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BI

Business Intelligence (BI)

data_visualization reporting analytics

1211

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111 peak/qtr
2020-Q1 2026-Q1

Activities

1211 activities · Newest first

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by Ani Jain (Google Cloud) , Vijay Venugopal (Google Cloud) , Anssi Rusi (SuperMetrics) , Marc Wollnik (Google Cloud)

Imagine a world where interacting with your data is as simple as chatting with a friend. Gemini in Looker makes this a reality, bringing the power of Google's most advanced AI models and agents directly to your BI workflows. This session explores how Gemini's genAI capabilities and Looker Agents are being integrated into Looker, empowering users to analyze data, build dashboards, and generate insights using natural language. Discover how this powerful combination unlocks new levels of productivity for BI professionals and business users alike.

Discover how Google’s interconnected ecosystem of Google Cloud platform and specialty solutions can address the needs and challenges of resource-constrained IT teams. We’ll delve into practical use cases and demonstrate how Google Cloud’s specialized business intelligence platform (Looker) and security solutions (Google Security Operations, Mandiant) can help your business improve efficiency and reduce costs while improving your security posture.

In this hands-on lab, you'll learn how to build a powerful business intelligence (BI) dashboard using Looker Studio and BigQuery. Discover how to upload and query data, create reports datasets, and run scheduled queries to uncover valuable insights from large service usage logs. With your dashboard, you'll gain the ability to identify trends, optimize operations, and make data-driven decisions to improve efficiency and service quality.

If you register for a Learning Center lab, please ensure that you sign up for a Google Cloud Skills Boost account for both your work domain and personal email address. You will need to authenticate your account as well (be sure to check your spam folder!). This will ensure you can arrive and access your labs quickly onsite. You can follow this link to sign up!

Looker is transforming business intelligence (BI) with the power of AI. This session explores the latest AI advancements in Looker, showcasing how these new features – combined with the power of its semantic modeling and self-service exploration capabilities – simplify data analysis and empower everyone to make data-driven decisions. We’ll cover Looker AI and BI strategies, demonstrate new capabilities, and share best practices for leveraging AI in your BI workflows.

Is siloed data hindering your operations? Learn how Nuro consolidated their transactional, relational, and vector data sets on AlloyDB for PostgreSQL, and how they’re now able to do operational analysis, real-time analytics, and business intelligence (BI) reports on the same platform. Join this panel session to discover best practices for unifying vector and relational data, and learn how Nuro is now able to satisfy their self-driven car analytics use cases in a cost-effective way.

Wayfair’s Multi-year Data Mesh Journey | Nachiket Mehta and Piyush Tiwari | Shift Left Data Confe...

Wayfair’s Multi-year Data Mesh Journey | Nachiket Mehta and Piyush Tiwari | Shift Left Data Conference 2025

Wayfair’s multi-year Data Mesh journey involved shifting from a monolithic, centralized data model to a decentralized, domain-driven architecture built on microservices. By embracing Data Mesh principles, Wayfair empowered domain teams to take end-to-end ownership of their data.

Key enablers included a data contract management platform ensure trusted, discoverable data products, and the development of Taxon, an internal ontology and knowledge graph that unified semantics across domains while supporting the company's tech modernization.

Organizationally, Wayfair introduced an Embedded Data Engineering model – embedding data engineers within domain teams – to instill a “Data-as-a-Product” mindset among data producers. This sociotechnical shift ensured that those who create data also own its quality, documentation, and evolution, rather than relying on a centralized BI team. As a result, Wayfair’s data producers are now accountable for well-defined, high-quality data products, and data consumers can more easily discover and trust data through the unified catalog and ontology.

The presentation will highlight how Wayfair has adopted the “shift left” (pushing data ownership and quality to the source teams) and next heading towards “shift right” (focusing on consumer-driven data products and outcomes) to unlock business outcomes. This session will share both technical strategies and business results from Wayfair’s Data Mesh journey.

