The Data Product Management In Action podcast, brought to you by Soda and executive producer Scott Hirleman, is a platform for data product management practitioners to share insights and experiences.  In Episode 24 of Data Product Management in Action, our host Nick Zervoudis is joined byTefi Trabuchi, Data Platform Product Manager at SumUp, to discuss her experience transforming a reactive data platform team into a user-focused, strategy-driven powerhouse. Tefi shares how she tackled challenges like burnout, prioritization struggles, and resistance to product practices such as user research and OKRs. She highlights the pivotal role of user interviews in shifting mindsets and the delicate balance between reducing risk, ensuring compliance, and driving innovation. Tefi also emphasizes the value of clear communication and curiosity when working in highly technical domains. This episode offers practical insights for product managers navigating the complexities of data, AI, and machine learning. About our host Nick Zervoudis: Nick is Head of Product at CKDelta, an AI software business within the CKHutchison Holdings group. Nick oversees a portfolio of data products and works with sister companies to uncover new opportunities to innovate using data,analytics, and machine learning.Nick's career has revolved around data and advanced analytics from day one,having worked as an analyst, consultant, product manager, and instructor for startups, SMEs, and enterprises including PepsiCo, Sainsbury's, Lloyds BankingGroup, IKEA, Capgemini Invent, BrainStation, QuantSpark, and Hg Capital. Nick is also the co-host ofLondon's Data Product Management meetup, andspeaks & writes regularly about data & AI product management. Connect with Nick on LinkedIn. About our guest Tefi Trabuchi:Tefi is a Data Platform Product Manager at SumUp, where she focuses on making sure our data tools are not only secure and efficient but also provide a smooth user experience for our internal teams. Before this, she led the development of an in-house Data Observability tool at Glovo, introducing governance rules and SLAs for key datasets. Tefi enjoys working closely with teams to create practical solutions that make accessing and using data easier and more intuitive, so everyone can make more informed decisions faster. Connect with Tefi 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!              

Help us become the #1 Data Podcast by leaving a rating & review! We are 67 reviews away! No fluff, no jargon; just the essentials to kick-start your data analyst career in 2025 with a strategy built for success. 💌 Join 30k+ 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 00:16 Understanding Different Data Roles 01:48 Essential Data Skills and Tools 04:36 Building Projects to Showcase Skills 08:13 Creating a Portfolio for Your Projects 09:06 Optimizing LinkedIn and Resume 10:46 Applying for Jobs and Networking 12:38 Preparing for Interviews 14:25 Conclusion and Final Tips Join the Bootcamp: Data Career Jumpstart Browse Data Jobs: Find a Data Job Must-Learn Skills for Aspiring Analysts: Watch on YouTube Find Free Datasets for Practice: Watch on YouTube Stratascratch for SQL Practice: Visit Stratascratch Prepare for Interviews: Interview Simulator 🔗 CONNECT WITH AVERY 🎥 YouTube Channel 🤝 LinkedIn 📸 Instagram 🎵 TikTok 💻 Website 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

We’re improving DataFramed, and we need your help! We want to hear what you have to say about the show, and how we can make it more enjoyable for you—find out more here. We’re often caught chasing the dream of “self-serve” data—a place where data empowers stakeholders to answer their questions without a data expert at every turn. But what does it take to reach that point? How do you shape tools that empower teams to explore and act on data without the usual bottlenecks? And with the growing presence of natural language tools and AI, is true self-service within reach, or is there still more to the journey? Sameer Al-Sakran is the CEO at Metabase, a low-code self-service analytics company. Sameer has a background in both data science and data engineering so he's got a practitioner's perspective as well as executive insight. Previously, he was CTO at Expa and Blackjet, and the founder of SimpleHadoop and Adopilot. In the episode, Richie and Sameer explore self-serve analytics, the evolution of data tools, GenAI vs AI agents, semantic layers, the challenges of implementing self-serve analytics, the problem with data-driven culture, encouraging efficiency in data teams, the parallels between UX and data projects, exciting trends in analytics, and much more. Links Mentioned in the Show: MetabaseConnect with SameerArticles from Metabase on jargon, information budgets, analytics mistakes, and data model mistakesCourse: Introduction to Data CultureRelated Episode: Towards Self-Service Data Engineering with Taylor Brown, Co-Founder and COO at FivetranRewatch Sessions from RADAR: Forward 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

Em uma conversa inspiradora em parceria com a comunidade Data Hackers, nosso co-fundador Gabriel Lages, é convidado como host no terceiro episódio do about data, o podcast oficial do Zoho Analytics !

