This week, I'm joined by Timothy Chan, Head of Data at Statsig. Tim has a fascinating background — he started his career as a scientist developing life-saving drugs before pivoting into data. He went on to become a Staff Data Scientist at Meta and now leads the data function at Statsig, one of the world's leading experimentation platforms, recently acquired by OpenAI. In this episode, we dive into the power of experimentation and how Meta embedded it into every aspect of product development. We unpack: ⚙️ What great experimentation really looks like 🏗️ How to build a world-class experimentation function 💬 How data teams can use experimentation to influence business decisions ⚖️ The balance between speed, rigour, and impact 👥 Why stakeholder collaboration is the true differentiator of high-performing data teams Tim shares brilliant insights from his time at both Meta and Statsig — including how to think about experimentation as a cultural capability, not just a technical one. If you care about driving real business impact with data, this is a must-listen.
𝐁𝐞𝐲𝐨𝐧𝐝 𝐭𝐡𝐞 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞 – 𝐓𝐫𝐚𝐜𝐤𝐢𝐧𝐠 𝐭𝐡𝐞 𝐓𝐫𝐮𝐞 𝐈𝐦𝐩𝐚𝐜𝐭 𝐨𝐟 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 Analytics Engineering has become one of the most in-demand roles for modern data teams in recent years. The role goes far beyond just building clean data pipelines and data models, but how do we actually measure that impact? In today's episode of the Stacked Data Podcast, we're joined by Ross Helenius, Director of Analytics Engineering & AI Transformation Engineering at Mimecast, to unpack one of the most important (and overlooked) questions in data: 👉 What does success look like for Analytics Engineering: beyond the technical? 𝚆̲𝚎̲ ̲𝚎̲𝚡̲𝚙̲𝚕̲𝚘̲𝚛̲𝚎̲:̲ ✅ The true role of Analytics Engineering in modern data teams ✅ Why measuring ROI is so hard and how you can do this ✅ How to define and track impact beyond pipelines, models, and dashboards ✅ Practical KPIs and strategies to showcase business value ✅ Pitfalls to avoid when proving the value of your data function Ross brings deep experience from the intersection of data, engineering, and AI, and offers actionable insights for data leaders and practitioners alike. Whether you're leading a data team, building one, or looking to become a better AE this episode is packed with value.
Digestible. Defensible. Actionable. What does it really take to drive impact with data? Too often, analysts are left wondering: "Why didn't anyone do anything with that insight?" This week on the Stacked Data Podcast, I'm joined by Sam Marks, Director of Analytics & Business Strategy at the Boston Bruins, to explore the gap between finding insights and driving action — and how to close it. Sam shares the Digestible, Defensible, Actionable framework he uses to turn analysis into outcomes, covering: ✅ What makes an insight digestible (and where analysts go wrong) ✅ How to make your work defensible and credible ✅ What separates an interesting insight from an actionable one ✅ How to overcome inaction from stakeholders ✅ Real-world examples of the framework in action This one's packed with tactical advice for any data professional tired of their work getting stuck in slide decks.
In this episode of Stacked Data Podcast, I'm joined by Manou, Head of Data & X at Medik8, to dive into the unique challenges of leading a small data team—and more importantly, how to overcome them. ✅ How do you prioritize when resources are tight? ✅ How can small data teams drive real business impact? ✅ What strategies help in building a strong team culture? Manou shares practical strategies on ensuring data teams stay focused on high-value projects, align with business goals, and maximize their potential—even with limited resources. If you're leading (or part of) a small data team, this one's for you.