In this episode I'll show you what it takes to land data analyst jobs! I'll provide in-depth insights and tips for six data analyst positions with salaries ranging from $35K to $200K-- and why should you apply even if you don't meet all the requirements. 💌 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 No College Degree As A Data Analyst Playlist: https://youtu.be/mSWtnjq4LRE?si=FlfChqSxIPBXc_Lb ⌚ TIMESTAMPS Data Analyst Jobs: How Much $$$ Could You ACTUALLY Make??? 00:00 - Introduction 00:21 - Data Analyst Job #1: Data Specialist ($35k) 04:00 - Data Analyst Job #2: Business and Data Analyst ($55k) 07:48 - Data Analyst Job #3: Data Visualization Analyst ($75k) 10:21 - Data Analyst Job #4: Senior Financial Analyst ($90k) 13:04 - Data Analyst Job #5: Senior Investment Operations Data Analyst ($125k) 14:35 - Data Analyst Job #6: Business Intelligence Engineer ($107k to $189k) 🔗 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

In the retail industry, data science is not just about crunching numbers—it's about driving business impact through well-designed experiments. A-B testing in a physical store setting presents unique challenges that require careful planning and execution. How do you balance the need for statistical rigor with the practicalities of store operations? What role does data science play in ensuring that test results lead to actionable insights?  Philipp Paraguya is the Chapter Lead for Data Science at Aldi DX. Previously, Philipp studied applied mathematics and computer science and has worked as a BI and advanced analytics consultant in various industries and projects since graduating. Due to his background as a software developer, he has a strong connection to classic software engineering and the sensible use of data science solutions. In the episode, Adel and Philipp explore the intricacies of A-B testing in retail, the challenges of running experiments in brick-and-mortar settings, aligning stakeholders for successful experimentation, the evolving role of data scientists, the impact of genAI on data workflows, and much more. Links Mentioned in the Show: Aldi DXConnect with PhilippCourse: Customer Analytics and A/B Testing in PythonRelated Episode: Can You Use AI-Driven Pricing Ethically? with Jose Mendoza, Academic Director & Clinical Associate Professor at NYUSign up to attend RADAR: Skills Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Over the past 1000 days, I've interviewed some of the brightest minds in the data world. And in today’s episode, you’ll hear genius career advice from 6 of my favorite female data analysts. They’ll teach you what it’s like working in data, and help you learn what it takes to actually land a data job. 💌 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 00:45 - Sundas Khalid: Keep going, no matter what. 05:26 - Cole Knaflic: It's less about analysis and more about presentation. 11:38 - Rachael Finch: Always be networking. 23:22 - Jess Ramos: Be proactive in your job search and networking. 28:44 - Hana M.K.: Avoid Shiny Object Syndrome! 32:41 - Erin Shina: The importance of having projects. Check out the full episodes from this compilation! 1. How This High School Drop Out Became a $500k Data Analyst (Sundas Khalid) - https://datacareerpodcast.com/episode/148-how-this-high-school-drop-out-became-a-500k-data-analyst-sundas-khalid 2. Meet The Woman Who Changed Data Storytelling Forever (Cole Knaflic) - https://datacareerpodcast.com/episode/142-meet-the-woman-who-changed-data-storytelling-forever-cole-knafflic 3. How She Landed a Business Intelligence Analyst Job in Less than 100 Days (w/ Rachael Finch) - https://datacareerpodcast.com/episode/125-how-she-landed-a-business-intelligence-analyst-job-in-less-than-100-days-w-rachael-finch 4. Navigating Your Data Career Journey w/ Jess Ramos - https://datacareerpodcast.com/episode/49-navigating-your-data-career-journey-w-jess-ramos 5. Presenting for Data Analysts w/ Hana M.K. - https://datacareerpodcast.com/episode/84-presenting-for-data-analysts-w-hana-mk 6. From Music to Spreadsheets: Erin Shina’s 90-Day Transformation from Music to Financial Data Analyst - https://datacareerpodcast.com/episode/65-from-music-sheet-to-spreadsheets-erin-shinas-90-day-transformation-from-music-to-financial-data-analyst 🔗 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

If you want to build a strong career in data, this show is for you. We welcomed the new face of Mavens of Data, Kristen Kehrer, who shared her best advice for data professionals and those aspiring toward a data career. You'll leave the show with some actionable tips and some of the best career advice directly from one of our favorite data pros of all time. What You'll Learn: What you should focus on if you're trying to land your first job How to succeed once you are in that initial role How to think about building a successful career long-term   Register for free to be part of the next live session: https://bit.ly/3XB3A8b   About our guest: Kristen Kehrer has been providing innovative & practical statistical modeling solutions in the utilities, healthcare, and eCommerce sectors since 2010. Alongside her professional accomplishments, she achieved recognition as a LinkedIn Top Voice in Data Science & Analytics in 2018. Kristen is also the founder of Data Moves Me, LLC, and has previously served as a faculty member and subject matter expert at the Emeritus Institute of Management and UC Berkeley Ext.

