Take part in the new SOC Experience – a view into real-world attack scenarios. Learn about the latest hacker tactics and how Google equips cybersecurity teams with the data, AI, and scalable analytics to detect and remediate attacks.

Learn how Dun & Bradstreet, a leading provider of business decisioning data and analytics, supercharged their business with Google Cloud. You’ll learn how Dun & Bradstreet achieved a 95% decrease in data onboarding time and a 50% reduction in data latency, and how they unlocked new revenue streams with AI. Learn how they built an enterprise-grade data cloud, and new and innovative AI solutions. Join us to find out how your organization can leverage Dun & Bradstreet’s experience and learnings to achieve similar success in your own data analytics and AI journey.

Unlock the potential of AI with high-performance, scalable lakehouses using BigQuery and Apache Iceberg. This session details how BigQuery leverages Google's infrastructure to supercharge Iceberg, delivering peak performance and resilience. Discover BigQuery's unified read/write path for rapid queries, superior storage management beyond simple compaction, and robust, high-throughput streaming pipelines. Learn how Spotify utilizes BigQuery's lakehouse architecture for a unified data source, driving analytics and AI innovation.

Discover how AI-driven personalization and agentic architecture are helping revolutionize customer experiences and operations in financial services. In this insightful panel discussion with leadership from USAA and Deloitte, we'll explore the next generation of AI-enabled end-to-end data value chains and highlight some top trends that are reshaping today's technology organizations.

This Session is hosted by a Google Cloud Next Sponsor.
Visit your registration profile at g.co/cloudnext to opt out of sharing your contact information with the sponsor hosting this session.

Breitbart’s Economic and Finance Editor, John Carney, and the Urban Institute’s Jim Parrott return to Inside Economics to discuss the motivations and endgame of President Trump’s global trade war, tax and spending policy, and what will happen with Fannie and Freddie. The upshot of the conversation: the trade war isn’t going to end soon, and a recession is dead-ahead. Guests: John Carney, Finance and Economics Editor at Breitbart & Jim Parrott, Nonresident Fellow at the Urban Institute   Hosts: Mark Zandi – Chief Economist, Moody’s Analytics, Cris deRitis – Deputy Chief Economist, Moody’s Analytics, Marisa DiNatale – Senior Director - Head of Global Forecasting, Moody’s Analytics Follow Mark Zandi on 'X', BlueSky or LinkedIn @MarkZandi, Cris deRitis on LinkedIn, and Marisa DiNatale on LinkedIn

Questions or Comments, please email us at [email protected]. We would love to hear from you.    To stay informed and follow the insights of Moody's Analytics economists, visit Economic View.

Cindy Clifford, a seasoned educator of 25 years, refused to let age or past career define her. She used her skills honed as a teacher and pivoted to data analytics! If you feel you're too old to pivot and become a data analyst, it's never too late-- dive into Cindy's story. 💌 Join 10k+ aspiring data analysts & get my tips in your inbox weekly 👉 https://www.datacareerjumpstart.com/newsletter 🆘 Feeling stuck in your data journey? Come to my next free "How to Land Your First Data Job" training 👉 https://www.datacareerjumpstart.com/training 👩‍💻 Want to land a data job in less than 90 days? 👉 https://www.datacareerjumpstart.com/daa 👔 Ace The Interview with Confidence 👉 https://www.datacareerjumpstart.com/interviewsimulator ⌚ TIMESTAMPS 00:00 - Introduction 01:26 - Burnout with teaching. 11:34 - Cindy's first data role. 13:04 - FindADataJob.com and PremiumDataJobs.com. 19:14 - Cindy's second data job. 30:10 - Advice for teachers who want to become a data analyst. 🔗 CONNECT WITH CINDY 🤝 LinkedIn: https://www.linkedin.com/in/cynthia-a-clifford/ 🔗 CONNECT WITH AVERY 🎥 YouTube Channel: https://www.youtube.com/@averysmith 🤝 LinkedIn: https://www.linkedin.com/in/averyjsmith/ 📸 Instagram: https://instagram.com/datacareerjumpstart 🎵 TikTok: https://www.tiktok.com/@verydata 💻 Website: https://www.datacareerjumpstart.com/ Mentioned in this episode: Join the last cohort of 2025! The LAST cohort of The Data Analytics Accelerator for 2025 kicks off on Monday, December 8th and enrollment is officially open!

To celebrate the end of the year, we’re running a special End-of-Year Sale, where you’ll get: ✅ A discount on your enrollment 🎁 6 bonus gifts, including job listings, interview prep, AI tools + more

If your goal is to land a data job in 2026, this is your chance to get ahead of the competition and start strong.

