Stacked Data Podcast

In this engaging discussion, Matt Raninski, Head of Data at Paired, shares his expertise in product analytics and event tracking. He dives into what makes event tracking essential for understanding user behavior and driving product success. Matt offers tips on prioritizing the right data to track, avoiding the pitfalls of over-tracking, and the importance of thorough documentation. He also emphasizes effective communication with non-technical teams and practical strategies for setting up event tracking in mobile apps, making data actionable for 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 metricsIn 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 successIf 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 MonzoIf 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

Karen Francis, a Data Product Manager at B&Q and host of the Women in Data podcast, and Rihanna Kelly, Head of Data at Zeps, dive into the transformational role of Data Product Managers. They share personal stories about their careers in data and discuss the challenges teams face in showcasing their value. Emphasizing the need for better communication and strategic partnership, they highlight how Data Product Managers can bridge gaps between analytics and engineering to drive efficiency and align data initiatives with business objectives.

Is AI coming for data jobs?Gaurav Tiwari, an Engineering Manager at Spotify, joining me on The Stacked Data Podcast. 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, TurinTech AI and moreGaurav'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 landscapeDavid'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!

Data strategist Dylan Anderson from Rekite emphasizes the importance of a holistic view of the data ecosystem. Topics include navigating data roles, avoiding siloes, consequences of poor communication, and the benefits of a broad understanding. The podcast explores challenges in understanding interconnected data elements, the significance of data unicorns, stakeholder management, treating data as a product, and the importance of empathy in the data field.

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 QualityStandardisation and ConsistencyScalabilityData GovernanceFacilitates 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

In this insightful discussion, Matt Webb, Head of Enterprise Data Management for UK Power Networks, shares his expertise in leading data transformation initiatives. He highlights the crucial role cloud migration and Databricks play in supporting the UK's decarbonization goals. Matt addresses the challenges traditional organizations face in adopting modern data architectures, emphasizing the need for a data-driven culture. He also explores the integration of engineering skills in data processes and the strategies for overcoming legacy system hurdles.

In our latest episode of The Stacked Data podcast, I’m joined by Veronica Saha, Head of Analytics at Zoopla (part of Houseful), as she shares real-world examples and strategies on data monetization.In 2024, data teams need to evolve beyond being mere support functions; they must become strategic drivers. Many data teams have struggled to show/prove their true value. The monetisation of data comes in many different forms, however, it's clear that raw data is not valuable on its own.Data only hold value as insight driving decisions. It needs to be packed into a PRODUCT!We discuss the need to treat data as a product in order to effectively implement a monetisation strategy.🎙️ Episode Highlights: Defining      Data Monetization: What exactly do we mean by 'data monetisation'?      When is the right time to think about it? How can data create business      value? Identifying      Opportunities: How do you identify areas for data monetization? What's      the role of technical architecture in this process? Building      a Revenue-Focused Team: Challenges and considerations in building a      team focused on generating revenue through data products. Feedback      Loops and Continuous Improvement: Establishing effective feedback      loops and optimizing data products for better monetization outcomes. Securing      Leadership Buy-In: Challenges in securing sign-off and budget from      leadership. Common      Challenges and Lessons Learned: Insightful lessons from Zoopla's      journey in overcoming challenges in data monetization.