Ele recebe Thiago Cardoso, Diretor de Dados e AI do iFood. Juntos, eles discutem como a personalização e o uso inteligente de dados estão transformando o atendimento, trazendo um toque humano e individualizado em larga escala.

Curioso para saber como tudo isso está moldando o mercado? Então, dá o play e mergulhe nessa conversa que conecta o presente ao futuro da tecnologia e da personalização do atendimento ao cliente.

Participantes:

Thiago Cardoso, Diretor de Dados e AI do iFood

Gabriel Lages, Diretor de Data & Analytics Sr. na Hotmart e Co-fundador da Data Hackers

This is a talk about how we thought we had Big Data, and we built everything planning for Big Data, but then it turns out we didn't have Big Data, and while that's nice and fun and seems more chill, it's actually ruining everything, and I am here asking you to please help us figure out what we are supposed to do now.

📓 Resources Big Data is Dead: https://motherduck.com/blog/big-data-... Small Data Manifesto: https://motherduck.com/blog/small-dat... Is Excel Immortal?: https://benn.substack.com/p/is-excel-immortal Small Data SF: https://www.smalldatasf.com/

➡️ Follow Us LinkedIn: / motherduck
X/Twitter : / motherduck
Blog: https://motherduck.com/blog/

Mode founder David Wheeler challenges the data industry's obsession with "big data," arguing that most companies are actually working with "small data," and our tools are failing us. This talk deconstructs the common sales narrative for BI tools, exposing why the promise of finding game-changing insights through data exploration often falls flat. If you've ever built dashboards nobody uses or wondered why your analytics platform doesn't deliver on its promises, this is a must-watch reality check on the modern data stack.

We explore the standard BI demo, where an analyst uncovers a critical insight by drilling into event data. This story sells tools like Tableau and Power BI, but it rarely reflects reality, leading to a "revolving door of BI" as companies swap tools every few years. Discover why the narrative of the intrepid analyst finding a needle in the haystack only works in movies and how this disconnect creates a cycle of failed data initiatives and unused "trashboards."

The presentation traces our belief that "data is the new oil" back to the early 2010s, with examples from Target's predictive analytics and Facebook's growth hacking. However, these successes were built on truly massive datasets. For most businesses, analyzing small data results in noisy charts that offer vague "directional vibes" rather than clear, actionable insights. We contrast the promise of big data analytics with the practical challenges of small data interpretation.

Finally, learn actionable strategies for extracting real value from the data you actually have. We argue that BI tools should shift focus from data exploration to data interpretation, helping users understand what their charts actually mean. Learn why "doing things that don't scale," like manually analyzing individual customer journeys, can be more effective than complex models for small datasets. This talk offers a new perspective for data scientists, analysts, and developers looking for better data analysis techniques beyond the big data hype.

Cedric Chin runs Commoncog—a publication about accelerating business expertise. He joins Tristan to talk about the analytics development lifecycle, how organizations value (or misvalue) data, and why "data teams are not some IT helpdesk to be ignored."   For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

The Inside Economics team assesses the inflation statistics, and why there is no going back to the where prices were prior to fallout from the pandemic and Russian war in Ukraine. And while inflation has largely been quelled, President-elect Trump’s tariff, immigration and other policies threaten to fan inflation anew.  There is also the stats game and listener questions.   Guest: Matt Colyar - Assistant Director, Moody's Analytics 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' @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.

Over the last decade, Big Data was everywhere. Let's set the record straight on what is and isn't Big Data. We have been consumed by a conversation about data volumes when we should focus more on the immediate task at hand: Simplifying our work.

Some of us may have Big Data, but our quest to derive insights from it is measured in small slices of work that fit on your laptop or in your hand. Easy data is here— let's make the most of it.