👉 Join the December Cohort & Claim Your Bonuses: https://DataCareerJumpstart.com/daa https://www.datacareerjumpstart.com/daa

The Moody's Analytics team discusses President Trump's trade war.   Hosts: Mark Zandi – Chief Economist, Moody’s Analytics, Cris deRitis – Deputy Chief Economist, Moody’s Analytics, and Marisa DiNatale – Senior Director - Head of Global Forecasting, Moody’s Analytics Follow Mark Zandi on 'X', BlueSky or LinkedIn @MarkZandi, Cris deRitis on LinkedIn, and Marisa DiNatale on LinkedIn

Questions or Comments, please email us at [email protected]. We would love to hear from you.    To stay informed and follow the insights of Moody's Analytics economists, visit Economic View.

In this podcast episode, we talked with Eddy Zulkifly about From Supply Chain Management to Digital Warehousing and FinOps

About the Speaker: Eddy Zulkifly is a Staff Data Engineer at Kinaxis, building robust data platforms across Google Cloud, Azure, and AWS. With a decade of experience in data, he actively shares his expertise as a Mentor on ADPList and Teaching Assistant at Uplimit. Previously, he was a Senior Data Engineer at Home Depot, specializing in e-commerce and supply chain analytics. Currently pursuing a Master’s in Analytics at the Georgia Institute of Technology, Eddy is also passionate about open-source data projects and enjoys watching/exploring the analytics behind the Fantasy Premier League.

In this episode, we dive into the world of data engineering and FinOps with Eddy Zulkifly, Staff Data Engineer at Kinaxis. Eddy shares his unconventional career journey—from optimizing physical warehouses with Excel to building digital data platforms in the cloud.

🕒 TIMECODES 0:00 Eddy’s career journey: From supply chain to data engineering 8:18 Tools & learning: Excel, Docker, and transitioning to data engineering 21:57 Physical vs. digital warehousing: Analogies and key differences 31:40 Introduction to FinOps: Cloud cost optimization and vendor negotiations 40:18 Resources for FinOps: Certifications and the FinOps Foundation 45:12 Standardizing cloud cost reporting across AWS/GCP/Azure 50:04 Eddy’s master’s degree and closing thoughts

🔗 CONNECT WITH EDDY Twitter - https://x.com/eddarief Linkedin - https://www.linkedin.com/in/eddyzulkifly/ Github: https://github.com/eyzyly/eyzyly ADPList: https://adplist.org/mentors/eddy-zulkifly

🔗 CONNECT WITH DataTalksClub Join the community - https://datatalks.club/slack.html Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ

Check other upcoming events - https://lu.ma/dtc-events LinkedIn - https://www.linkedin.com/company/datatalks-club/ Twitter - https://twitter.com/DataTalksClub Website - https://datatalks.club/

If you want to elevate your data career, mastering communication is key…and to do that, you do not want to miss this show about the people side of analytics! Tiankai Feng, Head of Data Strategy & Governance at Thoughtworks, dives deep into language's critical role in how data analysts work with business stakeholders—and why getting it right matters. You'll leave the show with practical tips for bridging the gap between technical and non-technical audiences and actionable insights to ensure your data drives impact, not confusion. Sharpen your ability to speak the language of business and create stronger partnerships with your stakeholders! What You'll Learn: How to translate technical insights into business-friendly language Common mistakes to avoid when working with non-technical teams Strategies to build trust and alignment through clear communication How mastering this skill can boost your career   Register for free to be part of the next live session: https://bit.ly/3XB3A8b   Interested in learning more from Tiankai? Check out his book, Humanizing Data Strategy: Leading Data with the Head and the Heart, and his YouTube channel, where he shares original songs about data!   Follow us on Socials: LinkedIn YouTube Instagram (Mavens of Data) Instagram (Maven Analytics) TikTok Facebook Medium X/Twitter

In this episode of Hub & Spoken, host Jason Foster welcomes Sam White, the multi-award-winning Founder of Freedom Services Group and Global Founder of Stella Insurance Australia.  Sam shares her journey of building Stella Insurance, the challenges and opportunities of creating a digital-first insurance company, the importance of customer experience, and how Stella Insurance is reimagining financial services from a female perspective. Sam also discusses the impact of regulatory changes, the role of AI in the insurance industry, and the significance of diversity in business. This is a real gem of an episode, especially for entrepreneurs and business leaders interested in digital transformation, insurance innovations, and diversity in leadership.  Follow Sam: linkedin.com/in/samwhiteentrepreneur/ Follow Jason: linkedin.com/in/jasonbfoster/ *****    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. 

