Leadership in data-driven organizations requires a delicate balance of technical expertise and human understanding. As businesses navigate unprecedented uncertainty in global markets, geopolitics, and technological change, the role of data as a source of truth becomes increasingly vital. But how do you create a culture where data informs decisions at every level? What separates leaders who merely collect data from those who leverage it to drive bold, transformative action? For data professionals looking to advance their careers, the challenge extends beyond technical skills to understanding how data connects to broader business strategy and organizational purpose. Carolyn Dewar is the founder and global co-leader of McKinsey & Company’s CEO Practice, where she partners with CEOs, founders, boards, and senior executives to help them maximize their effectiveness and lead their organizations through critical moments, including hypergrowth, transformation, crises, and mergers. Drawing on her extensive research and experience, Carolyn works with leaders across all stages of the CEO journey to drive large-scale organizational change, set bold strategies, and shape company culture to align leadership teams, manage external stakeholders, and optimize executive time and operating models. She helps CEOs develop the mindsets and frameworks needed to succeed in their role, ensuring they deliver lasting impact and sustainable growth. A recognized thought leader, Carolyn is the co-author of CEO Excellence: The Six Mindsets That Distinguish the Best Leaders from the Rest (a New York Times bestseller) and A CEO for All Seasons: Mastering the Cycles of Leadership. She publishes the monthly Strategic CEO newsletter and has contributed over 30 articles to Harvard Business Review, The Conference Board, and McKinsey Quarterly. Carolyn is also a member of the McKinsey Global Institute Council, which advises on MGI’s research on global economic, business, and technology trends. With over 25 years of experience advising clients across industries, including financial services, technology, and consumer sectors, Carolyn is also a sought-after keynote speaker and panelist at global conferences. In the episode, Richie and Carolyn explore common mistakes for CEOs, the unique responsibilities of a CEO, the importance of data-driven decision-making, fostering a data-centric culture, aligning data and business strategies, and much more. Links Mentioned in the Show: CEO Excellence: The Six Mindsets That Distinguish the Best Leaders from the RestConnect with CarolynSkill Track: Artificial Intelligence (AI) LeadershipRelated Episode: From Panic to Profit, Via Data with Bill Canady, CEO at Arrowhead Engineered ProductsRewatch RADAR AI  New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills witha...

Generative AI enables powerful new capabilities, but they come with some serious limitations that you'll have to tackle to ship a reliable application or agent. Luckily, experts in the field have compiled a library of 32 tried-and-true design patterns to address the challenges you're likely to encounter when building applications using LLMs, such as hallucinations, nondeterministic responses, and knowledge cutoffs. This book codifies research and real-world experience into advice you can incorporate into your projects. Each pattern describes a problem, shows a proven way to solve it with a fully coded example, and discusses trade-offs. Design around the limitations of LLMs Ensure that generated content follows a specific style, tone, or format Maximize creativity while balancing different types of risk Build agents that plan, self-correct, take action, and collaborate with other agents Compose patterns into agentic applications for a variety of use cases

Share your machine learning models, create chatbots, as well as build and deploy insightful dashboards speedily using Taipy with this hands-on book featuring real-world application examples from multiple industries Free with your book: DRM-free PDF version + access to Packt's next-gen Reader Key Features Create visually compelling, interactive data applications with Taipy Bring predictive models to end users and create data pipelines to compare scenarios with what-if analyses Go beyond prototypes to build and deploy production-ready applications using the cloud provider of your choice Purchase of the print or Kindle book includes a free PDF eBook in full color Book Description While data analysts, data scientists, and BI experts have the tools to analyze data, build models, and create compelling visuals, they often struggle to translate these insights into practical, user-friendly applications that help end users answer real-world questions, such as identifying revenue trends, predicting inventory needs, or detecting fraud, without wading through complex code. This book is a comprehensive guide to overcoming this challenge. This book teaches you how to use Taipy, a powerful open-source Python library, to build intuitive, production-ready data apps quickly and efficiently. Instead of creating prototypes that nobody uses, you'll learn how to build faster applications that process large amounts of data for multiple users and deliver measurable business impact. Taipy does the heavy lifting to enable your users to visualize their KPIs, interact with charts and maps, and compare scenarios for better decision-making. You’ll learn to use Taipy to build apps that make your data accessible and actionable in production environments like the cloud or Docker. By the end of this book, you won’t just understand Taipy, you'll be able to transform your data skills into impactful solutions that address real-world needs and deliver valuable insights. Email sign-up and proof of purchase required What you will learn Explore Taipy, its use cases, and how it's different from other projects Discover how to create visually appealing interactive apps, display KPIs, charts, and maps Understand how to compare scenarios to make better decisions Connect Taipy applications to several data sources and services Develop apps for diverse use cases, including chatbots, dashboards, ML apps, and maps Deploy Taipy applications on different types of servers and services Master advanced concepts for simplifying and accelerating your development workflow Who this book is for If you’re a data analyst, data scientist, or BI analyst looking to build production-ready data apps entirely in Python, this book is for you. If your scripts and models sit idle because non-technical stakeholders can’t use them, this book shows you how to turn them into full applications fast with Taipy, so your work delivers real business value. It’s also valuable for developers and engineers who want to streamline their data workflows and build UIs in pure Python.

