Data and analytics leaders expect their data and analytics investments to deliver business results. But unless they address data, analytics and AI risk, their initiatives will fail, leading to higher business risk exposure. This session will help D&A leaders target three key areas where better data and analytics risk practices will yield better business results.

Come together for a discussion with peers to discuss your GenAI early wins and challenges. Peer Meetups are networking sessions that allow you to connect and share with a small group of your peers, without Gartner facilitation. Please make every effort to attend your Peer Meetup as other attendees will be looking forward to meeting with you.

Come together for a discussion with peers to discuss best practices and challenges around building your talent pipeline. Peer Meetups are networking sessions that allow you to connect and share with a small group of your peers, without Gartner facilitation. Please make every effort to attend your Peer Meetup as other attendees will be looking forward to meeting with you.

Join us for this roundtable, tailored for Black and Brown technology leaders and open to all attendees, to explore the impact of GenAI on the workforce and underrepresented talent. As AI continues to shape the future of work, the high quality of diverse thought and talent holds both the promise of inclusivity and the hope of overcoming perpetuating biases. Together we’ll discuss how AI can be a double-edged sword — potentially amplifying existing inequalities and amplifying how diverse talent is highly qualified talent overall

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.

More than 50% of generative AI projects fail. This session will help data & analytics leaders learn the common causes of failures and understand best practices to mitigate those failures and scale AI across the enterprise.

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.

Data and analytics leaders can struggle to translate and assess the necessary investment and return value of D&A initiatives in the public sector. In this session, participants will learn frameworks for public sector value determination and communication and then build out value stories for their initiatives.

Lloyds Banking Group is on a bold mission to become the UK’s biggest FinTech. As the bank transforms, trusted data is key to driving innovation, compliance, and better customer outcomes. In this session, Kiran Bal, Head of Data Quality, shares how her team is embedding quality, ownership, and governance across the enterprise. Learn how they’re aligning with the CFO and business leadership through actionable metrics, stakeholder engagement, and a federated approach to trusted data at scale.

Data is the fastest growing asset for every enterprise, and traditional rule-based systems are not delivering intelligent, context-aware security that adapts at speed to evolving threats while reducing operational overhead. Join Ash Hunt to explore how enterprises are revolutionizing data security through the transformative power of artificial intelligence. Drawing from extensive experience protecting Fortune 500 companies, Ash will share how AI-native solutions are powering real-time threat detection and automated risk remediation across complex cloud environments.

Data leaders face a dual challenge: handling the explosive growth of data while managing costs — and they have to do both without compromising performance. In this session, we explore strategies to minimize data integration TCO and measure the performance of your pipelines. Hear how Saint Gobain achieved a 40% cost reduction and 250% productivity boost with automated data integration.

Data leaders will learn about:
Modern data architectures for increased efficiency
High-performance pipelines to move business-critical data from SAP
Hidden compute costs and how to reduce them for improved ROI

In the face of siloed systems and fragmented processes, Helvetia, a leading international insurance group, sought to increase efficiency and innovation. Discover how improving the integrity of location data, centralising it on a scalable platform, and automating workflows led to significant business transformation. With streamlined spatial operations integrated seamlessly with pricing and underwriting, Helvetia eliminated manual work, accelerated regulatory response, revealed new cross-sell opportunities, and enabled greater AI relevance and accuracy.

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.

AI-powered Data Engineering Agents usher in a new era of data agility. Engage with Google Cloud and your peers to explore the implementation of autonomous data agents and their impact on enterprise agility. From automating data pipelines to ingestion to transformation, discover how to leverage autonomous data agents to build self-managing data ecosystems and accelerate the time from raw data to impactful decisions. This is where data's potential truly meets AI power.

Enter the agentic era of data and analytics with Tableau and Agentforce. Discover how AI agents are accelerating data modeling and unlocking conversational analytics. Hear how leading organizations are harnessing agents to reimagine decision-making, supercharge insight delivery, and unleash the full potential of their data-driven workforce.

To achieve success, it’s essential to establish effective governance, standardise enterprise practices, and balance overall goals with the needs of individual business units. Governance provides a structured framework for analytics and decision-making, while standardisation enhances efficiency by establishing common practices. Together, these elements promote a culture of transparency and accountability, enabling organizations to adapt to change and drive sustainable growth.
In this session, I’ll share how Elsevier’s well-executed Master Data Management strategy helped us strike that balance.

Join us for an insightful session where biopharmaceutical leader Pfizer shares valuable lessons learned during the development of their global SMART application suite, which empowers representatives to deliver more patient-centric care. With a mission to create breakthroughs that change patients’ lives, the SMART application suite supports over 20,000 pharmaceutical sales employees across dozens of countries. Deploying at this scale required not just advanced technology, but also transformative ways of working. The result? A threefold increase in the adoption of previous solutions.

Rapid changes demand innovative decision-making tools beyond traditional methods. Businesses are turning to AI, BI, and data science to gain a competitive edge. The perfect blend of these technologies can be a true differentiator.

- Take a quick look at what to expect from this session
- Challenges in data and analytics today
- Unlocking the power of AI, BI, and data science
- The transformative role of AI-powered self-service BI platforms
- Live demos of next-generation analytics in action

Learn how these innovations can drive better decisions to deliver transformative business outcomes.

Whether you are new in your role or a seasoned veteran, you’re likely being asked how to extract more value from your analytics investment. In this session, we will review how to embed AI and conversational analytics into the flow of users’ daily lives to drive adoption and improve overall customer end-user experience. Putting the power of true self-service into the hands of all users, both internal and external, has never been easier and is now a requirement more than ever.

Data and AI are shifting industries and reshaping society, but with great power comes the need for robust governance. In this session, we will focus on governance in data and AI, highlighting why it is a strategic priority for organizations worldwide.

Join this session to discover how trusted AI governance with SAS Viya ensures transparency, ethics, and performance—without slowing innovation. Walk away with real-world strategies to keep your AI trustworthy, efficient, and impactful. Don’t let bad AI decisions cost you.

AI architects are challenged to define a technology roadmap in an environment that is currently defined by a rapid pace of AI technology, specifically with generative AI initiatives, evolving product capabilities, needs for up-skilling, all of this with the goal of improving business outcomes. This session will provide the framework, architectures and tools to define such a roadmap.

Traditional approaches and thinking around data quality are out of date and not sufficient in the era of AI. Data, analytics and AI leaders will need to reconsider their approach to data quality going beyond the traditional six data quality dimensions. This session will help data leaders learn to think about data quality in a holistic way that support making data AI-ready.

Metadata, data quality and data observability tools provide significant capabilities to ensure good data for your BI and AI initiatives. Metadata tools help discover, and inventory your data assets. Data quality tools help business users manage their data at sources by setting rules and policies. Data observability tools give organizations integrated visibility over the health of data, data pipeline and data landscape. Together the tools help organizations lay good foundation in data management for BI and AI initiatives.

To position their teams as a must-have discipline that can fulfill cross-functional use cases, D&A leaders must optimize their organizational model. The optimal organizational model is one that balances enterprisewide capabilities focused on enablement with decentralized needs focused on outcomes. In this session you will learn the design principles behind the right D&A organizational model and how to balance a centralized team (focused on enablement) and decentralized needs (focused on outcomes).