 Kristen lights up on stage and has spoken at conferences like ODSC, DataScienceGO, BI+Analytics Conference, Boye Conference, and Big Data LDN, etc.

She holds a Master of Science degree in Applied Statistics from Worcester Polytechnic Institute and a Bachelor of Science degree in Mathematics.

datamovesme.com   Follow us on Socials: LinkedIn YouTube Instagram (Mavens of Data) Instagram (Maven Analytics) TikTok Facebook Medium X/Twitter

Learn SQL in a Month of Lunches

Use SQL to get the data you need in no time at all! Learn to read and write basic queries, troubleshoot common problems, and control your own business data in just 24 short lessons–no programming experience required! SQL has been designed to be as close to English as possible—anyone can learn it! Learn SQL in a Month of Lunches helps you add this lucrative and highly sought-after skill to your resume in just 24 fun and friendly lessons. The book emphasizes practical uses for the language in the real-world, so you’ll just learn the most useful skills for business data analysis. Inside Learn SQL in a Month of Lunches you’ll discover how to: Set up your first database with MySQL Write your own SQL queries See only the data you need from large datasets Connect different sets of data Analyze data with functions and aggregations Master basic data manipulation techniques Save queries in stored procedures and views Create tables to store data efficiently Read and improve SQL written by others If you use Excel, Tableau, or PowerBI to crunch business data, you’ve probably seen a lot of SQL already. And guess what? It’s easy to master the most useful parts of SQL! In just a few quick lessons, Learn SQL in a Month of Lunches will get you writing your own queries, modifying existing SQL statements, and working with data like a pro. 25-year SQL veteran Jeff Iannucci makes SQL a snap through hands-on lab exercises, relevant code examples, and easy-to-understand language. About the Technology SQL, Structured Query Language, is the standard way to query, create, and manage relational databases like SQL Server, PostgreSQL, and Oracle. It’s also a superpower for data analysts who need to go beyond spreadsheets and BI dashboarding tools. SQL is easy to read and understand, and with this book (and a little practice) you’ll be pulling data, tweaking tables, and cranking out amazing reports and presentations in no time at all! About the Book Learn SQL in a Month of Lunches introduces SQL to data analysts and other aspiring data pros with no prior experience using relational databases. In it, you’ll complete 24 short lessons, each of which teaches an essential SQL skill for retrieving, filtering, and analyzing data. You’ll practice each new technique with a friendly hands-on lab designed to take about 15 minutes, as you learn to write queries that deliver the exact data you need. Along the way, you’ll build a valuable intuition for how databases operate in real business scenarios. What's Inside Get the data you need from any relational database Filter, sort, and group data Combine data from multiple tables Create, update, and delete data About the Reader For students, aspiring data analysts, software developers, and anyone else who wants to work with relational databases. About the Author Jeff Iannucci is a Senior Consultant with Straight Path Solutions. For over 20 years, he has worked extensively with SQL in sectors such as healthcare, finance, retail sales, and government. Quotes An essential guide. Jeff has carefully developed each chapter to ensure clarity and comprehensiveness, making complex concepts accessible and practical. - Buck Woody, Microsoft The fastest and the most effective way to learn SQL, regardless of your background or technical knowledge level. - Kevin Kline, author of SQL in a Nutshell Explains concepts straightforwardly to help the reader grow their skills over a month of sessions. - Steve Jones, SQL Server Central Great selection of bite-sized, digestible courses to complement your lunch arrangement. It leaves you smarter every day. - Simon Tschöke, Databricks

SnowPro Core Certification Study Guide

The "SnowPro Core Certification Study Guide" provides a comprehensive resource for mastering Snowflake data cloud concepts and passing the SnowPro Core exam. Through detailed explanations and practical exercises, you will gain the knowledge and skills necessary to successfully implement and manage Snowflake's powerful features and integrate data solutions effectively. What this Book will help me do Efficiently load and manage data in Snowflake for modern data processing. Optimize queries and configure Snowflake's performance features for data analytics. Securely implement access control and user roles to ensure data privacy. Apply Snowflake's sharing features to collaborate within and between organizations. Prepare effectively for the SnowPro Core exam with mock tests and review tools. Author(s) Jatin Verma is a renowned expert in Snowflake technologies and a certified SnowPro Core professional. With years of hands-on experience working with data solutions, Jatin excels at breaking down complex concepts into digestible lessons. His approachable writing style and dedication to education make this book a trusted resource for both aspiring and current professionals. Who is it for? This book is perfect for data engineers, analysts, database administrators, and business intelligence professionals who are looking to gain expertise in Snowflake and achieve SnowPro Core certification. It is particularly suited for those with foundational knowledge of databases, data warehouses, and SQL, seeking to advance their skills in Snowflake and become certified professionals. By leveraging this guide, readers can solidify their Snowflake knowledge and confidently approach the SnowPro Core certification exam.