Summary In this episode of the Data Engineering Podcast Vijay Subramanian, founder and CEO of Trace, talks about metric trees - a new approach to data modeling that directly captures a company's business model. Vijay shares insights from his decade-long experience building data practices at Rent the Runway and explains how the modern data stack has led to a proliferation of dashboards without a coherent way for business consumers to reason about cause, effect, and action. He explores how metric trees differ from and interoperate with other data modeling approaches, serve as a backend for analytical workflows, and provide concrete examples like modeling Uber's revenue drivers and customer journeys. Vijay also discusses the potential of AI agents operating on metric trees to execute workflows, organizational patterns for defining inputs and outputs with business teams, and a vision for analytics that becomes invisible infrastructure embedded in everyday decisions.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData teams everywhere face the same problem: they're forcing ML models, streaming data, and real-time processing through orchestration tools built for simple ETL. The result? Inflexible infrastructure that can't adapt to different workloads. That's why Cash App and Cisco rely on Prefect. Cash App's fraud detection team got what they needed - flexible compute options, isolated environments for custom packages, and seamless data exchange between workflows. Each model runs on the right infrastructure, whether that's high-memory machines or distributed compute. Orchestration is the foundation that determines whether your data team ships or struggles. ETL, ML model training, AI Engineering, Streaming - Prefect runs it all from ingestion to activation in one platform. Whoop and 1Password also trust Prefect for their data operations. If these industry leaders use Prefect for critical workflows, see what it can do for you at dataengineeringpodcast.com/prefect.Data 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.Your host is Tobias Macey and today I'm interviewing Vijay Subramanian about metric trees and how they empower more effective and adaptive analyticsInterview IntroductionHow did you get involved in the area of data management?Can you describe what metric trees are and their purpose?How do metric trees relate to metric/semantic layers?What are the shortcomings of existing data modeling frameworks that prevent effective use of those assets?How do metric trees build on top of existing investments in dimensional data models?What are some strategies for engaging with the business to identify metrics and their relationships?What are your recommendations for storage, representation, and retrieval of metric trees?How do metric trees fit into the overall lifecycle of organizational data workflows?When creating any new data asset it introduces overhead of maintenance, monitoring, and evolution. How do metric trees fit into existing testing and validation frameworks that teams rely on for dimensional modeling?What are some of the key differences in useful evaluation/testing that teams need to develop for metric trees?How do metric trees assist in context engineering for AI-powered self-serve access to organizational data?What are the most interesting, innovative, or unexpected ways that you have seen metric trees used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on metric trees and operationalizing them at Trace?When is a metric tree the wrong abstraction?What do you have planned for the future of Trace and applications of metric trees?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 Metric TreeTraceModern Data StackHadoopVerticaLuigidbtRalph KimballBill InmonMetric LayerDimensional Data WarehouseMaster Data ManagementData GovernanceFinancial P&L (Profit and Loss)EBITDA ==Earnings before interest, taxes, depreciation and amortizationThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

The US shutdown leaves us with limited visibility at an important juncture in the global outlook. There are reasons to believe that the factors of lift are set to fade and that the factors of drag are intensifying. These are just narratives for now, but keep the risks for a bend-but-not break global outlook skewed to the downside and the Fed in insurance easing mode.

