Join Simo & friends at the fireside to discuss topics ranging from analytics to data engineering, from career advice to data-related life hacks, and anything and everything you can think of. Audience participation is (almost) mandatory.

We asked 1,000(ish) marketing analytics pros their feelings about their job and their frustrations. The data shows that data is low on their list of frustrations. Let's dive into what they said, and what they didn't.

Demonstrate any Digital Analytics solutions or method of your own that is way beyond the defaults. Who decides who's gonna win? The audience. Send your nomination to [email protected]!

It was a big bang on social media when the Austrian data protection authority published its decision on Google Analytics, effectively declaring the current default implementation as non-compliant.

Maybe you've been running Google Analytics 4, aka GA4, alongside Universal Analytics for a while now. Or maybe you haven't even started. Well, now is the time, because everything is about to change! GA4 is here and it's here to stay. In this session, Krista will walk you through the ins and outs of GA4 and show you some cool new things to help you maximize your use of Google Analytics.

All too often, both individuals and organizations hoard the most prized asset in the digital ecosystem: data. This has led to different types of data — such as those tied to ad revenue, subscriptions, content engagement and customer profiles — being kept in silos, to be managed via disparate point solutions. Operating in this way means businesses never get a comprehensive view of their customers, leading to missed opportunities to drive personalized experiences, increase revenues, boost retention and remain privacy-compliant.

Is it possible to analyze and fix the e-commerce tracking of a top “Serie A” football team WITHOUT having any access to the Google Analytics and Tag Manager panels, WITHOUT collaboration from the developers, WITHOUT a brief from the former supplier, and with very little time available? Apparently, yes. Follow the running commentary of an incredible case history and the secrets of a providential intervention carried out at the very last moment, to understand the logic beneath it and be able to replicate it if needed.

One of the things that Yehoshua enjoys very much about Superweek is the opportunity to learn from so many different individuals. In this talk, Yehoshua will take the role of teacher and share with you a potpourri list of "Things He's Learned" that relate to data and analytics over the past two years. Ranging from strategy to tactics, technical to generalized, this presentation is sure to have at least 1 slide that is so amazing that the viral tweetstorm that will ensue might possibly crash the internet

Why? What's new here? What's the innovation?

In this podcast, I give my opinion if YOU should do the Masters in Analytics from Georgia Tech (OMSA). I’ll share my experience, what I thought was good, and not so good, and help you make your decision!

Watch this episode on YouTube: https://www.youtube.com/watch?v=dpVNRB67-So&t=1s

If you want a free way to kickstart your analytics career, check out my free 33-page PDF giving you an introduction to everything you need to know: https://www.datacareerjumpstart.com/roadmap

If you’re just starting out, you can check out my 21 Day To Data Challenge: https://www.datacareerjumpstart.com/challenge

Want to learn data science while building your portfolio? Check out Data Career Jumpstart: https://www.datacareerjumpstart.com/data-career-jumpstart-course

MORE DATA ANALYTICS CONTENT HERE:

📺 Subscribe YouTube: https://www.youtube.com/c/AverySmithDataCareerJumpstart/videos

🎙Listen to My Podcast: https://podcasts.apple.com/us/podcast/data-career-podcast/id1547386535

👔 Connect with me on LinkedIn: https://www.linkedin.com/in/averyjsmith/

📸 Instagram: https://www.instagram.com/datacareerjumpstart/

👾Join My Discord: https://www.datacareerjumpstart.com/discord

🎵 TikTok: https://www.tiktok.com/@verydata? 

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

Chad Moutray, Chief Economist of National Association of Manufacturers, joins the podcast to discuss manufacturing, supply chains, and labor market shortages. Mark is on a hot streak in the stats game.

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 the physical world, you can see a bridge rusting or a building facade crumbling and know you have to intervene to prevent the infrastructure from collapsing. But when all you have is bits and bytes - digital stuff, like software and data ---how can you tell if your customer-facing digital interactions or data-driven analytics and models are about to go up in smoke?

Observability is a new term that describes what we used to call IT monitoring. The new moniker is fitting given all the technology changes that have happened in the past decade. The cloud, big data, microservices, containers, cloud applications, machine learning, and artificial intelligence have created a dramatically complex IT and data environment that is harder than ever to manage. And the stakes are higher as organizations move their operations online to compete with digital natives. Today, you can't run digital or data operations without observability tools.

Kevin Petrie is one of the industry's foremost experts on observability. He is vice president of research at Eckerson Group where he leads a team of distinguished analysts. He recently wrote an article titled "The Five Shades of Observability" that describes five types of observability tools. In this podcast, we discuss what observability is, why you need it, and the types of available tools. We also speculate on the future of this technology and recommend how to select an appropriate observability product.