📓 Resources Big Data is Dead: https://motherduck.com/blog/big-data-is-dead/ Small Data Manifesto: https://motherduck.com/blog/small-data-manifesto/ Small Data SF: https://www.smalldatasf.com/

➡️ Follow Us LinkedIn: https://linkedin.com/company/motherduck X/Twitter : https://twitter.com/motherduck Blog: https://motherduck.com/blog/

Explore the "Small Data" movement, a counter-narrative to the prevailing big data conference hype. This talk challenges the assumption that data scale is the most important feature of every workload, defining big data as any dataset too large for a single machine. We'll unpack why this distinction is crucial for modern data engineering and analytics, setting the stage for a new perspective on data architecture.

Delve into the history of big data systems, starting with the non-linear hardware costs that plagued early data practitioners. Discover how Google's foundational papers on GFS, MapReduce, and Bigtable led to the creation of Hadoop, fundamentally changing how we scale data processing. We'll break down the "big data tax"—the inherent latency and system complexity overhead required for distributed systems to function, a critical concept for anyone evaluating data platforms.

Learn about the architectural cornerstone of the modern cloud data warehouse: the separation of storage and compute. This design, popularized by systems like Snowflake and Google BigQuery, allows storage to scale almost infinitely while compute resources are provisioned on-demand. Understand how this model paved the way for massive data lakes but also introduced new complexities and cost considerations that are often overlooked.

We examine the cracks appearing in the big data paradigm, especially for OLAP workloads. While systems like Snowflake are still dominant, the rise of powerful alternatives like DuckDB signals a shift. We reveal the hidden costs of big data analytics, exemplified by a petabyte-scale query costing nearly $6,000, and argue that for most use cases, it's too expensive to run computations over massive datasets.

The key to efficient data processing isn't your total data size, but the size of your "hot data" or working set. This talk argues that the revenge of the single node is here, as modern hardware can often handle the actual data queried without the overhead of the big data tax. This is a crucial optimization technique for reducing cost and improving performance in any data warehouse.

Discover the core principles for designing systems in a post-big data world. We'll show that since only 1 in 500 users run true big data queries, prioritizing simplicity over premature scaling is key. For low latency, process data close to the user with tools like DuckDB and SQLite. This local-first approach offers a compelling alternative to cloud-centric models, enabling faster, more cost-effective, and innovative data architectures.

Power BI is one of the hottest data analysis tools in today's job market. In this episode, Aaron Parry will talk about why Power BI is in such high demand, the things companies are hiring Power BI experts for, and where you should focus if you want to add Power BI to your bag of tricks.  You'll leave the show with a deeper understanding of why Power BI is so valuable, what it can do, and where you should focus if you want to build Power BI skills that will advance your career. What You'll Learn: What makes Power BI such a valuable skill set for so many roles Some of the most valuable ways you can use Power BI on the job Where you should focus if you want to build job-ready Power BI skills   Register for free to be part of the next live session: https://bit.ly/3XB3A8b   About our guests: Aaron is a professional analytics consultant and Microsoft Power BI expert, with 10+ years working in business intelligence and marketing analytics. He's an instructor, coach and mentor for aspiring analysts, and has deep experience helping companies develop and implement full-stack BI solutions. Follow Aaron on LinkedIn

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

Jeremy Forman joins us to open up about the hurdles– and successes that come with building data products for pharmaceutical companies. Although he’s new to Pfizer, Jeremy has years of experience leading data teams at organizations like Seagen and the Bill and Melinda Gates Foundation. He currently serves in a more specialized role in Pfizer’s R&D department, building AI and analytical data products for scientists and researchers. .

Jeremy gave us a good luck at his team makeup, and in particular, how his data product analysts and UX designers work with pharmaceutical scientists and domain experts to build data-driven solutions..  We talked a good deal about how and when UX design plays a role in Pfizer’s data products, including a GenAI-based application they recently launched internally.  

