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.

Patrick Thompson, co-founder of Clarify and former co-founder of Iteratively (acquired by Amplitude), joined Yuliia and Dumky to discuss the evolution from data quality to decision quality. Patrick shares his experience building data contracts solutions at Atlassian and later developing analytics tracking tools. Patrick challenges the assumption that AI will eliminate the need for structured data. He argues that while LLMs excel at understanding unstructured data, businesses still need deterministic systems for automation and decision-making. Patrick shares insights on why enforcing data quality at the source remains critical, even in an AI-first world, and explains his shift from analytics to CRM while maintaining focus on customer data unification and business impact over technical perfectionism.Tune in!

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.

The Bank of India is redefining trust through the power of data. Join its Analytics Head as they share how AI, real-time analytics, and predictive insights are transforming security, transparency, and customer experience. Discover how a legacy institution is embracing agility to lead the future of banking.
In addition - see how organizations unlock value with SAS Viya — achieving over 100x performance gains and half the cost in compute and storage costs when modernizing SAS 9 environments. It will explore how Intelligent Decisioning and Generative AI are integrated with data and models to automate decisions and drive stronger business outcomes.

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.

D&A leaders and teams are at an AI-driven inflection point. They are crucial to enable business value through AI-readiness but must evolve their strategy and operating model to meet their organization’s AI ambitions. Reporting from the Gartner 2025 CDAO Agenda Survey, this session reveals actions successful D&A leaders have taken to deliver business outcomes from AI, data, and analytics, and what they need to do next year.

Moving AI projects from pilot to production requires substantial effort for most enterprises. AI Engineering provides the foundation for enterprise delivery of AI and generative AI solutions at scale unifying DataOps, MLOps and DevOps practices. This session will highlight AI engineering best practices across these dimensions covering people, processes and technology.

A global Pharma giant missed a $30M market opportunity and faced FDA sanctions when siloed systems delayed critical product insights leading to poor production and supply chain execution.
Manual handovers and fragmented workflows broke down at the worst moment leading to missed deadlines and demand spike windows.
Agentic AI autonomously connects, predicts, recommends and acts to course-correct in real time.
Discover how businesses can win big with AI acting in business process not just advising!

With high D&A ambitions and AI pilots becoming increasingly ubiquitous, focus is shifting toward consistent execution. We will share how to continue innovating and experimenting while scaling success. In this keynote, Gartner’s perspective will guide you on preparing for the expected and adapting to the unexpected. Learn to enhance your D&A and AI capabilities on the path to success.

Bruce Kasman is joined by Joe Lupton to discuss their view that hopes of a court-led off-ramp to the US war on trade are overstated. They maintain that the risks are skewed toward higher, not lower, tariffs. However, the trajectory of growth is complicated by prior front-loading and there is debate about how to track resilience. Weakness could presage recession, muddle-through, or rebound. The latter scenario risks Fed cuts only to be followed by hikes.

This podcast was recorded on May 30, 2025.

This communication is provided for information purposes only. Institutional clients please visit www.jpmm.com/research/disclosures for important disclosures. © 2025 JPMorgan Chase & Co. All rights reserved. This material or any portion hereof may not be reprinted, sold or redistributed without the written consent of J.P. Morgan. It is strictly prohibited to use or share without prior written consent from J.P. Morgan any research material received from J.P. Morgan or an authorized third-party (“J.P. Morgan Data”) in any third-party artificial intelligence (“AI”) systems or models when such J.P. Morgan Data is accessible by a third-party. It is permissible to use J.P. Morgan Data for internal business purposes only in an AI system or model that protects the confidentiality of J.P. Morgan Data so as to prevent any and all access to or use of such J.P. Morgan Data by any third-party.