Shift Left with Apache Iceberg Data Products to Power AI | Andrew Madson | Shift Left Data Conference 2025

High-quality, governed, and performant data from the outset is vital for agile, trustworthy enterprise AI systems. Traditional approaches delay addressing data quality and governance, causing inefficiencies and rework. Apache Iceberg, a modern table format for data lakes, empowers organizations to "Shift Left" by integrating data management best practices earlier in the pipeline to enable successful AI systems.

This session covers how Iceberg's schema evolution, time travel, ACID transactions, and Git-like data branching allow teams to validate, version, and optimize data at its source. Attendees will learn to create resilient, reusable data assets, streamline engineering workflows, enforce governance efficiently, and reduce late-stage transformations—accelerating analytics, machine learning, and AI initiatives.

Jen Hawkins went from delivering pizzas to becoming a six-figure data analyst at a FAANG company in just 17 weeks. In our chat, she shares her Data Accelerator Program journey, how she used her background and new skills to stay motivated, land job offers, and eventually achieve her dream role. 💌 Join 10k+ aspiring data analysts & get my tips in your inbox weekly 👉 https://www.datacareerjumpstart.com/newsletter 🆘 Feeling stuck in your data journey? Come to my next free "How to Land Your First Data Job" training 👉 https://www.datacareerjumpstart.com/training 👩‍💻 Want to land a data job in less than 90 days? 👉 https://www.datacareerjumpstart.com/daa 👔 Ace The Interview with Confidence 👉 https://www.datacareerjumpstart.com/interviewsimulator Jen Hawkins' Confessions of an Accidental Delivery Driver: Tableau Supply Chain Project: ⌚ TIMESTAMPS 00:00 - Introduction 00:30 - The Struggles and Turning Points 07:49 - Transitioning to a Data Analyst Role 19:46 - Life as a Data Analyst at a FAANG Company 🔗 CONNECT WITH JEN: 🤝 LinkedIn: https://www.linkedin.com/in/jeandriska/ 🔗 CONNECT WITH AVERY 🎥 YouTube Channel: https://www.youtube.com/@averysmith 🤝 LinkedIn: https://www.linkedin.com/in/averyjsmith/ 📸 Instagram: https://instagram.com/datacareerjumpstart 🎵 TikTok: https://www.tiktok.com/@verydata 💻 Website: https://www.datacareerjumpstart.com/ Mentioned in this episode: Join the last cohort of 2025! The LAST cohort of The Data Analytics Accelerator for 2025 kicks off on Monday, December 8th and enrollment is officially open!

To celebrate the end of the year, we’re running a special End-of-Year Sale, where you’ll get: ✅ A discount on your enrollment 🎁 6 bonus gifts, including job listings, interview prep, AI tools + more

If your goal is to land a data job in 2026, this is your chance to get ahead of the competition and start strong.

👉 Join the December Cohort & Claim Your Bonuses: https://DataCareerJumpstart.com/daa https://www.datacareerjumpstart.com/daa

Today I am going to try to answer a fundamental question: how should you actually measure user experience, especially with data products—and tie this to business value? It's easy to get lost in analytics and think we're seeing the whole picture, but I argue that this is far from the truth. Product leaders need to understand the subjective experience of our users—and unfortunately, analytics does not tell us this.

The map is not the territory.

In this episode, I discuss why qualitative data and subjective experience is the data that will most help you make product decisions that will lead you to increased business value. If users aren't getting value from your product(s), and their lives aren’t improving, business value will be extremely difficult to create. So today, I share my thoughts on how to move beyond thinking that analytics is the only way to track UX, and how this helps product leaders uncover opportunities to produce better organizational value. 

Ultimately, it’s about creating indispensable solutions and building trust, which is key for any product team looking to make a real impact. Hat tip to UX guru Jared Spool who inspired several of the concepts I share with you today.

Highlights/ Skip to 

Don't target adoption for adoption's sake, because product usage can be a tax or benefit (3:00) Why your analytical mind may bias you—and what changes you might have to do this type of product and user research work (7:31) How "making the user's life better" translates to organizational value (10:17) Using Jared Spool's roller coaster chart to measure your product’s user experience and find your opportunities and successes (13:05) How do you measure that you have done a good job with your UX? (17:28)  Conclusions and final thoughts (21:06)