Summary In this episode of the Data Engineering Podcast Bartosz Mikulski talks about preparing data for AI applications. Bartosz shares his journey from data engineering to MLOps and emphasizes the importance of data testing over software development in AI contexts. He discusses the types of data assets required for AI applications, including extensive test datasets, especially in generative AI, and explains the differences in data requirements for various AI application styles. The conversation also explores the skills data engineers need to transition into AI, such as familiarity with vector databases and new data modeling strategies, and highlights the challenges of evolving AI applications, including frequent reprocessing of data when changing chunking strategies or embedding models.

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. Your host is Tobias Macey and today I'm interviewing Bartosz Mikulski about how to prepare data for use in AI applicationsInterview IntroductionHow did you get involved in the area of data management?Can you start by outlining some of the main categories of data assets that are needed for AI applications?How does the nature of the application change those requirements? (e.g. RAG app vs. agent, etc.)How do the different assets map to the stages of the application lifecycle?What are some of the common roles and divisions of responsibility that you see in the construction and operation of a "typical" AI application?For data engineers who are used to data warehousing/BI, what are the skills that map to AI apps?What are some of the data modeling patterns that are needed to support AI apps?chunking strategies metadata managementWhat are the new categories of data that data engineers need to manage in the context of AI applications?agent memory generation/evolution conversation history managementdata collection for fine tuningWhat are some of the notable evolutions in the space of AI applications and their patterns that have happened in the past ~1-2 years that relate to the responsibilities of data engineers?What are some of the skills gaps that teams should be aware of and identify training opportunities for?What are the most interesting, innovative, or unexpected ways that you have seen data teams address the needs of AI applications?What are the most interesting, unexpected, or challenging lessons that you have learned while working on AI applications and their reliance on data?What are some of the emerging trends that you are paying particular attention to?Contact Info WebsiteLinkedInParting 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 SparkRayChunking StrategiesHypothetical document embeddingsModel Fine TuningPrompt CompressionThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Tag Manager Italia collaborated with CNH to design and implement a global GA4-based data strategy, unifying analytics across their extensive operations. This session explores the whole project, with a focus on how advanced tools like BigQuery and Databricks enabled data centralization, while custom Power BI dashboards and privacy-compliant frameworks empowered informed decisions and enhanced marketing and business outcomes.

Season 1 Episode 29: Navigating Trade-Offs and Balancing Priorities The Data Product Management In Action podcast, brought to you by executive producer Scott Hirleman, is a platform for data product management practitioners to share insights and experiences. In this episode of Data Product Management in Action, host Alexa Westlake talks with Anita Chen, diving into the complexities of managing data products. Anita, a product manager at PagerDuty, shares her approach to defining data products, prioritizing work, and balancing project work with interrupt-driven tasks. They discuss the critical roles of governance, security, and user enablement while emphasizing the importance of transparency and communication. The conversation also explores the transformative potential of generative AI in data product interactions and the build-vs-buy decision-making process. Gain insights into how data product management uniquely differs from traditional software product management and learn actionable strategies for success. Meet our Host Alexa Westlake: Alexa is a Data Analytics Leader in the Identity and Access Management space with a proven track record scaling high-growth SaaS companies. As a Staff Data Analyst at Okta, she brings a wealth of expertise in enterprise data, business intelligence, and strategic decision-making from the various industries she's worked in including telecommunications, strategy execution, and cloud computing. With a passion for harnessing the power of data for actionable insights, Alexa plays a crucial role in driving Okta's security, growth, and scale, helping the organization leverage data to execute on their market opportunity. Connect with Alexa on LinkedIn.