Nos últimos tempos, se você é dev, tech lead ou faz parte de algum squad de desenvolvimento, é impossível não ter sido impactado pela enxurrada de ferramentas de IA voltadas para desenvolvimento de software. Mas… o que de fato mudou na prática? Será que a AI está mesmo revolucionando o jeito de programar — ou estamos só vivendo mais a hype do Vibe Coding ? Nesse episódio, chamamos Caio Gomes - Chief AI Officer & Chief Data Officer @ Magalu e Wallysson Nunes - Staff Frontend Engineer @ Hotmartpra discutir tudo sobre como a inteligência artificial está moldando o presente e o futuro da engenharia de software. Lembrando que você pode encontrar todos os podcasts da comunidade Data Hackers no Spotify, iTunes, Google Podcast, Castbox e muitas outras plataformas. Falamos no episódio: Caio Gomes - Chief AI Officer & Chief Data Officer @ Magalu Wallysson Nunes - Staff Frontend Engineer @ Hotmart Nossa Bancada — Data Hackers: Gabriel Lages - Data Hackers  Paulo Vasconcellos - Data Hackers Referências: 🛠️ Ferramenta de Crawler → Firecrawl 🛠️ Ferramenta de Crawler → Jina AI 🤖 Ferramenta de Agentes de AI → Manus 👨‍🏫 Criador do termo Vibe Coding → Andrej Karpathy 🔥 Lovable → Site Oficial 🔥 Cursor → Site Oficial 🔥 Windsurf → Site Oficial 🔥 GitHub Copilot → Site Oficial 💀 Stack Overflow está praticamente morto — Pragmatic Engineer 🔓 Hacker mostra como quebrou os 10 maiores sites feitos no Lovable em 47 minutos

What happens when a passion for data science meets the fast-paced world of stock trading? In this episode, we're joined by Matt Dancho, Founder of Business Science, Quant Science, and the creator of the popular tidyquant package, who shares his journey from data scientist to launching Business Science and the projects and packages he's built along the way. We explore how he leverages Python and R to trade stocks, as well as the lessons he's learned from building a business in the data. Whether you're a data professional, a stock market enthusiast, or an aspiring entrepreneur, this episode is packed with actionable insights to level up your skills and strategies. What You'll Learn: Taking your data skills and furthering your career to make money. Tips for working with stock data. How AI is changing analytics   Register for free to be part of the next live session: https://bit.ly/3XB3A8b   Follow us on Socials: LinkedIn YouTube Instagram (Mavens of Data) Instagram (Maven Analytics) TikTok Facebook Medium X/Twitter

In "Mathematics of Machine Learning," you will explore the foundational mathematics essential for understanding and advancing in machine learning. The book covers linear algebra, calculus, and probability theory, offering readers clear explanations and practical Python-based implementations. What this Book will help me do Master fundamental linear algebra concepts such as matrices, eigenvalues, and vector spaces. Understand and apply principles of calculus, including multivariable functions and optimization. Gain confidence in utilizing probability theory concepts like Bayes' theorem and random distributions. Learn to implement mathematical concepts in Python to solve machine learning problems. Bridge the gap between theoretical mathematics and the practical demands of modern machine learning. Author(s) Tivadar Danka is a PhD mathematician with a specialized focus on machine learning applications. Known for his clear and engaging teaching style, Tivadar has a deep understanding of both mathematical rigor and practical ML challenges. His ability to break down complex ideas into comprehensible concepts has helped him reach thousands of learners globally. Who is it for? The book is perfect for data scientists, aspiring machine learning engineers, software developers working with ML, and researchers interested in advanced ML methodologies. If you have a basic understanding of algebra and Python programming, alongside some familiarity with machine learning concepts, this book will help you deepen your mathematical insight and elevate your practical applications.

