Stacked Data Podcast

🔍 𝐖𝐢𝐭𝐡 𝐭𝐡𝐞 𝐫𝐢𝐬𝐞 𝐨𝐟 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 𝐢𝐧 𝐭𝐡𝐞 𝐥𝐚𝐬𝐭 𝐲𝐞𝐚𝐫, 𝐀𝐈/𝐌𝐋 𝐡𝐚𝐬 𝐛𝐞𝐞𝐧 𝐩𝐮𝐭 𝐨𝐧 𝐚 𝐩𝐞𝐝𝐞𝐬𝐭𝐚𝐥 𝐚𝐬 𝐭𝐡𝐞 𝐟𝐮𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐦𝐮𝐬𝐭-𝐡𝐚𝐯𝐞 𝐟𝐨𝐫 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬, 𝐛𝐮𝐭 𝐜𝐨𝐮𝐥𝐝 𝐭𝐡𝐢𝐬 𝐚𝐬𝐬𝐮𝐦𝐩𝐭𝐢𝐨𝐧 𝐛𝐞 𝐝𝐨𝐢𝐧𝐠 𝐦𝐨𝐫𝐞 𝐡𝐚𝐫𝐦 𝐭𝐡𝐚𝐧 𝐠𝐨𝐨𝐝?🎧 In this episode of The Stacked Data Podcast, we dive deep into understanding the strategies for implementing ML/DS effectively and avoiding common pitfalls that lead to a lack of ROI and missed opportunities.👥 Join me as I chat with Irina Ioana Brudaru, the Head of Data from Element Insurance AG. Irina has had an incredible career, including a stint at Google; her passion for this subject is infectious. Tune in to uncover insights into:➡️ What is ML & AI, and how are they linked/differentiated?➡️ How can a business determine the right time to incorporate machine learning and data science into its operations?➡️ Signs indicating a company is ready to bring in ML expertise.➡️ Strategies for building a team and infrastructure ready to utilise ML/DS projects.➡️ Common pitfalls when launching DS/ML capabilities and their consequences.➡️ We touch on some of the latest tools that are breaking down the barriers to advanced ML like Infer➡️ Balancing immediate gains with a long-term vision in developing ML products.🎙️ Tune in and I’d love you to give us a FOLLOW to hear all the great conversation we have coming up!

🌟 New episode on The Stacked Podcast featuring an incredible guest, Joao, the Head of Data at Plum. We dive deep into the Challenges of Building a Data Team in a Start-up. 🎙️✨🌐 Introduction: In this episode, we take a journey with Joao, exploring key milestones in his career that led him to leadership in the dynamic world of data.🚀 Building from the Ground Up: When stepping into a scale-up as the Head of Data. Where do you begin? Joao shares his insights into the crucial first steps and priorities on the to-do list.💻 Tech Talk: Tech is the backbone of any data team. Joao discusses the overwhelming choices data leaders face when building their tech stack and how technology influences the hiring process.🤝 Hiring Hacks:Hiring isn't just about filling roles; it's about building a successful team. Joao sheds light on how to decide which roles to hire, the crucial skill sets for an early data team, and how roles evolve as the function matures.🌐 Cultivating a Data Culture: Tech and team are essential, but how is it all perceived by the business? Joao shares his insights on growing a data culture in a start-up, what defines a good data culture, and common pitfalls to avoid.📈 Measuring Impact:How do you know if your team is making a real impact? Joao discusses the metrics of success, showcasing value to secure more resources and bigger opportunities.💡 Facing Challenges:Joao opens up about the common challenge of being under-resourced as a Data Leader in a rapidly growing organisation and how to tackle it head-on.🚀 Beyond the Horizon:Explore with Joao the additional challenges Data Leaders in start-ups often face, gaining valuable insights into navigating the dynamic landscape.Here is the link Joao spoke about - https://towardsdatascience.com/how-i-won-singapores-gpt-4-prompt-engineering-competition-34c195a93d41Tune in now 🎧And give us a follow and a rating, your feedback means the world to me!✨ Don't miss out – it's a knowledge-packed episode you won't want to skip! 🚀🌟#DataLeadership #Startups #PodcastLaunch #TechTalks