Highlights/ Skip to:

(1:26) Jeremy's background in analytics and transition into working for Pfizer (2:42) Building an effective AI analytics and data team for pharma R&D (5:20) How Pfizer finds data products managers (8:03) Jeremy's philosophy behind building data products and how he adapts it to Pfizer (12:32) The moment Jeremy heard a Pfizer end-user use product management research language and why it mattered (13:55) How Jeremy's technical team members work with UX designers (18:00) The challenges that come with producing data products in the medical field (23:02) How to justify spending the budget on UX design for data products (24:59) The results we've seen having UX design work on AI / GenAI products (25:53) What Jeremy learned at the  Bill & Melinda Gates Foundation with regards to UX and its impact on him now (28:22) Managing the "rough dance" between data science and UX (33:22) Breaking down Jeremy's GenAI application demo from CDIOQ (36:02) What would Jeremy prioritize right now if his team got additional funding (38:48) Advice Jeremy would have given himself 10 years ago (40:46) Where you can find more from Jeremy

Quotes from Today’s Episode

“We have stream-aligned squads focused on specific areas such as regulatory, safety and quality, or oncology research. That’s so we can create functional career pathing and limit context switching and fragmentation. They can become experts in their particular area and build a culture within that small team. It’s difficult to build good [pharma] data products. You need to understand the domain you’re supporting. You can’t take somebody with a financial background and put them in an Omics situation. It just doesn’t work. And we have a lot of the scars, and the failures to prove that.” - Jeremy Forman (4:12) “You have to have the product mindset to deliver the value and the promise of AI data analytics. I think small, independent, autonomous, empowered squads with a product leader is the only way that you can iterate fast enough with [pharma data products].” - Jeremy Forman (8:46) “The biggest challenge is when we say data products. It means a lot of different things to a lot of different people, and it’s difficult to articulate what a data product is. Is it a view in a database? Is it a table? Is it a query? We’re all talking about it in different terms, and nobody’s actually delivering data products.” - Jeremy Forman (10:53) “I think when we’re talking about [data products] there’s some type of data asset that has value to an end-user, versus a report or an algorithm. I think it’s even hard for UX people to really understand how to think about an actual data product. I think it’s hard for people to conceptualize, how do we do design around that? It’s one of the areas I think I’ve seen the biggest challenges, and I think some of the areas we’ve learned the most. If you build a data product, it’s not accurate, and people are getting results that are incomplete… people will abandon it quickly.” - Jeremy Forman (15:56) “ I think that UX design and AI development or data science work is a magical partnership, but they often don’t know how to work with each other. That’s been a challenge, but I think investing in that has been critical to us. Even though we’ve had struggles… I think we’ve also done a good job of understanding the [user] experience and impact that we want to have. The prototype we shared [at CDIOQ] is driven by user experience and trying to get information in the hands of the research organization to understand some portfolio types of decisions that have been made in the past. And it’s been really successful.” - Jeremy Forman (24:59) “If you’re having technology conversations with your business users, and you’re focused only the technology output, you’re just building reports. [After adopting If we’re having technology conversations with our business users and only focused on the technology output, we’re just building reports. [After we adopted  a human-centered design approach], it was talking [with end-users] about outcomes, value, and adoption. Having that resource transformed the conversation, and I felt like our quality went up. I felt like our output went down, but our impact went up. [End-users] loved the tools, and that wasn’t what was happening before… I credit a lot of that to the human-centered design team.” - Jeremy Forman (26:39) “When you’re thinking about automation through machine learning or building algorithms for [clinical trial analysis], it becomes a harder dance between data scientists and human-centered design. I think there’s a lack of appreciation and understanding of what UX can do. Human-centered design is an empathy-driven understanding of users’ experience, their work, their workflow, and the challenges they have. I don’t think there’s an appreciation of that skill set.” - Jeremy Forman (29:20) “Are people excited about it? Is there value? Are we hearing positive things? Do they want us to continue? That’s really how I’ve been judging success. Is it saving people time, and do they want to continue to use it? They want to continue to invest in it. They want to take their time as end-users, to help with testing, helping to refine it. Those are the indicators. We’re not generating revenue, so what does the adoption look like? Are people excited about it? Are they telling friends? Do they want more? When I hear that the ten people [who were initial users] are happy and that they think it should be rolled out to the whole broader audience, I think that’s a good sign.” - Jeremy Forman (35:19)

