Data ecosystems, built on data fabric design and infused with AI, promise an integrated, cost effective, and operationally simple approach to varied data management challenges. However, they don't yet always deliver on that promise. This research explores the maturity of various ecosystem components and provides a guide for D&A leaders and others looking to invest in data foundations for competitive differentiation.

As enterprises embrace GenAI and intelligent agents, securing sensitive data—like PII, financial records, and IP—while maintaining compliance is crucial. This session explores how Skyflow helps meet modern privacy demands, including India’s DPDP Act, using polymorphic encryption, tokenization, consent management, and fine-grained access controls. See real-world architectures that show how to embed privacy into both legacy and AI-first systems, enabling innovation without compromising security or regulatory compliance.

CDAOs and AI leaders are grappling with two crucial questions: 1. What public cloud provider should we choose for AI and GenAI initiatives, and 2. how do we assemble the right cloud architecture to scale and deploy AI more effectively?
This session compares public cloud AI and Generative AI architectures from AWS, Azure and GCP and provides insights on their points of differentiation.

AI is having a huge impact, but is not the only thing with societal, technological, and organizational implications driving change in data and analytics. We examine trends in areas such as complexity, trust, and empowerment facing leaders and teams as they make decisions in all aspects of their bet-the-business D&A strategy.

In this session, we will explore cost transformative vendors such as DeepSeek and MistralAI’s impact on AI development, highlighting the potential to enhance enterprise innovation at a price/performance that is unprecedented. Data, analytics & AI leaders can bring their questions on how they can capitalize on new training and inferencing paradigms in AI, the future of AI model development and the impact of reasoning models on a more autonomous future in AI.

Lakehouse has become a cornerstone in managing large and heterogeneous data by providing capabilities that simplify organizational data architecture, unify data assets, and help streamline, harmonize, and enhance data processes, operations and governance. Lakehouse provides high value to the organization, reduces technical debt, and prepares the organization for new frontiers like AI.

AI systems can have significant impact in organizations decisions and product offerings. This has triggered a complex regulatory landscape and related organizational guidance to ensure responsible and ethical implementation of AI systems. This session provides the technology framework supporting the processes and policies for a reliable AI system that is fair, safe and secure.

Heads of analytics & AI face many challenges such as keeping afloat, staying the course or racing ahead in the race for AI superiority. This session will focus on the top fears, risks, objections and hurdles leaders of Analytics & AI face and most importantly, how to handle them.

The emergence of Agentic AI will revolutionize automation and efficiency gains cutting across businesses. However, challenges like hallucination and inconsistency hinder the immediate adoption of GenAI models. Utilizing Agentic AI to tame the inherent variability of unstructured data, which is at the core of every white collar work, is the basic step to exploit larger AgenticAI automation in enterprises. This is based on our experience of designing hundreds of automation solutions. We will showcase a few use-cases in this session.

The fourth industrial revolution is fueled by AI and data. This talk will describe how to create and consume data products leveraging capabilities of Generative AI. Empowering knowledge workers with self-service access to data is an essential capability of a modern enterprise. Robust metadata and a framework for Minimum Viable Governance (MVG) is at the heart of trusted AI. The use of Generative AI to automate creation of data products will be discussed along with critical techniques to ensure integrity of the data product design. The use of natural language interfaces for interrogating data products will also be discussed.
-Learn about the anatomy of data as a product in support of corporate AI initiatives.
-Learn about the concept of Minimum Viable Governance to drive the right balance between agility and governance.
-Learn how business users can be empowered to create data products through a scalable manufacturing process using Generative AI.

Qlik Fireside Chat: Unifying Data, Insights & Intelligence: How Industry Leaders Are Driving Business Value

In an era defined by data, industry leaders are turning to platforms that not only analyze data but also unify and activate it across the enterprise. Join senior leaders from Mahindra & Mahindra, Ather Energy, and Parle Biscuits as they explore how data integration and real-time analytics to AI and ML—are helping them drive operational efficiency, accelerate innovation, and make smarter, faster decisions. Discover how these organizations are future proofing their data strategies while delivering measurable business outcomes.

AI-powered decisioning is here—but why aren’t enterprises fully leveraging its potential? Many organizations still struggle with fragmented decisioning engines, inefficient workflows, siloed data and lack of AI governance.
Join us for an engaging discussion with your peers. We’ll tackle the real barriers to AI-driven automation, explore strategies for integration, and explore how organizations are achieving measurable impact through seamless, scalable decisioning.

