In today's hyper-connected world, many organizations are overwhelmed by the volume of data generated every second. Making timely decisions using this information remains a challenge for many. Real-time intelligence has transformed from a luxury to a necessity for businesses striving to stay ahead in a rapidly evolving marketplace. Enter Microsoft Fabric's Real-Time Intelligence: a new tool that not only analyzes data but also acts upon the results. If you're ready to unlock the power of immediate insights, this comprehensive primer offers an exploration into the capabilities of Real-Time Intelligence with Microsoft Fabric. Authors Johan Ludvig Brattås and Frank Geisler explain AI-driven insights and how to use them to drive business success. Whether you're a seasoned professional or an enthusiast, this guide is the key to understanding an exciting new platform. You'll discover: The core concepts of Real-Time Intelligence within Microsoft Fabric Challenges that can be solved with Real-Time Intelligence, enhancing efficiency Techniques for using KQL queries, including SQL knowledge to optimize these queries Practical applications including data analytic solutions, event streams, and more How to automatically trigger actions based on data conditions

Delve into the critical aspects of risk management and regulatory change in data, analytics and AI. Bring your questions and learn how to identify potential risks, triage and prioritize them, and implement effective mitigation strategies to safeguard your organization’s digital assets to be able to deliver AI projects.

BI has a bad rap as last century’s analytics and static reporting, while AI is the cool kid on the block. Yet most AI projects focus on tech over business impact. With real-time analytics and actionable insights, BI is now focused on driving measurable value across operations, strategy and customer engagement.

Maverick is Gartner's program for unconventional research. Every year we collect the most unconventional, bold and unexpected predictions that aim to end up making your think that there might be something to them. Interactive, fun, high energy and you always win. You either gain a different perspective, or dismiss them and continue on your path with renewed conviction.

AI governance is no longer optional. This session will explore practical strategies for embedding AI governance directly into the design and development of AI architectures, ensuring that compliance, transparency and ethical standards are upheld from the ground up.

The AI agent ecosystem is rapidly advancing, characterized by considerable hype and overwhelming choices. In this session, we provide an overview of the AI agent ecosystem, highlight key players and outline evolving deployment models to help IT leaders drive business value.

As data continues to expand, it is becoming more problematic to provide performant query access to entire datasets. The data twin, implemented as a data product, is a representative subset of the entire population that is significantly smaller. It can be reliably generated and consumed for inference and supporting a range of use cases, including exploratory, estimation and hypothesis testing. In this session, attendees will learn what a data twin is and how to implement data twins to support AI initiatives.

As Microsoft continues to promote and enhance their Microsoft Fabric offering, many clients are asking: How does Microsoft Fabric impact my current Power BI estate? What are some strategies for successful deployment of Microsoft Fabric? How do we scale analytics in Microsoft Fabric and leverage its native AI functionality? This session provides expert insights on Power BI to Microsoft Fabric migrations.

100% of executive leadership discussions involve AI strategy, value creation and headcount reductions for potential cost savings. AI is decoupling revenue from headcount. Here we present the Gartner position on job loss, headcount analysis, layoffs and business value creation. Using real life examples we will review the impact on business processes, skills based analysis, business value and costs.

According to the 2025 Gartner Generative and Agentic AI survey, around 75% of organizations have either deployed or are piloting some form of agentic AI and a similar percentage report the ongoing development of new use case opportunities.
This multi-group discussion session for D&A executive leaders will focus on sharing experiences and addressing the following questions:

-- How are organizations scoping & funding agentic investments? How are these efforts similar or different from other investments?
-- What agentic use cases are getting the most funding right now and what guardrails are they putting in place?
-- How are organizational defining agentic program objectives & outcomes?

Analytics has always been "ground zero" for citizen developers and the low-code/no-code movement. However, the shift toward generative AI and the avalanche of analytics content will overwhelm current analytics operating models, which are already struggling to scale delivery.

Explaining AI/ML models to nontechnical stakeholders is challenging and affects their trust and adoption in production. Data scientists must adopt three practices to enhance model explanations, gain trust, and improve the responsible and ethical use of AI/ML.

We are at the start of a massive, AI-driven feedback loop. A loop between a universal language, Python, a universal engine, Spark, and universal storage, Open Table Formats, that will accelerate us from simple automation to fully agentic, automated data management. This session helps D&A leaders assess their strategy for navigating this disruptive transition and its opportunities and risks.

Data and analytics governance has historically lagged in innovating. We are at a tipping point where D&A governance can be a single point of failure for AI. To succeed, D&A leaders must evolve governance for the future while reinforcing proven practices. This session provides a forward-looking perspective on D&A governance, including governance of AI, by AI and for AI.

Discover the essential steps for developing a successful AI strategy. This session will provide guidance on aligning AI initiatives with business goals to drive innovation and gain a competitive advantage.

This multigroup session will focus on questions like: How should data and analytics teams be structured to maximize business value, align with strategy, and support governance, AI and data quality across distributed environments? Join this session to learn more.

As AI shapes business decisions, making unstructured data AI-ready is a key governance priority. The quality, accessibility and security of unstructured data directly determine the performance of AI applications, particularly for GenAI. To unlock its value for AI initiatives, data and business leaders should evolve their governance strategies to effectively manage, protect and utilize unstructured data, ensuring it is AI-ready while meeting compliance and security requirements.

D&A leaders have a key strategic decision to make over the next few years. What does their strategic and long-term data management platform looks like and where to source it from? There are four options that this session will discuss: utilizing the all encompassing data and AI platform from their cloud service providers, extending their ISV solution providers to enable their data platform, engaging their enterprise SaaS application providers to support D&A use cases, or taking a blended approach.

D&A leaders play a central role in their organization's AI success by providing the critical accelerants needed to realize value from AI. Based on your organization's data, analytics, and AI ambition and where you are today, this session will unfold the vision for data and analytics in 2030. It will describe how your capabilities, operating model and practices need to evolve to realize that ambition.

AI is moving faster than ever. AI techniques should bring adaptability to an uncertain world in constant flux. However, despite its extraordinary power and early promises, AI has not been leveraged to its full potential. What is missing? Where did we go wrong? Join us as we discuss our ambition for the future of AI and AI should do for us to deliver the value that we are expecting.