𝐓𝐡𝐞 𝐡𝐚𝐫𝐝𝐞𝐬𝐭 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞 𝐟𝐚𝐜𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐓𝐞𝐚𝐦𝐬 𝐢𝐬 𝐭𝐡𝐞 𝐚𝐝𝐨𝐩𝐭𝐢𝐨𝐧 𝐨𝐟 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬-𝐰𝐢𝐝𝐞 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐢𝐦𝐩𝐚𝐜𝐭!🚀 This week on the Stacked Data Podcast, I had the pleasure of sitting down with McKinley Muir Hyden, the Director of Data Value & Strategy at the Financial Times. We dive into the critical topics of data adoption and culture change in today's data-driven world.🌐 McKinley brings a wealth of experience, and we kick off the discussion by exploring her journey into the realm of data and her current unique role at the FT. McKinley’s role revolves around bridging the chasm between data and the business. It's about ensuring alignment and, most importantly, measuring the impact of the data team's efforts as well as building the FT’s Data Academy.𝚆̲𝚎̲ ̲𝚌̲𝚘̲𝚟̲𝚎̲𝚛̲:̲🔍 How McKinley navigates the challenge of bridging the gap between data and business.🤔 Reasons for lack of data adoption and the strategies employed to combat them.🌟 McKinley's role is unique, and we delve into the origins of this specialized position and the objectives she aims to achieve. 📊 What exactly is a data culture and why is it crucial for organizations?📏 How McKinley measures and evaluates data culture.🚀 The challenges encountered in creating a robust data culture.If you are looking to build and foster a data culture and inspire trust to ensure analytics impact this is not one to miss. McKinley shares her strategies and approaches as well as the common pit falls and blockers such as; Awareness, Discoverability, and Understanding.Tune in and give us a FOLLOW, like or share! 🙌 Stay tuned for more inspiring conversations, and don't forget to subscribe! hashtaghashtag#DataCulture hashtag#DataAdoption hashtag#Podcast hashtag#DataInnovation hashtag#StackedDataPodcast

In our latest episode of The Stacked Data podcast, we're thrilled to have Thomas, CEO and co-founder of Tasman Analytics, sharing insights on "Maximizing ROI in Data & Analytics."🔍 Episode Highlights: Uncover      the common reasons why data teams are often perceived as cost centers. Learn      strategies to transform your data team into a value generator. Dive      into the keys to driving ROI for your analytics team. Discover      where teams typically go wrong in maximizing the value of their data. Gain      expert advice on crafting a clear strategy for a high ROI.🎙️ Introduction: Get to know Thomas and Tasman, exploring their background and journey in the dynamic world of data.💼 Data Team Transformation: Explore the challenges of data teams being seen as cost centers and learn how to assess if your team is a cost centre or value adder.📈 Driving ROI:Thomas shares valuable insights on evaluating, strategizing, and ensuring your team drives a positive ROI.🚀 Workload Management:Learn effective ways to manage the immense workload on data teams and optimize team efficiency.🌟 Avoiding Common Pitfalls: Discover the biggest mistakes data teams make and get expert advice on resolving them.

In this episode on Stacked Data Podcast, we explored how the team at Carbon Underwriting use  the modern data stack to innovate and revolutionized the way they operate in a the traditional industry of Insurance, allowing them to swiftly deliver top-tier data products that can exponentially improve work in the insurance space.We dive into there leading data product “Graphene”, taking an old school Excel reporting, consolidating this all in a common data model and surfacing an analytics layer on top! This allows there underwriters to deliver work at a much faster rate.This episode is all about how to build teams, infrastructure and processes to create data products that are truly impactful.Key Takeaways:Start with your customer: Work closely with your stakeholders to understand their pain points and mission, from here you can start to design a data product that can enable them.The power of leveraging the modern data stack: The use of cutting-edge technology has transformed there data infrastructure, enabling faster, more agile operations.Accelerating product delivery: How this modern approach has supercharged our ability to bring data products to the market at an unprecedented pace.Overcoming challenges with efficiency: We discussed the hurdles faced and the strategies employed by the team to harness the potential of the modern data stack effectively.

AI and LLM’s have soared into the mainstream, but did you know @Cleo has been pioneering in this space for years?Join me on @stacked as I dive into conversation with Andrea, the Lead Analytics Engineer at Cleo. He unveils his fascinating journey from Data Science to Analytics Engineering, a transition many can resonate with. Discover how Analytics Engineering is transforming data reliability, accuracy, and velocity to drive invaluable insights for Celo.🎙️ Highlights Include:1) The DS to AE Journey: Spending 70% of his time transforming data, Andrea noticed a lack of clear consistency in data quality—a common post-DS hype challenge.2) Rise of AE and Data Modeling: In a world with more complex data, more analysts, and a growing push to be "data-driven," Analytics Engineers are recognized for their crucial role in creating high-quality, trustworthy modeled data.3) Justifying AE Team Costs: Cleo treats Analytics Engineering like a standard Product quad, aligning OKRs with stakeholders' needs and measuring success metrics.4) Automated PR Reviews: Cleo's team ensures efficiency and data quality by implementing an automated PR review system in dbt, scoring queries out of 100% and providing valuable recommendations.Andrea shares even more insights in the episode! If you're curious about what Analytics Engineering truly entails, the challenges it faces, and where the sector is heading, this episode is a must-listen.

This week on The Stacked Data Podcast, I had the pleasure of hosting Simone Spinalbelli from Phoenix Group.Simone, the Head of Analytics Engineering, shared invaluable insights into implementing DataOps, taking us beyond the standard definitions. He delved into a real narrative on how Phoenix Group successfully implemented a DataOps strategy, unblocking vast and diverse data, effectively processing, analyzing, and leveraging it for business insights and decisions to drive them forward.Simone’s key integrations in their data environment include:🚀 Agile Working: Going beyond the familiar Agile methodology, Simone emphasized the significance of focusing on people and interactions over rigid processes. Continuous collaboration with end-users remains a priority, fostering an adaptive and responsive work culture.🔧 DevOps Integration: By embracing DevOps tooling and software engineering practices, Phoenix Group unlocked critical functionalities such as CI/CD pipelines, version control, and robust testing mechanisms.🛠️ Pipelines and Control Gates: Developing pipelines with controlled gates and integrated tests ensures the validation of business logic and a smooth flow of data throughout the process.DataOps, as Simone highlighted, is not just a methodology but a proactive approach that enables a shift from reactive practices. It provides a secure and structured framework for the entire data team, allowing accelerated and more efficient work processes.