Behavioural data is fast becoming a cornerstone of modern business strategy. Not just for media measurement or advertising optimisation, but across product, pricing, logistics, and platform development. It tells us what people actually do, not just what they say they do. As traditional market research struggles with low engagement and recall bias, brands are turning to digital behavioural data to make sharper, faster decisions. Whether it's tracking consumer journeys in the app economy or identifying early adoption trends (like the impact of AI tools on category disruption), the value lies in real, observable behaviour at scale. But, that shift raises new questions around data ownership, consent, and fairness. And, the rise of AI is only accelerating both the opportunity and the complexity. In the latest episode of Hub & Spoken, Jason Foster, CEO & Founder of Cynozure, speaks to Chris Havemann, CEO of RealityMine, and discusses everything from: The transition from survey-based research to behavioural data analysis The impact of AI on interpreting digital interactions Ethical considerations surrounding data consent and transparency Building trust through clear data collection and usage practices Learn from Chris's 25+ years in data and insight, and explore how behavioural signals are reshaping everything from media to market intelligence. ****    Cynozure is a leading data, analytics and AI company that helps organisations to reach their data potential. It works with clients on data and AI strategy, data management, data architecture and engineering, analytics and AI, data culture and literacy, and data leadership. The company was named one of The Sunday Times' fastest-growing private companies in both 2022 and 2023 and recognised as The Best Place to Work in Data by DataIQ in 2023 and 2024. Cynozure is a certified B Corporation. 

Summary In this episode of the Data Engineering Podcast Chakravarthy Kotaru talks about scaling data operations through standardized platform offerings. From his roots as an Oracle developer to leading the data platform at a major online travel company, Chakravarthy shares insights on managing diverse database technologies and providing databases as a service to streamline operations. He explains how his team has transitioned from DevOps to a platform engineering approach, centralizing expertise and automating repetitive tasks with AWS Service Catalog. Join them as they discuss the challenges of migrating legacy systems, integrating AI and ML for automation, and the importance of organizational buy-in in driving data platform success.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.This is a pharmaceutical Ad for Soda Data Quality. Do you suffer from chronic dashboard distrust? Are broken pipelines and silent schema changes wreaking havoc on your analytics? You may be experiencing symptoms of Undiagnosed Data Quality Syndrome — also known as UDQS. Ask your data team about Soda. With Soda Metrics Observability, you can track the health of your KPIs and metrics across the business — automatically detecting anomalies before your CEO does. It’s 70% more accurate than industry benchmarks, and the fastest in the category, analyzing 1.1 billion rows in just 64 seconds. And with Collaborative Data Contracts, engineers and business can finally agree on what “done” looks like — so you can stop fighting over column names, and start trusting your data again.Whether you’re a data engineer, analytics lead, or just someone who cries when a dashboard flatlines, Soda may be right for you. Side effects of implementing Soda may include: Increased trust in your metrics, reduced late-night Slack emergencies, spontaneous high-fives across departments, fewer meetings and less back-and-forth with business stakeholders, and in rare cases, a newfound love of data. Sign up today to get a chance to win a $1000+ custom mechanical keyboard. Visit dataengineeringpodcast.com/soda to sign up and follow Soda’s launch week. It starts June 9th.Your host is Tobias Macey and today I'm interviewing Chakri Kotaru about scaling successful data operations through standardized platform offeringsInterview IntroductionHow did you get involved in the area of data management?Can you start by outlining the different ways that you have seen teams you work with fail due to lack of structure and opinionated design?Why NoSQL?Pairing different styles of NoSQL for different problemsUseful patterns for each NoSQL style (document, column family, graph, etc.)Challenges in platform automation and scaling edge casesWhat challenges do you anticipate as a result of the new pressures as a result of AI applications?What are the most interesting, innovative, or unexpected ways that you have seen platform engineering practices applied to data systems?What are the most interesting, unexpected, or challenging lessons that you have learned while working on data platform engineering?When is NoSQL the wrong choice?What do you have planned for the future of platform principles for enabling data teams/data applications?Contact Info LinkedInParting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links RiakDynamoDBSQL ServerCassandraScyllaDBCAP TheoremTerraformAWS Service CatalogBlog PostThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA