The Stacked Data Podcast

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.

This week I’m joined by Leon Tang the VP of Data from @fresha. Leon shares his unique career, from frying fish and chips to building and leading data teams at some of the world's most well-known tech companies.Leon stresses the importance of relationship building and business understanding as a key attributes to success in data.We speak about how to make sure your data team is a driver of the business and not a support function.I dive into how to excel in your data career and have impact on the business everyday!🌐 What You'll Learn:How to break from a “Senior” into LeadershipHow to build your toolkit to succeed in dataHow to foster a data driven culture to set your team up for success.Top tips for interviewing and a successful career in data!The Stacked Data Podcast isn't just about technology; it's about the stories, experiences, and lessons that drive innovation in the data landscape. Whether you're a seasoned data professional or simply curious about the future of data, this conversation offers a wealth of knowledge and inspiration.Please give us a follow as we have lots more episodes coming!We are always looking for feedback or topics you'd like to hear about. Please reach out!

Sandeep Mehta, the Engineering lead for data platforms at Dojo, discusses the exciting shift towards a self-serve data platform using the data mesh strategy. He reveals how Dojo handles an astonishing £50 billion in transactions daily and emphasizes empowering domain teams to enhance data ownership. Sandeep shares insights on tackling data debt, aligning business strategies, and fostering collaboration for faster product development cycles. He also offers valuable interview tips for aspiring data professionals, highlighting the importance of ownership and deep understanding in the field.

I'm joined by Sami Rahman as we dive deep into the fascinating world of Modern Data, covering topics around maximising your stack and the power of a semantic layer.In this episode, I had the privilege of sitting down with Sami, who has a truly unique path into data. Together, we explore the intricacies of the semantic layer and modern data stack, unearthing its transformative potential and its vital role in modern data architecture.🌐 What You'll Learn:An honest insight into Sami unique career journey (including a stint in ANTI-TERRORISM)Insights on harnessing the power of the modern data stack and a semantic layer to unlock data's true potential.Real-world applications and success stories that shed light on its impact.Practical tips and strategies to navigate the challenges of implementing a robust semantic layer.Top tips for interviewing and a successful career in data!This podcast isn't just about technology; it's about the stories, experiences, and lessons that drive innovation in the data landscape. Whether you're a seasoned data professional or simply curious about the future of data, this conversation offers a wealth of knowledge and inspiration.Please give us a follow as we have lots more episodes coming!We are always looking for feedback or topics you'd like to hear about. Please reach out!

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