Discover the power of Amazon QuickSight with this comprehensive guide. Learn to create stunning data visualizations, integrate machine learning insights, and automate operations to optimize your data analytics workflows. This book offers practical guidance on utilizing QuickSight to develop insightful and interactive business intelligence solutions. What this Book will help me do Understand the role of Amazon QuickSight within the AWS analytics ecosystem. Learn to configure data sources and develop visualizations effectively. Gain skills in adding interactivity to dashboards using custom controls and parameters. Incorporate machine learning capabilities into your dashboards, including forecasting and anomaly detection. Explore advanced features like QuickSight APIs and embedded multi-tenant analytics design. Author(s) None Samatas is an AWS-certified big data solutions architect with years of experience in designing and implementing scalable analytics solutions. With a clear and practical approach, None teaches how to effectively leverage Amazon QuickSight for efficient and insightful business intelligence applications. Their expertise ensures readers will gain actionable skills. Who is it for? This book is ideal for business intelligence (BI) developers and data analysts looking to deepen their expertise in creating interactive dashboards using Amazon QuickSight. It is a perfect guide for professionals aiming to explore machine learning integration in BI solutions. Familiarity with basic data visualization concepts is recommended, but no prior experience with Amazon QuickSight is needed.

In my opinion, any organisation with respect for its data should have a Chief Data & Analytics Officer (CDAO) as part of their C-suite. Although the CDAO role is still nascent, business leaders across many industries are starting to appreciate the need for a data and analytics voice at board and executive level. So, what does a CDAO do? How should they spend their time to balance strategic influence with operational delivery of data products? To answer these questions and many more related to the principal analytics role, I recently spoke to Kshira Saagar, who is the Chief Data Officer at Latitude Financial. As the CDO at one of Australia’s largest consumer financial services firms, Kshira is responsible for the end-to-end journey of data through the organisation, from extraction to value creation through data products. He leads a large team of Data Scientists, Data analysts, Data Architects, Data Engineers, Machine Learning Engineers, Data Warehouse Developers, BI Developers and Data Governance experts, who are responsible for bringing the company’s data and analytics strategy to life. In this episode of Leaders of Analytics, we discuss: What a week in the role of a CDAO looks likeHow to secure strategic support and executive sponsorship for analytics projectsWhat’s required of CDAOs and their teams to foster a data literate organisationHow to structure data and analytics functions for successThe future of the CDAO role, and much more.Learn more about Kshira at https://www.kshirasaagar.com/

Send us a text Want to be featured as a guest on Making Data Simple? Reach out to us at [[email protected]] or for faster response, complete this form and tell us why you should be next.

Abstract Making Data Simple Podcast is hosted by Al Martin, WW VP Account Technical Leader IBM Technology Sales, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun. This week on Making Data Simple, we have Ayal Steinburg VP, WW Data, AI, and Automation Sales Leader Global Markets. Ayal started off in music and then in the late 1990’s shifted to retail where he learned about data and analytics. In the past 20 years Ayal has held various sales rolls during his career.  Show Notes 9:18 – Ayal’s history 11:50 – Ayal talks about his portfolio  16:16 – Market expansion and reducing costs 19:02 – Platform and one product 21:50 – Why IBM technologies? 24:20 – Why are customers moving data? 27:56 – Is “Switzerland” a hard or easy sell? 30:52 – What is your biggest challenge right now? IBM Connect with the Team Producer Kate Brown - LinkedIn. Producer Steve Templeton - LinkedIn. Host Al Martin - LinkedIn and Twitter.  Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

In this episode, I interviewed Kyle Pastor (aka @DataStuffPlus 70K followers on Instagram). We chatted about how Kyle got started with data, why he runs his Instagram, and why he does fun data projects.

When breaking into data, it’s always important to have a portfolio of projects to show off, and who knows, these projects could turn into businesses, job offers, or sponsorship opportunities.

You can follow Kyle’s writing and tutorials on his Medium.

Also, don’t miss Kyle’s data viz Instagram.

Want a free guide to get your data journey started? Get a free data roadmap here.

Ready to jumpstart your data career? Try the #21DaysToData Challenge.