Links Referenced LinkedIn: https://www.linkedin.com/in/jeremy-forman-6b982710/

We’re improving DataFramed, and we need your help! We want to hear what you have to say about the show, and how we can make it more enjoyable for you—find out more here. Data is no longer just for coders. With the rise of low-code tools, more people across organizations can access data insights without needing programming skills. But how can companies leverage these tools effectively? And what steps should they take to integrate them into existing workflows while upskilling their teams?  Michael Berthold is CEO and co-founder at KNIME, an open source data analytics company. He has more than 25 years of experience in data science, working in academia, most recently as a full professor at Konstanz University (Germany) and previously at University of California (Berkeley) and Carnegie Mellon, and in industry at Intel’s Neural Network Group, Utopy, and Tripos. Michael has published extensively on data analytics, machine learning, and artificial intelligence. In the episode, Adel and Michael explore low-code data science, the adoption of low-code data tools, the evolution of data science workflows, upskilling, low-code and code collaboration, data literacy, integration with AI and GenAI tools, the future of low-code data tools and much more.  Links Mentioned in the Show: KNIMEConnect with MichaelCode Along: Low-Code Data Science and Analytics with KNIMECourse: Introduction to KNIMERelated Episode: No-Code LLMs In Practice with Birago Jones & Karthik Dinakar, CEO & CTO at Pienso 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

In this episode, host Jason Foster sits down with Paula Bobbett, Chief Digital Officer at Boots. They discuss her extensive digital experience, which includes roles at Dixon's Carphone, British Airways, Debenhams and Avon.   They also explore the importance of an omnichannel strategy that bridges online and in-store customer experiences, using customer insights and AI tools. *****    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.  

Chris Avallone, Head of Merchant Banking at Amherst, joins the Inside Economics crew to discuss the housing market.  The group examines the "lock-in" effect keeping existing homeowners in their homes and the "lock-out" effect preventing aspiring homebuyers from realizing their dreams.  Chris describes a playbook that local governments could use to address zoning and free up the "locked up" housing market.  After a quick stats game, Mark polls the group for their forecasts for when and how the housing market will normalize.  The recording of this podcast took place before the results of the 2024 election. For Cris's paper on the Housing Deficit and Housing Affordability click here Guest: Chris Avallone, Head of Merchant Banking at The Amherst Group 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' @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.

Data Dynamics: Navigating the Role of Data Product Managers The Data Product Management In Action podcast, brought to you by Soda and executive producer Scott Hirleman, is a platform for data product management practitioners to share insights and experiences.  In Episode 22 of Data Product Management in Action, we welcome back our host Michael Toland as he chats with guest Chandan Gadodia. Listen along as  they explore the vital role of data product managers. Discover the distinctions between data, IT, and functional product managers and their contributions to product success. Learn more about key focus areas like data pipelines, quality, and standards, as well as the role of AI in enhancing data quality. Join us for an enlightening discussion on the evolving landscape of data management! About our host Michael Toland: Toland is a Product Management Coach and Consultant with Pathfinder Product, aTest Double Operation. He has worked in product officially since 2016, where he worked at Verizon on large scale system modernizations and migration initiatives for reference data and decision platforms. Outside of his professional career, Michael serves as the Treasurer for the New Leaders Council, mentors fellows with Venture for America, sings in the Columbus Symphony, writes satire posts for his blog Dignified Productor Test Double, depending on the topic, and is excited to be chatting with folks on Data Product Management. Connect with Michael on LinkedIn. About our guest Chadan Gadodia: With over a decade serving in data and analytics, Chandan Gadodia, a seasoned Data Product Manager, has held several roles within the industry. From managing internal data assets to overseeing global data products, Chandan's passion for learning from diverse perspectives, connecting with global colleagues, and contributing to growth for himself and his organization has been constant. He is a big advocate for data literacy and strongly believes in data driven business outcomes. Connect with Chadan 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!              

Send us a text 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.