🚀 𝐖𝐡𝐲 𝐭𝐡𝐞 𝐒𝐞𝐦𝐚𝐧𝐭𝐢𝐜 𝐋𝐚𝐲𝐞𝐫 𝐢𝐬 𝐭𝐡𝐞 𝐌𝐢𝐬𝐬𝐢𝐧𝐠 𝐏𝐢𝐞𝐜𝐞 𝐢𝐧 𝐃𝐚𝐭𝐚 & 𝐀𝐈 🎙️ Data teams are investing more than ever in AI and analytics, yet many still struggle to make their data truly accessible, consistent, and reliable. One major reason? The semantic layer. Without a curated Semantic Layer, self-serve AI is a pipedream! In the latest episode of the Stacked Data Podcast, I sit down with Sarah Levy [https://www.linkedin.com/in/sarahlevyschreier/], CEO of Euno [https://www.linkedin.com/company/eunoai/], to unpack why the semantic layer is often overlooked—and why it's so crucial for businesses looking to scale AI and analytics effectively. 𝕎𝕖 𝕕𝕚𝕧𝕖 𝕚𝕟𝕥𝕠: 🔹Why so many companies struggle with data consistency and accessibility 🔹What a semantic layer actually is and why it's a game-changer 🔹How it bridges the gap between raw data and real business impact 🔹The challenges of adoption & why many data teams still don't have one 🔹How Euno is tackling this problem head-on If you're working in data and your organisation is pushing for AI, this is a must-listen.
In the world of app-based products, event tracking is the backbone of understanding user behaviour, engagement, and product success. But with so much data available, how do you decide what to track? How do you avoid over-tracking while ensuring your insights drive real business impact? In this episode of the Stacked Data Podcast, I sit down with Matt R, Head of Data at Paired, to break down the art of event tracking—what to track, what to ignore, and how to ensure your analytics strategy is built for action, not overload. 🔥 𝕎𝕙𝕒𝕥 𝕎𝕖 ℂ𝕠𝕧𝕖𝕣: ✅ How to define and prioritize the "right" events to track ✅ Avoiding common pitfalls like over-tracking & inconsistent data ✅ Best practices for documentation, governance, and making event data actionable ✅ How to communicate the importance of tracking to non-technical teams ✅ Practical advice for setting up event tracking in mobile apps from scratch Whether you're a data professional, product manager, or startup founder, this episode will give you the framework to master event tracking and turn data into real insights that drive growth.
In today's fast-moving, data-driven world, embedding analytics from the start... rather than as an afterthought, is becoming essential. But what does it really mean to design analytics into the DNA of a business process? Have you ever had a stakholder launch a new product and then come to see you as a data team to see how its performing and what the key metrics are? I bet you have, Analytics by design is the process of ensuring data team is at the table from day one to drive the best practices and understand the right metrics In the latest episode of the Stacked Data Podcast, I sit down with @Barbora Spacilova, Product Data & Insight Manager at @NMI, to dive into Analytics by Design—why it matters, how to implement it, and the challenges that come with it. 🔥 𝚆̲𝚎̲ ̲𝚌̲𝚘̲𝚟̲𝚎̲𝚛̲:̲ ✅ Why 'Analytics by Design' is a game-changer for modern businesses ✅ How to align data strategy with business goals from day one ✅ Real-world examples of companies doing this right ✅ The biggest pitfalls to avoid & how to measure success If you're in data, product, or analytics, this one's for you! 🎧
We're back, and we're kicking off with a BANG! 💥 Monzo is home to one of the most respected data teams in the UK—true trailblazers in Analytics Engineering, regularly sharing insights into how they scale data. This episode is no different! I had the pleasure of sitting down with John Azzopardi, Senior Analytics Engineering Manager at Monzo, who's been with the company for over 6 years. We dive deep into how Monzo's data team scaled from 20 to over 170 people and how Analytics Engineering plays a crucial role in supporting their hyper-growth. In this episode, we cover: ✅The evolution of the Analytics Engineering function at Monzo ✅How the team is structured for success and their key responsibilities ✅Challenges of scaling Monzo's data warehouse and infrastructure ✅Why incremental modeling is a cornerstone of their data strategy ✅The future of Analytics Engineering in fintech and what's next for Monzo If you're looking to learn from one of the UK's top data teams, this episode is a must-listen! 🎧 ✨ Hear firsthand how Monzo is mastering data at scale and driving innovation in their infrastructure. We're dropping new episodes every other week, so make sure to FOLLOW & SHARE to stay in the loop! #Podcast #Fintech #AnalyticsEngineering #DataEngineering #Monzo #Data #Innovation #DataInfrastructure #Scaling #IncrementalModeling #TechLeadership #ModernData
The Rise of the Data Product Manager… How often have you joined a data team where data is poorly understood by the business, siloed, and churning out tickets with little impact? Or found that the Analytics team doesn't communicate with Data Engineering, leading to project delays due to missing data? These common challenges underscore the need for a new role that's gaining traction: the Data Product Manager. This week on The Stacked Data, I'm joined by Karen Francis, a Data Product Manager at B&Q, and Rianna Kelly a Head of Data at Zeps. Karen and Rianna have firsthand experience with these issues and many more. They've seen how powerful and transformative the role of a Data Product Manager can be. In this episode, they share insightful stories and discuss why this role is crucial for high-performing modern data teams. We discuss: * The responsibilities of a Data Product Manager * The typical challenges they overcome * How Data Product Managers drive value and efficiency * Case Studies and Success Stories * Why more data teams need to adopt this role Tune in to gain valuable insights and understand why the Data Product Manager is essential for any data-driven organization.