Speakers:

Bruce Kasman

Joseph Lupton

This podcast was recorded on 3 October 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.

Você já pensou em como a Inteligência Artificial generativa está transformando o jeito que grandes empresas criam produtos digitais? Neste episódio, conversamos com o time do Grupo Boticário para entender como a companhia está unindo tecnologia e inovação para transformar o futuro da beleza. Exploramos como a GenAI vem impulsionando o desenvolvimento de produtos digitais e potencializando o trabalho de analistas, times de produto e engenharia com ferramentas. Falamos sobre os bastidores da Semana de IA GB, os aprendizados que ela trouxe para o negócio e como a GenAI está ajudando os times a ganharem eficiência e profundidade nas análises. Se você quer entender como uma das maiores empresas de beleza do país está moldando sua cultura de produto e engenharia para o futuro, esse episódio é para você! Lembrando que você pode encontrar todos os podcasts da comunidade Data Hackers no Spotify, iTunes, Google Podcast, Castbox e muitas outras plataformas. Convidados: Bruno Fuzetti Penso - Gerente Sênior de Plataforma Thayana Borba - Gerente Sênior de Produtos Digitais João Alves De Oliveira Neto - Gerente Sênior Produtos de Dados Nossa Bancada Data Hackers: Paulo Vasconcellos — Co-founder da Data Hackers e Principal Data Scientist na Hotmart. Monique Femme — Head of Community Management na Data Hackers Canais do Grupo Boticário: LinkedIn do GB Página de vagas do GB Instagram do GB Referências: Plataforma de Desenvolvimento (Alquimia) https://github.com/customer-stories/grupoboticario https://medium.com/gbtech/plataforma-do-desenvolvimento-grupo-botic%C3%A1rio-61b1aaddbc9b https://medium.com/gbtech/opentelemetry-na-nova-plataforma-de-integra%C3%A7%C3%A3o-350e744b6a5f https://aws.amazon.com/pt/solutions/case-studies/grupo-boticario-summit/

It's all about acquisitions, acquisitions, acquisitions! Matt Housley joins me to tackle the biggest rumor in the data world this week: the potential acquisition of dbt Labs by Fivetran. This news sparks a wide-ranging discussion on the inevitable consolidation of the Modern Data Stack, a trend we predicted as the era of zero-interest-rate policy ended. We also talk about financial pressures, vendor exposure to the rise of AI, the future of data tooling, and more.

From launching AI products to modernizing legacy data stacks, we're going behind the scenes of data-driven transformation in financial remittance. In this episode, we sit down with Sia Zahedi, former CDO at a global financial remittance company, to get a candid look at the projects, challenges, and decisions that define data leadership in finance. If you've ever wondered what it's like to lead data strategy at a global financial company, this one is for you. What You'll Learn: What the day-to-day of a CDO looks like Real-world use cases for AI in financial services The difference between launching AI prototypes and real products Career advice for aspiring CDOs and senior data leaders   🤝 Follow Sia on LinkedIn!   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

Panel discussion on intelligent software infrastructure design decisions in an AI era.

• Approche & Implémentation 

 Déploiement de la plateforme StellAI via une méthode agile et collaborative, intégrée aux processus métier. 

• ROI & Chiffres clés 

 Amélioration de la satisfaction client, réduction des coûts opérationnels, accélération du time-to-market. 

• Valeur & Perspectives 

 Renforcement de la compétitivité, extension à de nouveaux domaines, montée en compétence IA à l’échelle. 

• Nous présenterons les enjeux du déploiement d’une IA générative et d’un pipeline de RAG dans un environnement industriel sensible, en garantissant la plus stricte confidentialité des données.

• Découvrez l’approche innovante choisie par Sodern : déploiement on-premise de la plateforme LightOn, vectorisation de la base documentaire et intégration des fonctionnalités via API.

• Nous expliquerons comment ces technologies sont utilisées concrètement pour la génération et la correction de code, la création de user stories et le knowledge management, avec les premiers résultats observés.

• Enfin, nous partagerons la valeur ajoutée apportée à l’organisation et les axes d’évolution envisagés pour renforcer l’usage de l’IA sécurisée dans l’industrie.

IA générative accélère le code, tests et docs. Mais qu’est-ce qui reste essentiel dans l’acte de programmer ?