Quotes from Today’s Episode

Usage doesn't automatically equal value. Analytics on your analytics is not telling you useful things about user experience or satisfaction. Why? "The map is not the territory." Analytics measure computer metrics, not feelings, and let's face it, users aren't always rational. To truly gauge user value, we need qualitative research - to talk to users - and to hear what their subjective experience is. Want meaningful adoption? Talk to and observe your users. That's how you know you are actually making things better. When it’s better for them, the business value will follow. (3:12) Make better things—where better is a measurement based on the subjective experience of the user—not analytics. Usable doesn’t mean they will necessarily want it. Sessions and page views don’t tell you how people feel about it. (7:39) Think about the dreadful tools you and so many have been forced to use: the things that waste your time and don’t let you focus on what’s really important. Ever talked to a data scientist who is sick of doing data prep instead of building models, and wondering, “why am I here? This isn’t what I went to school for.” Ignoring these personal frustrations and feelings and focusing only on your customers’ feature requests, JIRA tickets, stakeholder orders, requirements docs, and backlog items is why many teams end up building technically right, effectively wrong solutions. These end user frustrations are where we find our opportunities to delight—and create products and UXs that matter. To improve their lives, we need to dig into their workflows, identify frustrations, and understand the context around our data product solutions. Product leaders need to fall in love with the problems and the frustrations—these are the magic keys to the value kingdom. However, to do this well, you probably need to be doing less delivery and more discovery. (10:27) Imagine a line chart with a Y-axis that is "frustration" at the bottom to "delight" at the top. The X-axis is their user experience, taking place over time. As somebody uses your data product to do their job/task, you can plot their emotional journey. “Get the data, format the data, include the data in a tool, derive some conclusion, challenge the data, share it, make a decision” etc. As a product manager, you probably know what a use-case looks like. Your first job is to plot their existing experience trying/doing that use case with your data product. Where are they frustrated? Where are they delighted? Celebrate your peaks/delighters, and fall in love with the valleys where satisfaction work needs to be done. Connect the dots between these valleys and business value. Address the valleys—especially the ones that impede business value—and you’ll be on your way to “showing the value of your data product.” Analytics on your data product won’t tell you this information; the map is not the territory. (13:22) Analytics about your data product are lying to you. They give you the facts about the product, but not about the user. An example? “Time spent” doing a task. How long is too long? 5 minutes? 50? Analytics will tell you precisely how long it took. The problem is, it won’t tell you how long it FELT it took. And guess what? Your customers and users only care about how long it felt it took—vs. their expectation. Sure, at some point, analytics might eventually help—at scale—understand how your data product is doing—but first you have to understand how people FEEL about it. Only then will you know whether 5 minutes, or 50 minutes is telling you anything meaningful about what—if anything—needs to change. (16:17)

Do you cringe at the mere mention of the word, "insights"? What about its fancier cousin, "actionable insights"? We do, too. As a matter of fact, on this episode, we discovered that Moe has developed an uncontrollable reflex: any time she utters the word, her hands shoot up uncontrolled to form air quotes. Alas! Our podcast is an audio medium! What about those poor souls who got hired into an "Insights & Analytics" team within their company? Egad! Nonetheless, inspired by an email exchange with a listener, we took a run at the subject with Chris Kocek, CEO of Gallant Branding, who both wrote a book and hosts a podcast on the topic of insights! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

This book is an essential guide designed to equip you with the vital tools and knowledge needed to excel in data science. Master the end-to-end process of data collection, processing, validation, and imputation using R, and understand fundamental theories to achieve transparency with literate programming, renv, and Git--and much more. Each chapter is concise and focused, rendering complex topics accessible and easy to understand. Data Insight Foundations caters to a diverse audience, including web developers, mathematicians, data analysts, and economists, and its flexible structure allows enables you to explore chapters in sequence or navigate directly to the topics most relevant to you. While examples are primarily in R, a basic understanding of the language is advantageous but not essential. Many chapters, especially those focusing on theory, require no programming knowledge at all. Dive in and discover how to manipulate data, ensure reproducibility, conduct thorough literature reviews, collect data effectively, and present your findings with clarity. What You Will Learn Data Management: Master the end-to-end process of data collection, processing, validation, and imputation using R. Reproducible Research: Understand fundamental theories and achieve transparency with literate programming, renv, and Git. Academic Writing: Conduct scientific literature reviews and write structured papers and reports with Quarto. Survey Design: Design well-structured surveys and manage data collection effectively. Data Visualization: Understand data visualization theory and create well-designed and captivating graphics using ggplot2. Who this Book is For Career professionals such as research and data analysts transitioning from academia to a professional setting where production quality significantly impacts career progression. Some familiarity with data analytics processes and an interest in learning R or Python are ideal.

