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. 

The introduction of Generative AI in the enterprise heralds a new era of advanced analytics and operational efficiency. By harnessing the sophisticated capabilities of Gen AI, businesses can significantly accelerate their decision-making processes and empower their employees across multiple dimensions. Gen AI enables intricate data analysis, natural language processing, and decision-making with just a few prompts, facilitating faster innovation and competitive advantage.

However, implementation and optimization of Gen AI for enterprise analytics use cases present several challenges. Gen AI is hard to put into production, due to the complexities associated with data integration and secure data access. Additionally, enterprises struggle to tune and deliver consistently high quality and compelling responses to AI-driven questions.

Join this session to learn how implementing a data fabric can help accelerate time to value and enable Generative AI.

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.

How to unlock business knowledge to accelerate data value.

Big data has moved beyond being just a buzzword; it's now at the heart of modern business strategies. When used effectively and efficiently, data can open up new revenue opportunities, provide deep insights, and even drive social impact. As digital transformation accelerates, data is no longer just a tool—it's woven into the fabric of every part of an organization. Designing and maintaining a tier 1 data platform has become essential to staying ahead of the competition. 

Especially with AI-driven applications on the rise, the convergence of DevSecOps and DataOps is becoming increasingly critical. The recent global disruption caused by a security company's mistake was a wake-up call—highlighting just how high the stakes can be. Building and scaling data platforms isn't enough; security and scalability need to be integral to the entire data lifecycle. 

Bringing more than a decade of SRE experience to maintaining and managing top enterprise software, we will discuss how to tear down silos and encourage collaboration among development, security, operations, and data teams. By doing so, organizations can achieve unprecedented levels of reliability and security. Integrating DevSecOps with DataOps doesn't just automate and protect data operations—it also safeguards data integrity, privacy, and compliance, even as data environments expand in size and complexity. In today's competitive market, this proactive stance is what will set the leaders apart from the rest.

Main Actionable Takeaways:

• Cultivate a Collaborative Culture

• Prioritize Resilience and Recovery

• Integrate Security Seamlessly into Data Pipeline

In today's data-centric business landscape, robust governance, comprehensive auditing, and resilient disaster recovery are paramount for ensuring data integrity, availability, and compliance. This session will explore best practices and advanced strategies for managing and securing your Power BI and Microsoft Fabric environments. Discover how to mitigate risks, optimize operational efficiency, and derive maximum value from your data assets.

Improve your data infrastructure with governance and security, using proven methods and best practices. Break down data silos, foster collaboration, and optimise data accessibility, empowering your business units with the data and technologies they need. Learn how AI improves efficiency and streamlines data product development. And see how Microsoft Fabric simplifies data estate modernization with a focus on unifying your data in an open and governed foundation.

Join us Join us for an engaging and insightful session as we delve into the innovative patterns of mesh, fabric, and knowledge hubs, all grounded in federated operating principles. We’ll explore the common pitfalls encountered on the data journey, key considerations for success, and how Microsoft’s cutting-edge solutions can drive your transformation forward. 

Generative AI (GenAI) has garnered significant attention for its potential to revolutionize various industries, from creative arts to data analysis. However, organizations are realizing that implementing GenAI is not as easy as just asking ChatGPT a few questions. Providing the most relevant and accurate contextual data to the LLM is critical if organizations are going to realize the full benefits of GenAI. Retrieval Augmented Generation, or RAG, is a well understood and effective technique for augmenting the original user prompt with additional, contextual data. However, many examples of RAG grossly oversimplify the reality of enterprise data ecosystems. In this session, we will examine how a Logical Data Fabric can make RAG a practical reality in large, complex organizations and deliver AI-ready data that make RAG effective and accurate.

As many organizations strive to harness the transformative power of Generative AI, implementing a data fabric has emerged as the solution of choice to manage and leverage vast amounts of data effectively.

Join Kaycee Lai, Founder of Promethium, and Matt Clark, who leads data at National Grid Energy Transmission (NGET), as they explore how National Grid is deploying a data fabric to accelerate time-to-insight, enhance data value, and empower AI initiatives. They will discuss the specific challenges their team aimed to address, the strategic approach they took to implement the data fabric, and the critical role that data products play in delivering rapid analytics and enabling Generative AI.

Don’t miss this insightful session to learn how the company is pioneering data fabric technology to drive innovation and efficiency in the energy sector.

How about a workplace where generative AI accelerates every data management task, transforming routine into innovative experiences? A vision which can be in production for the AWS customers in just 60 days through a combination of Amazon Bedrock, which enables rapid development and deployment of AI applications, and Stratio Generative AI Data Fabric, which provides accurate output based on quality data with business meaning. Join us to learn how a combination of these products is empowering data managers and chief data officers to drive innovation and efficiency across their organizations.