Data governance has traditionally encompassed analytics governance, managing most risks and value in traditional analytics. However, AI introduces new risks and considerations that D&A governance may not be equipped for. Should D&A governance evolve to govern AI or is it time for a separate discipline with a fresh mandate? This session explores conflicting accountabilities, leadership and operating models between these disciplines.

Data quality and data observability tools provide significant capabilities to ensure good data for your BI and AI. Data observability tools give organizations integrated visibility into the health of their data, data pipelines and data landscape. Data quality tools enable business users to manage data at its sources by setting rules and policies. Together, these tools help organizations build a strong foundation in data management for BI and AI initiatives.

Leaders responsible for AI must evaluate the potential benefits and costs of new agentic AI use cases. This insight offers a framework, model and specific numbers for building and simulating total cost and key value drivers at scale for AI agent use cases.

National AI sovereignty is a nation-state’s desire to control its own AI, reducing reliance on foreign innovation, talent, and vendors. Ambition and maturity vary globally, creating commercial opportunities and risks. This session explores various nation state approaches of sovereign AI and the public policy initiatives, including global AI regulations, that support them. Attendees will leave with a blueprint to identify value creation strategies while confronting geopolitical uncertainties.

This hands-on workshop is for D&A leaders seeking to unlock AI’s full potential as a professional co-pilot. Learn why mastering prompts is the first step to true AI fluency. Through live exercises focused on real-world D&A scenarios, you will learn the core techniques to build a more efficient and impactful analytics practice. Leave with a tactical toolkit of prompt engineering skills you can use immediately and a clear vision for your strategic AI value.

AI spending continues unabated and so is the pressure on leaders. CIOs need to demonstrate the value of AI. CFOs need to calculate it. The c-suite needs to collaborate to create real value. Here we present Gartner's framework for a rigorous, repeatable approach to financial operations (FinOps) of AI initiatives. The focus is on the economics of business value, cost and risk of AI, GenAI and agentic AI.

AI agents are fundamentally transforming traditional D&A operating models. D&A leaders must recognize this shift and adapt their operating models to integrate AI agents, ensuring AI-ready operations, enhanced value delivery and improved decision making. In this session, we will share three real client cases from Mastercard, Danone and Inspur that demonstrate how organizations have successfully made this transition.

No data? No AI! No metadata? No data! Gartner codified AI-ready data concepts and crafted the language in mid-2022. Practices have evolved for the four AI-ready data modes and 30 expected data evaluation and preparation steps. In this session, we recap the approach combined with how organizations are using AI data readiness as a foundation to evolve toward standardizing data for production AI.

AI leaders must strategically structure their organizations to maximize the business value of AI. This session equips you with practical guidance to build effective and scalable AI operating models and organizations.

Analytics and BI platforms and data Science and machine learning platforms are important technologies that drive insight-driven decision making and allow AI systems to be built and operationalized throughout the enterprise. This session unpacks the Magic Quadrants of both markets and gives inisght on the trends that you should be aware of.

The enormous potential business value of AI will not materialize spontaneously. AI leaders should guide their organizations toward an era in which AI is not only creating tangible business value but goes beyond to become a critical competitive differentiator and industry disruptor.

AI is drastically changing how organizations do business and operate. As a result, data, analytics and AI must be approached differently, with a focus on value — the organizational capabilities, competencies and processes required to utilize AI and data to redesign workflows and enable decisions orchestrated by both people and machines in near real time. This session gets you grounded on the capabilities of the intelligent enterprise, why it is important and how to pivot toward this model.

RAG has emerged as a powerful approach for building advanced AI systems that combine the strengths of large language models with external knowledge sources. However, RAG solutions struggle with reliability and require a lot of experimentation. This session will address key questions to help determine the best design pattern and optimization for RAG implementations.

AI agents are becoming a critical AI trend as they enable levels of business adaptability, flexibility and agility that can’t be achieved with traditional AI systems trained for a specific task. Their flexibility is valuable in unpredictable operating environments and real-time monitoring and control aren’t practical. Autonomous behaviors have significant societal, legal and ethical implications, but are the answer to the increasing complexity paralyzing our enterprise systems.

Join an interactive executive discussion on emerging trends from the 2026 CDAO Agenda Survey, highlighting cost optimization for D&A, scalable design patterns and the use of AI to improve D&A operations. Discover how these insights can drive efficiency and innovation across your organization.

This session will provide a blueprint for the comprehensive data, analytics, and AI capabilities and services that leaders need to build to enable their organization's AI ambition. It will explore the implications of your AI ambition archetype on your buying, deployment, and organization strategies.

Organizations struggle to scale AI governance, mitigate risk, and effect compliance while delivering value in AI deployments. AI governance programs only check the box if they are not adaptive and embedded into the fabric of the AI lifecycle. In this session, you will learn how to define your role in AI governance, assess what technology capabilities are required to govern AI at scale, and make an investment decision in an AI Governance Platform or other technology tool.

This session covers the use and output of Gartner’s AI-Ready Data Toolkit, which includes practices for both structured and unstructured data. The process develops metrics that “stack” as you progress from POCs to multicontext data use, operationalization and production support. The session also explains how to customize the toolkit with your own thresholds and readiness analysis.

AI is accelerating new possibilities for data and analytics everywhere. Success isn’t always about being the fastest, but about finding your own path to value, while managing risk and cost. Join our Gartner’s Opening Keynote to discover how a thoughtful approach to speed and direction helps you prepare for what’s next, no matter where you are today.

Gain expertise in modern time series forecasting and causal inference in R to solve real-world business problems with reproducible, high-quality code Key Features Explore forecasting and causal inference with practical R examples Build reproducible, high-quality time series workflows using tidyverse and modern R packages Apply models to real-world business scenarios with step-by-step guidance Purchase of the print or Kindle book includes a free PDF eBook Book Description Modern Time Series Analysis with R is a comprehensive, hands-on guide to mastering the art of time series analysis using the R programming language. Written by leading experts in applied statistics and econometrics, this book helps data scientists, analysts, and developers bridge the gap between traditional statistical theory and practical business applications. Starting with the foundations of R and tidyverse, you’ll explore the core components of time series data, data wrangling, and visualization techniques. The chapters then guide you through key modeling approaches, ranging from classical methods like ARIMA and exponential smoothing to advanced computational techniques, such as machine learning, deep learning, and ensemble forecasting. Beyond forecasting, you’ll discover how time series can be applied to causal inference, anomaly detection, change point analysis, and multiple time series modeling. Practical examples and reproducible code will empower you to assess business problems, choose optimal solutions, and communicate results effectively through dynamic R-based reporting. By the end of this book, you’ll be confident in applying modern time series methods to real-world data, delivering actionable insights for strategic decision-making in business, finance, technology, and beyond. What you will learn Understand core concepts and components of time series data Wrangle and visualize time series with tidyverse and R packages Apply ARIMA, exponential smoothing, and machine learning methods Explore deep learning and ensemble forecasting approaches Conduct causal inference with interrupted time series analysis Detect anomalies, structural changes, and perform change point analysis Analyze multiple time series using hierarchical and grouped models Automate reproducible reporting with RStudio and dynamic documents Who this book is for This book is for data scientists, analysts, and developers who want to master time series analysis using R. It is ideal for professionals in finance, retail, technology, and research, as well as students seeking practical, business-oriented approaches to forecasting and causal inference. Basic knowledge of R is assumed, but no advanced mathematics is required.