Mark, Marisa, and Cris break down this week's flood of economic data and explain how new auto tariffs will affect consumers and the overall economy. Their conversation addresses important concerns about the quality and availability of government statistics as they consider private-sector alternatives. They wrap up the episode with the ever-popular "statistics game" highlighting key economic indicators and with responses to listener questions.   Hosts: Mark Zandi – Chief Economist, Moody’s Analytics, Cris deRitis – Deputy Chief Economist, Moody’s Analytics, and Marisa DiNatale – Senior Director - Head of Global Forecasting, Moody’s Analytics Follow Mark Zandi on 'X', BlueSky or LinkedIn @MarkZandi, Cris deRitis on LinkedIn, and Marisa DiNatale on LinkedIn

Questions or Comments, please email us at [email protected]. We would love to hear from you.    To stay informed and follow the insights of Moody's Analytics economists, visit Economic View.

On this podcast episode of Data Unchained, Sanjay Annadate, Vice President and Business Head EMEA at Latent View Analytics, joins us to talk about optimizing marketing spend to making supply chains more sustainable. Discover the top four challenges clients bring to LatentView as Sanjay shares how his team uses pre-built analytics solutions to solve problems fast and unlock performance across industries.

DataUnchained #AIinBusiness #DigitalTransformation #DataAnalytics #AICompliance #EnterpriseAI #DataStrategy #CustomerInsights #DataDriven #MarketingAnalytics #SupplyChainOptimization #LatentView #BusinessIntelligence #TechPodcast #InnovationLeadership #FutureOfData #AIGovernance #SmartData #AITrends #EuropeanTech

Cyberpunk by jiglr | https://soundcloud.com/jiglrmusic Music promoted by https://www.free-stock-music.com Creative Commons Attribution 3.0 Unported License https://creativecommons.org/licenses/by/3.0/deed.en_US Hosted on Acast. See acast.com/privacy for more information.

A pesquisa State of Data Brazil 2025, conduzida pelo Data Hackers em parceria com a Bain & Company, reuniu mais de 5,2 mil profissionais de dados para entender os desafios, tendências e transformações do setor. Esse é o maior mapeamento já realizado sobre o mercado brasileiro de trabalho em dados e inteligência artificial !! Neste episódio, recebemos Felipe Fiamozzini (Expert Partner na Bain & Company) para explorar os principais insights do relatório, como: Salários e evolução das carreiras em dados e IA; Tendências tecnológicas e adoção de GenAI; Impacto dos layoffs e mudanças no modelo de trabalho; e o que esperar do mercado de dados em 2025. 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 Felipe Fiamozzini — Expert Partner na Bain & Company Nossa Bancada Data Hackers: Monique Femme — Head of Community Management na Data HackersGabriel Lages — Co-founder da Data Hackers e Data & Analytics Sr. Director na Hotmart. Referências: Semana de tecnologia Itaú: https://comunicatech.itau.com.br/semanadatecnologia2025_datahackersBaixe a pesquisa State of Data Brazil 2025: https://www.datahackers.news/p/relatorio2024-2025

How do you stand out as an analyst or gain the support you need to succeed? Join Jason Krantz, a renowned analytics leader, in this eye-opening session designed to help you thrive in your role and make a meaningful impact. Jason brings a wealth of experience and actionable insights, helping analysts improve their skills and influence within their organizations. Whether you're just starting or looking to level up, this session will equip you with the mindset and tools to excel. What You'll Learn: How to align your work with the business's most important goals. The value of having a boss who understands data and why it matters. Strategies to excel in environments where support might be lacking. What it takes to stand out as an analyst in today's competitive landscape.   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

Time Series Analysis with Spark provides a practical introduction to leveraging Apache Spark and Databricks for time series analysis. You'll learn to prepare, model, and deploy robust and scalable time series solutions for real-world applications. From data preparation to advanced generative AI techniques, this guide prepares you to excel in big data analytics. What this Book will help me do Understand the core concepts and architectures of Apache Spark for time series analysis. Learn to clean, organize, and prepare time series data for big data environments. Gain expertise in choosing, building, and training various time series models tailored to specific projects. Master techniques to scale your models in production using Spark and Databricks. Explore the integration of advanced technologies such as generative AI to enhance predictions and derive insights. Author(s) Yoni Ramaswami, a Senior Solutions Architect at Databricks, has extensive experience in data engineering and AI solutions. With a focus on creating innovative big data and AI strategies across industries, Yoni authored this book to empower professionals to efficiently handle time series data. Yoni's approachable style ensures that both foundational concepts and advanced techniques are accessible to readers. Who is it for? This book is ideal for data engineers, machine learning engineers, data scientists, and analysts interested in enhancing their expertise in time series analysis using Apache Spark and Databricks. Whether you're new to time series or looking to refine your skills, you'll find both foundational insights and advanced practices explained clearly. A basic understanding of Spark is helpful but not required.