Is AI coming for data jobs? Gaurav Tiwari [https://www.linkedin.com/feed/], an Engineering Manager at Spotify [https://www.linkedin.com/feed/], joining me on The Stacked Data Podcast [https://www.linkedin.com/feed/]. I first encountered Gaurav's insightful perspectives on AI at The London Analytics Engineering Meet-up, I had to get him on the show! With a deep-seated passion for AI, Gaurav brings a critical eye to this rapidly evolving field. We dived deep into the implications of Generative AI on the data landscape and how it will impact your roles and responsibilities in data... Gaurav, shares his thoughts on how AI will impact the life of a data professional… In this episode, we cover: ✅ Understanding Generative AI ✅ GenAI at the Consumption Layer: Discussing how GenAI is reshaping the interaction between businesses and data through analytics. ✅ Benefits of GenAI in Analytics: Exploring the potential benefits GenAI could bring to self-serve analytics platforms. ✅ Challenges and Solutions: Identifying the biggest challenges when integrating GenAI into analytics processes and how to address them effectively. ✅ Strategic Investment and Pitfalls: Guidance for organisations on where to start their investments in GenAI and potential pitfalls to avoid. ✅ Data Engineering AI Impact: ✅ Challenges Specific to Data Engineering: Examining the unique challenges data engineers face when integrating GenAI and how to overcome them. ✅ Optimal Strategies for Implementation: Recommended strategies for adopting GenAI within data engineering teams and aligning methodologies with new tools. ✅ Tools and Technologies: Highlighting specific tools or technologies such as Infer [https://www.linkedin.com/feed/], TurinTech AI [https://www.linkedin.com/feed/] and more Gaurav's insights and expertise make this episode a must-listen for anyone interested in the evolving landscape of Generative AI and its impact on data analytics and engineering.
The Semantic Layer, what cand it do? The modern data stack has transformed the way businesses store, prepare, and consume data. Yet, amidst this data revolution, organisations are continuing to encounter challenges such as data quality, inconsistency, and escalating costs. One solution gaining traction is the semantic layer. Excited to announce the latest episode of the Stacked Data Podcast, featuring David Jayatillake, the VP of AI at Cube! We explore the transformative impact of the modern data stack and delve into the intricacies of the semantic layer. David reminisces about being a human semantic layer, translating business logic into usable data in his early career, to setting up an LLM NLP start-up, David has an incredible story and the best insights. In this episode, we cover: * David's journey in the Data & AI space * Understanding the term "semantic layer" and its function within a data infrastructure * The importance of the semantic layer in the modern data stack * Specific challenges like data inconsistency and scalability that the semantic layer can effectively address * Tangible benefits of implementing a semantic layer, including enhanced security and AI capabilities * Cube's unique approach to the semantic layer and the value it brings to organisations * Exciting developments and future plans for Cube in the evolving data landscape David's insights and expertise make this episode a must-listen for anyone interested in the modern data stack and the role of the semantic layer. Don't miss out on this deep dive into one of the most critical components of data infrastructure!
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.