Synopsis What if hiring wasn’t about flipping through endless CVs but instead focused solely on skills? In this episode of Making Data Simple, we sit down with Tim Freestone, founder of Alooba, the groundbreaking platform revolutionizing how businesses hire for analytics, data science, and engineering roles. Tim shares how Alooba eliminates bias, speeds up hiring, and ensures candidates are evaluated based on what really matters—their capabilities. From his journey as an economics teacher to leading data teams, Tim’s insights are a must-hear for anyone tackling hiring challenges in today’s competitive job market. Learn how Alooba’s data-driven approach is transforming recruitment and why the future of hiring might just leave resumes in the dust. Show Notes 4:46 – How do you go from economics teacher to head of business intelligence?7:53 – Do CV’s matter anymore?13:22 – What business problem is Alooba solving?16:05 – Do you have any data that supports your theory?19:01 – Why analytics, data science, data engineering?20:26 - What do you do that others don’t?23:50 – How does Alooba define success?25:42 – Who’s your target client base?32:40 –Is there a customer you can talk about?36:24 – What does Alooba mean?Alooba  Connect with the Team Executive Producer Kate Mayne - 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. 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.

We’re improving DataFramed, and we need your help! We want to hear what you have to say about the show, and how we can make it more enjoyable for you—find out more here. Staying ahead means knowing what’s happening right now—not minutes or hours later. Real-time analytics promises to help teams react faster, make informed choices, and even predict issues before they arise. But implementing these systems is no small feat, and it requires careful alignment between technical capabilities and business needs. How do you ensure that real-time data actually drives impact? And what should organizations consider to make sure their real-time analytics investments lead to tangible benefits? Zuzanna Stamirowska is the CEO of Pathway.com - the fastest data processing engine on the market which makes real-time intelligence possible. Zuzanna is also the author of the state-of-the-art forecasting model for maritime trade published by the National Academy of Sciences of the USA. While working on this project she saw that the digitization of traditional industries was slowed down by the lack of a software infrastructure capable of doing automated reasoning on top of data streams, in real time. This was the spark to launch Pathway. She holds a Master’s degree in Economics and Public Policy from Sciences Po, Ecole Polytechnique, and ENSAE, as well as a PhD in Complexity Science.. Hélène Stanway is Independent Advisor & Consultant at HMLS Consulting Ltd. Hélène is an award-winning and highly effective insurance leader with a proven track record in emerging technologies, innovation, operations, data, change, and digital transformation. Her passion for actively combining the human element, design, and innovation alongside technology has enabled companies in the global insurance market to embrace change by achieving their desired strategic goals, improving processes, increasing efficiency, and deploying relevant tools. With a special passion for IoT and Sensor Technology, Hélène is a perpetual learner, driven to help delegates succeed.  In the episode, Richie, Zuzanna and Hélène explore real-time analytics, their operational impact, use-cases of real-time analytics across industries, the benefits of adopting real-time analytics, the key roles and stakeholders you need to make that happen, operational challenges, strategies for effective adoption, the real-time of the future, common pitfalls, and much more.  Links Mentioned in the Show:

Pathway

Connect with Zuzanna and HélèneLiArticle: What are digital twins and why do we need them?Course: Time Series Analysis in Power BIRelated Episode: How Real Time Data Accelerates Business Outcomes with George TrujilloSign up to RADAR: Forward Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data

Help us become the #1 Data Podcast by leaving a rating & review! We are 67 reviews away! For anyone aiming to break into data analysis, Avery’s roadmap is the ultimate guide. With practical advice and clear steps, this episode sets you up for success in just 100 days. 💌 Join 30k+ 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 01:06 AI Avatars Talk About the Plan 02:04 Learning About Data Roles 03:55 Getting Good at Excel 04:55 Visualizing Data with Tableau 05:54 Learning SQL Basics 06:58 Starting Job Prep Early 07:15 Applying for Jobs Smartly 09:27 Capstone Project: Showing Off Your Skills 11:22 Last Tips and Encouragement 🔗 CONNECT WITH AVERY 🎥 YouTube Channel 🤝 LinkedIn 📸 Instagram 🎵 TikTok 💻 Website 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

Mark joins the podcast from Europe to provide the European perspective on the U.S. election and how the outcome may affect their economies.  The team then dissects the reactions of the stock, bond, and cryptocurrency markets before turning to listener questions.  Marisa asks the group for their views on the vibecession, a term coined by Kyla Scanlon, to explain the disconnect between economists and the general public, and the integrity of economic data.  Cris's dog makes a special guest appearance.   https://genius.com/Alanis-morissette-ironic-lyrics https://kyla.substack.com/p/the-vibecession-the-self-fulfilling   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' @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.