Industry Focus: Banking | FS | Insurance | Manufacturing
Level of attendees: Analytics Experts| Credit Mgrs, Sr. Mgrs, BU Head

There’s a lot of interest and buzz around AI agents, however, what separates an agent from an assistant? How do you bring agents to production and drive ROI for your business? Join IBM, clients, and partners, to learn how the latest AI agents and assistants are empowering teams with needed expertise, turning piles of data into personalized insights and automating business processes across various enterprise domains.

Explore how Salesforce Data Cloud and Tableau, powered by intelligent agents, are revolutionizing data-driven decision-making, enabling seamless analytics and faster insights. This session explores how unified customer 360, real-time analytics, and AI-driven insights empower organizations to act swiftly and innovate effectively.

Getting AI/ML innovation out of the lab and into production means operationalising and scaling complex pipelines, and ensuring that the resulting signals are pushed into operational workflows where they can be actioned. We will briefly introduce Teradata’s extensive AI/ML capabilities and provide examples of how customers leverage them. We will also discuss Agentic AI, what it means for the data platform - and how Teradata’s industry-leading vector store is enabling customers to deploy scalable RAG applications.

Philips, with its legacy of innovation and diverse AI portfolio, recognized the potential of Large Language Models. To enable scalable and responsible Generative AI adoption, Philips established the "AI Foundation." This multi-disciplinary team provides Generative AI as a service, addressing, awareness and education, rapid iteration, enterprise data integration, platform experiences, and robust compliance. Learn how The AI Foundation empowers Philips teams to build innovative, Generative AI-powered solutions at scale through foundational services and a governed platform.

The enormous potential business value of AI is not going to materialize spontaneously. AI leaders should guide their organization 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.

As enterprises accelerate their adoption of AI, a critical challenge emerges: how to move from static post processing to AI systems that adapt and act continuously in real time. Combining data, context and behavior is essential for enterprises seeking competitive advantage through automation, personalization and operational efficiency. From fraud detection to supply chain optimization and intelligent customer engagement, enterprises will require low-latency data platforms with rapid ingestion, instant complex inference and real-time feedback loops to build adaptive AI solutions.

This session explores how agentic AI—autonomous systems powered by large language models—drives real business value by automating complex, multi-step workflows. Attendees will learn how IBM’s watsonx Orchestrate platform enables enterprises to build, manage, and scale AI agents that improve productivity, reduce costs, and enhance customer and employee experiences across sales, HR, IT, and more. The session highlights real-world use cases like IBM’s AskHR agent and offers insights into low-code/no-code tools for rapid AI agent development. Join to discover how agentic AI transforms digital labour and accelerates enterprise automation at scale.

Trust models enable D&A governance leaders to understand how data, analytics and AI assets are used across their enterprise to support business outcomes. This session will help you understand what trust models are, and how they can be used to drive better business outcomes.

Generative AI costs can rapidly escalate due to poor architectural decisions, lack of operational know-how and inadequate governance. In this session, we outline the top ten best practices that D&A leaders can adopt to optimize costs, enabling them to achieve quicker business value and operational efficiency.

The rapid advances in generative AI are fueling great excitement. Within just a few years, one-third of generative AI interactions are expected to utilize autonomous agents, propelling a new wave of productivity for enterprises. However, this potential can only be realized if the challenges surrounding AI trustworthiness, inferencing costs, domain-specificity, and effective and secure leveraging of quality enterprise data can be overcome. Data and AI leaders require a practical approach to accelerate AI adoption. Discover actionable techniques to maximize the value of your data for AI, learn from real-world examples of data and AI driven innovation in defence and aerospace, and gain insights into fostering greater AI productivity across your teams with IBM watsonx.

Organisations are racing to adopt AI/ML solutions. But AI/ML solutions are only as trustworthy as the data used to train them, and are only impactful where we can deploy and operationalise them at scale. In this session we will discuss proven approaches to improving quality, reducing time-to-market for new analytics, and improving Data Scientist productivity, whilst also addressing concerns about ethics, compliance and data leakage.

As enterprises accelerate their digital transformation journeys, the convergence of AI, data, and analytics is reshaping decision-making at every level. CDOs and CIOs are at the forefront of this shift, balancing data strategy, governance, and AI-driven automation to drive competitive advantage.

As AI rapidly moves from experimentation to deployment, one truth is becoming clear: AI is only as powerful as the data that fuels it. Traditional data architectures—rigid, siloed & hard to scale—are no longer fit for purpose in this new era. Enter the era of data products and data fabric—a bold shift in how organizations treat data not as a byproduct, but as a high-value asset engineered for consumption, agility, and impact. Join us as we unpack what this shift means & why CXOs need to rethink data strategies to accelerate AI adoption & enable real-time intelligence to unlock business value.