This week on The Stacked Data Podcast [https://www.linkedin.com/feed/#], I had the pleasure of hosting Simone Spinalbelli [https://www.linkedin.com/feed/#] from Phoenix Group [https://www.linkedin.com/feed/#]. 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.
What does it take to run over 1,500 experiments a year — and still maintain speed, quality, and impact? In this episode, Harry sits down with Dmitry Zolotukhin [https://www.linkedin.com/in/dimazolotukhin/overlay/about-this-profile/?miniProfileUrn=urn%3Ali%3Afs_miniProfile%3AACoAAAhItnwB4sPSrzvmZ8E27qMch_Me0dFnbEI], VP of Analytics at Flo, to unpack how one of the world's leading health and wellness apps built a truly data-driven culture of experimentation. They explore Flo's journey from early-stage testing to a mature experimentation framework, the technical infrastructure behind it, and how the team coordinates hundreds of tests at once without losing focus on user experience or business value. You'll learn: * How Flo scaled experimentation from small tests to 1,500+ annual experiments * The tools and frameworks powering experimentation at scale * Strategies for balancing speed vs. quality in testing * How to foster a culture of learning and experimentation across teams * Real examples of how data-driven insights shaped Flo's product roadmap
On this episode of the Stacked Data Podcast, Harry Gollop sits down with Hugo Lu, co-founder of Orchestra, to tackle one of the most common debates in modern data teams: should you build your own tools or buy off-the-shelf solutions? Hugo shares his experiences on both sides of the decision, practical frameworks for evaluating cost, opportunity, and long-term value, and real-world examples of when building or buying was the right call. Whether you're a Head of Data, an engineer, or just curious about tooling strategy, this episode provides actionable insights to help your team make smarter, strategic decisions.
Welcome back to the Stacked Data Podcast — where we explore what it really takes to build impactful data teams in the modern world. This week's episode is all about stepping out of the ticket queue and into the strategic driver seat. I sat down with Adam Cassar, Director of Analytics at Wise, to explore how analytics teams can break free from the reactive reporting cycle and become genuine business partners. This conversation is packed with real-life examples, practical strategies, and hard-earned lessons from Adam's experience leading high-performing teams. We cover: ✅Why many analytics teams end up in a service-provider role — and how to shift that perception ✅The biggest barriers to becoming more strategic (and how to overcome them) ✅How to proactively influence business decisions (not just report on them) ✅What skills, mindsets, and relationships actually matter if you want your team to have impact Whether you're an IC or leading a data team, this episode is for anyone who wants to stop being a dashboard factory — and start driving real change in the business. 🔁Share it with someone who's ready to level up their data career #StackedDataPodcast #AnalyticsEngineering #ModernDataTeam #DataLeadership #Wise #DataStrategy
Building High-Value Data Teams and the Future of BI with Oliver Hughes, CEO of Count In this episode of the Stacked Data Podcast, we're joined by Oliver Hughes, CEO of Count — a business intelligence platform reinventing how teams collaborate through a flexible, canvas-style interface. Together, we explore: * The key principles that make data teams truly impactful * Why operational clarity, effective problem-solving, and reducing time to value are so critical * How Count's unique approach is transforming data collaboration beyond traditional dashboards * The future of BI and how data teams can stay ahead in a fast-evolving landscape Whether you're leading a data team, building one, or looking for smarter ways to drive insights, this episode is packed with valuable lessons. Tune in and discover a smarter, more collaborative future for data!
In this episode of the Stacked Data Podcast, we're joined by Zach from Advancing Analytics to dive deep into the world of modern data platforms. Zach walks us through his career in data and his current role at one of the UK's leading data consultancies. We explore what a modern data platform really is, why companies are investing in them, and what it takes to build one that's scalable, reliable, and genuinely useful to the business. From core stages and common pitfalls to ensuring business alignment and future-proofing, Zach shares the lessons he's learned delivering platforms for a wide range of clients. We also zoom out to talk about what it's like working in data consulting—what skills matter, what a typical day looks like, and what makes someone successful at Advancing Analytics. If you're interested in data architecture, consulting, or just want to understand what "modern" really means when it comes to data platforms—this one's for you.