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

Innovative organizations today are reaping the benefits of combining data from a variety of internal and external sources. By collecting, storing, analyzing, and leveraging external data, these companies are able to outperform competitors by unlocking improvements in growth, productivity, and risk management. This report explains how you can harness the power of external data to boost analytics, find competitive advantages, and drive value. Author Joseph D. Stec explains how clever companies are now using advanced analytics tools that can simultaneously collect, mix, and match diverse data from disparate data sources. This enables them to improve products and brand loyalty, generate better conversions, identify trends earlier, and pinpoint additional ways to improve customer satisfaction. With this report, you will: Learn how external data elevates and enhances the way you analyze and interpret data outside of your apps or databases Dive into the nuts and bolts of external data platforms to solve key challenges Understand how new technology makes external data easier to use with analytics Learn how an external data platform fits into your data architecture Gain access to relevant external data signals with Explorium, an automated external data management platform Unlock improvements in growth, productivity, and risk management

Summary Data engineering is a relatively young and rapidly expanding field, with practitioners having a wide array of experiences as they navigate their careers. Ashish Mrig currently leads the data analytics platform for Wayfair, as well as running a local data engineering meetup. In this episode he shares his career journey, the challenges related to management of data professionals, and the platform design that he and his team have built to power analytics at a large company. He also provides some excellent insights into the factors that play into the build vs. buy decision at different organizational sizes.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Today’s episode is Sponsored by Prophecy.io – the low-code data engineering platform for the cloud. Prophecy provides an easy-to-use visual interface to design & deploy data pipelines on Apache Spark & Apache Airflow. Now all the data users can use software engineering best practices – git, tests and continuous deployment with a simple to use visual designer. How does it work? – You visually design the pipelines, and Prophecy generates clean Spark code with tests on git; then you visually schedule these pipelines on Airflow. You can observe your pipelines with built in metadata search and column level lineage. Finally, if you have existing workflows in AbInitio, Informatica or other ETL formats that you want to move to the cloud, you can import them automatically into Prophecy making them run productively on Spark. Create your free account today at dataengineeringpodcast.com/prophecy. The only thing worse than having bad data is not knowing that you have it. With Bigeye’s data observability platform, if there is an issue with your data or data pipelines you’ll know right away and can get it fixed before the business is impacted. Bigeye let’s data teams measure, improve, and communicate the quality of your data to company stakeholders. With complete API access, a user-friendly interface, and automated yet flexible alerting, you’ve got everything you need to establish and maintain trust in your data. Go to dataengineeringpodcast.com/bigeye today to sign up and start trusting your analyses. Your host is Tobias Macey and today I’m interviewing Ashish Mrig about his path as a data engineer

Interview

Introduction How did you get involved in the area of data management? You currently lead a data engineering team at a relatively large company. What are the topics that account for the majority of your time and energy? What are some of the most valuable lessons that you’ve learned about managing and motivating teams of data professionals? What has been your most consistent challenge across the different generations of the data ecosystem? How is your current data platform architected? Given the current state of the technology and services landscape, how would you approach the design and implementation of a greenfield rebuild of your platform? What are some of the pitfalls that you have seen data teams encounter most frequently? You are running a data engineering meetup for your local community in the Boston area. What have been some of the recurring themes that are discussed in those events?

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Elaine Buckberg, Chief Economist of General Motors, joins Mark, Ryan, and Cris to discuss the vehicle industry. They breakdown what is going on with vehicle prices, supply chains, and the outlook for electric vehicles. Full episode transcript

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.

Delve into advanced Data Analysis Expressions (DAX) concepts and Power BI capabilities with Extreme DAX, designed to elevate your skills in Microsoft's Business Intelligence tools. This book guides you through solving intricate business problems, improving your reporting, and leveraging data modeling principles to their fullest potential. What this Book will help me do Master advanced DAX functions and leverage their full potential in data analysis. Develop a solid understanding of context and filtering within Power BI models. Employ strategies for dynamic visualizations and secure data access via row-level security. Apply financial DAX functions for precise investment evaluations and forecasts. Utilize alternative calendars and advanced time-intelligence for comprehensive temporal analyses. Author(s) Michiel Rozema and Henk Vlootman bring decades of deep experience in data analytics and business intelligence to your learning journey. Both authors are seasoned practitioners in using DAX and Microsoft BI tools, with numerous practical deployments of their expertise in business solutions. Their approachable writing reflects their teaching style, ensuring you can easily grasp even challenging concepts. This book combines their comprehensive technical knowledge with real-world, hands-on examples, offering an invaluable resource for refining your skills. Who is it for? This book is perfect for intermediate to advanced analysts who have a foundational knowledge of DAX and Power BI and wish to deepen their expertise. If you are striving to improve performance and accuracy in your reports or aiming to handle advanced modeling scenarios, this book is for you. Prior experience with DAX, Power BI, or equivalent analytical tools is recommended to maximize the benefit. Whether you're a business analyst, data professional, or enthusiast, this book will elevate your analytical capabilities to new heights.