In Richard's own words, a data platform serves as a horizontal service—a team and platform that supports various vertical pillars across a business, enabling them to operate as efficiently as possible.Large enterprises are often notorious for lacking speed, agility, and efficiency. Richard tackled this challenge head-on at VMO2 by combining cutting-edge technology with impeccable processes to construct a data platform that fuels exponential growth.Richard's data platform team has achieved a remarkable 10x impact across an organization, with over 200+ engineers and analysts using the platform daily to drive business value.Tune in now to hear more, and you'll discover: How to structure a team to successfully deliver such a wide-ranging project. The      Return on Investment (ROI) of a data platform. The most significant challenges in building an enterprise data platform. The essential skills required for successful execution. Striking the right balance between technical expertise and soft skills as a data leader.SEE THE EXACT QUESTIONS AT THESE TIMES2.00 Richards's background and experience5.50 What is a data platform10.40 What is the end goal of a data platform how does it facilitate ROI13.30 How do you structure a team to deliver a data platform at scale19.00 What have been your biggest challenges and lessons on this journey25.00 if you could do it all again what would you do differently27.22 How do you have the agility to navigate the ever-evolving landscape24.00 How do you use your tools and platform to “level up “ the whole organization41.20 Quick fire questions

🌱 Our planet is getting warmer, and the undeniable impact of climate change is a stark reminder that we all need to take action. But as individuals, it's easy to feel powerless in the face of such a monumental challenge. That's where modern data teams come in, with the potential to drive positive change and make businesses greener and more efficient.🎙 In our latest episode of the The Stacked Data Podcast, we dive headfirst into the world of Carbon Analytics, a space filled with untapped potential. Join me as I sit down with the passionate advocate for sustainability, Nina Anderson from Infinite Lambda. Nina is on a mission to empower data professionals and shed light on the transformative power of Carbon Analytics.Why Carbon Analytics? The Essential Business ImperativeYou might be wondering, "Why does Carbon Analytics matter for my business?" Nina breaks it down for us in this insightful episode, outlining key reasons why it's no longer a luxury but an essential:0️⃣ NET ZERO Targets: As the world races towards carbon neutrality, understanding your environmental impact is crucial to achieving NET ZERO targets.📜 Regulation Change: Regulations around carbon emissions are evolving rapidly. Being prepared is not just advisable; it's necessary.💸 Cost Reduction: Carbon Analytics can uncover hidden efficiencies and cost-saving opportunities in your operations.💰 Competitive Advantage: Sustainability is a competitive differentiator. Embrace Carbon Analytics to stay ahead of the curve.😇 Customer Satisfaction: Modern consumers favor businesses that prioritize sustainability. Happy customers mean better business.We also explore practical examples of how you can leverage your own modern data stack to start generating actionable insights.Did you know that the National Grid provides detailed data on the carbon intensity of electricity across regions? When combined in your Data Warehouse with your electricity consumption, you can quickly assess your carbon impact. Surface this in your BI tool per sale and you might be able to start shifting a culture! ⚡A Wealth of Insight for Sustainability AdvocatesThis episode is full of knowledge about an emerging and important space. If you're passionate about sustainability and committed to making a positive impact, this is an episode you can't afford to miss.

Is dbt lowering the bar for data modelling having a negative effect on data quality?Data modeling is the cornerstone of data-driven decision-making. It's the art of translating a business's concepts, definitions, and activities into data structures. When done right, it empowers you to answer the crucial "why" questions by capturing the "who, when, how, and what" of a business. Moreover, it paves the way for future efficiency, reusability, and data consistency.So, why do so many organizations still overlook the importance of a robust data modelling strategy?This week, on The Stacked Data Podcast, I have the pleasure of hosting Rob, the Head of Data Product at Miro. Rob delves into the critical significance of data modelling, the common pitfalls to avoid, and shares invaluable insights on how to approach data modelling and effectively lead teams of data modellers.🚀 Key Takeaways: Know      Your Critical Concepts and Attributes: Define and design them upfront,      ensuring alignment across your organization. Regularly revisit and expand      your list of conceptual definitions. Invest      in Ongoing Education: Constantly enhance the skills of your data      contributors. While not everyone needs to be a data expert, analysts      should grasp architectural principles, master their tools, and engage in      continuous learning. Rob, for instance, dedicates 5-7% of team time to      Learning and Development (L&D) activities. Maintain      Your Models Like a Garden: Regularly dedicate time to review, refine,      refactor, clean, upgrade, and promote your data models. This should be a      shared responsibility and part of your sprint routine.