Meet our guest Anita Chen:  Anita is a Data Product Manager at PagerDuty, a digital operations company helping teams resolve issues faster, eliminate alert fatigue, and build more reliable services! Her background is mainly in the People Analytics space which has now expanded to data at scale with our Enterprise Data Team. She currently helps build data products that enable our teams to deliver the best possible customer experience. Anita is most passionate about how data can impact someone's lived experience and endeavor to democratize data in everything she builds. Connect with Anita on LinkedIn. All views and opinions expressed are those of the individuals and do not necessarily reflect their employers or anyone else.  Join the conversation on LinkedIn.  Apply to be a guest or nominate someone that you know.  Do you love what you're listening to? Please rate and review the podcast, and share it with fellow practitioners you know. Your support helps us reach more listeners and continue providing valuable insights! 

The Data Product Management In Action podcast, brought to you by  executive producer Scott Hirleman, is a platform for data product management practitioners to share insights and experiences. In Season 01, Episode 28, we are excited to introduce to you a new host, Alexa Westgate! Join us as we learn all about her data journey. She'll discuss how she got into DPM, some of her greatest moments and challenges. You'll be excited for her future episodes! About our host Alexa Westlake: Alexa is a Data Analytics Leader in the Identity and Access Management space with a proven track record scaling high-growth SaaS companies. As a Staff Data Analyst at Okta, she brings a wealth of expertise in enterprise data, business intelligence, and strategic decision-making from the various industries she's worked in including telecommunications, strategy execution, and cloud computing. With a passion for harnessing the power of data for actionable insights, Alexa plays a crucial role in driving Okta's security, growth, and scale, helping the organization leverage data to execute on their market opportunity. Connect with Alexa on LinkedIn. All views and opinions expressed are those of the individuals and do not necessarily reflect their employers or anyone else.  Join the conversation on LinkedIn.  Apply to be a guest or nominate someone that you know.  Do you love what you're listening to? Please rate and review the podcast, and share it with fellow practitioners you know. Your support helps us reach more listeners and continue providing valuable insights! 

As we look back at 2024, we're highlighting some of our favourite episodes of the year, and with 100 of them to choose from, it wasn't easy! The four guests we'll be recapping with are: Lea Pica - A celebrity in the data storytelling and visualisation space. Richie and Lea cover the full picture of data presentation, how to understand your audience, how to leverage hollywood storytelling and more. Out December 19.Alex Banks - Founder of Sunday Signal. Adel and Alex cover Alex’s journey into AI and what led him to create Sunday Signal, the potential of AI, prompt engineering at its most basic level, chain of thought prompting, the future of LLMs and more. Out December 23.Don Chamberlin - The renowned co-inventor of SQL. Richie and Don explore the early development of SQL, how it became standardized, the future of SQL through NoSQL and SQL++ and more. Out December 26.Tom Tunguz - general Partner at Theory Ventures, a $235m VC firm. Richie and Tom explore trends in generative AI, cloud+local hybrid workflows, data security, the future of business intelligence and data analytics, AI in the corporate sector and more. Out December 30. Rapid change seems to be the new norm within the data and AI space, and due to the ecosystem constantly changing, it can be tricky to keep up. Fortunately, any self-respecting venture capitalist looking into data and AI will stay on top of what’s changing and where the next big breakthroughs are likely to come from. We all want to know which important trends are emerging and how we can take advantage of them, so why not learn from a leading VC.  Tomasz Tunguz is a General Partner at Theory Ventures, a $235m early-stage venture capital firm. He blogs sat tomtunguz.com & co-authored Winning with Data. He has worked or works with Looker, Kustomer, Monte Carlo, Dremio, Omni, Hex, Spot, Arbitrum, Sui & many others. He was previously the product manager for Google's social media monetization team, including the Google-MySpace partnership, and managed the launches of AdSense into six new markets in Europe and Asia. Before Google, Tunguz developed systems for the Department of Homeland Security at Appian Corporation.  In the episode, Richie and Tom explore trends in generative AI, the impact of AI on professional fields, cloud+local hybrid workflows, data security, and changes in data warehousing through the use of integrated AI tools, the future of business intelligence and data analytics, the challenges and opportunities surrounding AI in the corporate sector. You'll also get to discover Tom's picks for the hottest new data startups. Links Mentioned in the Show: Tom’s BlogTheory VenturesArticle: What Air Canada Lost In ‘Remarkable’ Lying AI Chatbot Case[Course] Implementing AI Solutions in BusinessRelated Episode: Making Better Decisions using Data & AI with Cassie Kozyrkov, Google's First Chief Decision ScientistSign up to RADAR: AI...