The data industry is evolving at an exponential rate; what is possible today was a dream just 10 years ago. As data teams become more complex and roles increasingly specialised, the risk of losing a holistic view of the systems and elements that make up a successful data team grows. This week, I'm joined by data strategist Dylan Anderson from Rekite to discuss the importance of understanding the entire data ecosystem, not just your own "fragment." Data professionals are becoming more specialised in their respective areas, adopting an inch-wide, mile-deep approach as data roles demand ever-increasing expertise. While specialisation is necessary, are we overlooking the benefits of a broader perspective? Dylan puts it well: "It's like appreciating a single piece of a puzzle without acknowledging the entire picture." In This Episode, We Discuss: * How to Navigate the Data Ecosystem: Gain insights into understanding the interconnected elements of data roles. * Avoiding Siloes in Teams and Skill Sets: Learn why it's crucial not to isolate your team or your personal skill set. * Consequences of Poor Communication: Discover the impact of inadequate communication within data teams. * The Importance of a Broad Understanding: Understand why having a wide-ranging knowledge of the data ecosystem is beneficial. Dylan is a globally recognised data strategist who regularly shares his insights, thoughts, and lessons on LinkedIn and through his new newsletter. As a long-time follower of Dylan and his content, it was a privilege to have him on the podcast. If you don't already follow Dylan or subscribe to his newsletter, I highly recommend doing so! Dylan newsletter: Issue #2 - The Data Ecosystem: Where do you even start? (substack.com) [https://thedataecosystem.substack.com/p/issue-2-the-data-ecosystem-where]
Data Contracts have gained a huge amount of traction over recent years as Data teams strive to increase the quality of their offering. This week on Stacked Data podcast I had the pleasure of taking a deep dive into the world of data contracts with none other than Andrew Jones, the Principal Engineer at GoCardless and the pioneer behind this transformative concept. If you haven't heard of Data Contracts they bring several significant benefits to a data team and their data management practices, including: Improved Data Quality Standardisation and Consistency Scalability Data Governance Facilitates Data Monetization 🔍 Episode Highlights: * Introduction to Data Contracts: Andrew kicks off our episode with a basic understanding of what data contracts are and how they revolutionise GoCardless approach to data management. * Why Data Contracts?: Discover the crucial role data contracts play in today's complex data landscape and why Andrew felt compelled to develop them. * Benefits Unveiled: Learn about the substantial advantages data contracts bring to data governance, integrity, and management practices. * Real-World Impact: Andrew shares compelling examples from his experiences at GoCardless, illustrating the significant improvements data contracts have facilitated. * Addressing Common Data Challenges: Find out how data contracts can solve prevalent issues like data inconsistency and quality concerns. * Implementation Insights: Gain practical strategies and tips for integrating data contracts into your data management processes, alongside the best tools and cultural shifts required for success. * Overcoming Obstacles: Hear about the common challenges organizations face when adopting data contracts and the solutions to overcome them. * Future Perspectives: What does the future hold for data contracts? Andrew shares his vision for adapting to upcoming data management trends and opportunities. * Final Thoughts: Before we wrap up, Andrew imparts some invaluable advice for anyone considering data contracts as part of their data strategy. Andrew Book: Driving Data Quality with Data Contracts: A comprehensive guide to building reliable, trusted, and effective data platforms: Amazon.co.uk: Jones, Andrew: 9781837635009: Books [https://www.amazon.co.uk/Driving-Data-Quality-Contracts-comprehensive/dp/1837635005/ref=sr_1_1?adgrpid=1187474295122222&dib=eyJ2IjoiMSJ9.uDYXwhPxoae-G1kTCMAlWTf0zgFrbpZNvGOGuKyNsyw.WI26H3mqHOhMl_hMw-Km4gaeOrkyRl00z8rF6GHc0bw&dib_tag=se&hvadid=74217345298573&hvbmt=bb&hvdev=c&hvlocphy=40877&hvnetw=o&hvqmt=b&hvtargid=kwd-74217415972160%3Aloc-188&hydadcr=24434_2219261&keywords=data+contracts+andrew+jones&qid=1715757692&sr=8-1]