Summary The challenges of integrating all of the tools in the modern data stack has led to a new generation of tools that focus on a fully integrated workflow. At the same time, there have been many approaches to how much of the workflow is driven by code vs. not. Burak Karakan is of the opinion that a fully integrated workflow that is driven entirely by code offers a beneficial and productive means of generating useful analytical outcomes. In this episode he shares how Bruin builds on those opinions and how you can use it to build your own analytics without having to cobble together a suite of tools with conflicting abstractions.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementImagine catching data issues before they snowball into bigger problems. That’s what Datafold’s new Monitors do. With automatic monitoring for cross-database data diffs, schema changes, key metrics, and custom data tests, you can catch discrepancies and anomalies in real time, right at the source. Whether it’s maintaining data integrity or preventing costly mistakes, Datafold Monitors give you the visibility and control you need to keep your entire data stack running smoothly. Want to stop issues before they hit production? Learn more at dataengineeringpodcast.com/datafold today!Your host is Tobias Macey and today I'm interviewing Burak Karakan about the benefits of building code-only data systemsInterview IntroductionHow did you get involved in the area of data management?Can you describe what Bruin is and the story behind it?Who is your target audience?There are numerous tools that address the ETL workflow for analytical data. What are the pain points that you are focused on for your target users?How does a code-only approach to data pipelines help in addressing the pain points of analytical workflows?How might it act as a limiting factor for organizational involvement?Can you describe how Bruin is designed?How have the design and scope of Bruin evolved since you first started working on it?You call out the ability to mix SQL and Python for transformation pipelines. What are the components that allow for that functionality?What are some of the ways that the combination of Python and SQL improves ergonomics of transformation workflows?What are the key features of Bruin that help to streamline the efforts of organizations building analytical systems?Can you describe the workflow of someone going from source data to warehouse and dashboard using Bruin and Ingestr?What are the opportunities for contributions to Bruin and Ingestr to expand their capabilities?What are the most interesting, innovative, or unexpected ways that you have seen Bruin and Ingestr used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Bruin?When is Bruin the wrong choice?What do you have planned for the future of Bruin?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 BruinFivetranStitchIngestrBruin CLIMeltanoSQLGlotdbtSQLMeshPodcast EpisodeSDFPodcast EpisodeAirflowDagsterSnowparkAtlanEvidenceThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Vamos explorar como o Grupo Boticário está desbravando o universo do self-service em BI e AI, democratizando o acesso aos dados e impulsionando a autonomia nas áreas de negócio. 

Neste episódio do Data Hackers — a maior comunidade de AI e Data Science do Brasil-, conheçam Jéssika Ribeiro, Gerente Sênior de Produto de Dados no Grupo Boticário; Matheus Garibalde, Diretor de Produtos de Dados e IA; e Wagner Acorsi Aleixo, Gerente Sênior de Governança e Cultura de Dados no Grupo Boticário; juntos discutem as evoluções mais recentes, os desafios de implementar um sistema de self-service de dados com a evolução do uso de IA na empresa.

Lembrando que você pode encontrar todos os podcasts da comunidade Data Hackers no Spotify, iTunes, Google Podcast, Castbox e muitas outras plataformas. Caso queira, você também pode ouvir o episódio aqui no post mesmo

Falamos no episódio !

Matheus Garibalde, Diretor de Produtos de Dados e IA; Jéssika Ribeiro, Gerente Sênior de Produto de Dados no Grupo Boticário;  Wagner Acorsi Aleixo, Gerente Sênior de Governança e Cultura de Dados no Grupo Boticário; 

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 Gabriel Lages — Co-founder da Data Hackers e Data & Analytics Sr. Director na Hotmart.