build a matching engine that can match and master large amounts of data in a scalable and future-proof manner. Finally, we'll explore how you can serve mastered data to other applications within the organisation and provide business users with a method to amend and enrich mastered data. This session is based on learnings from real-world master data management projects and will explore how we can adopt an iterative approach to master data management, using Azure Databricks with ready-built open source implementations of matching algorithms to enrich our matching engine and ensure we get the highest possible match rate. No specific prior knowledge is required to attend this session however, some understanding of Azure services such as Databricks is useful.

Do your source systems contain duplicate data? Are the business begging you to merge together multiple, disparate sources to provide a single view of entities such as products and customers? Are you struggling to meet these requirements using PaaS services in Azure? If so, then this session is...