Data Matas

Matatika
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A show to explore all data matters. From small to big, every company on the market, irrespective of their industry, is a data merchant. How they choose to keep, interrogate and understand their data is now mission critical. 30 years of spaghetti-tech, data tech debt, or rapid growth challenges are the reality in most companies. Join Aaron Phethean, veteran intrapreneur-come-entrepreneur with hundreds of lived examples of wins and losses in the data space, as he embarques on a journey of discovering what matters most in data nowadays by speaking to other technologists and business leaders who tackle their own data challenges every day. Learn from their mistakes and be inspired by their stories of how they've made their data make sense and work for them. This podcast brought to you by Matatika - "Unlock the Insights in your Data"

  1. S2E6 - Stop Scaling What You Don’t Understand With John Napoleon-Kuofie

    6月12日

    S2E6 - Stop Scaling What You Don’t Understand With John Napoleon-Kuofie

    What if scaling your data platform meant starting from scratch? In this episode of Data Matas, John Napoleon-Kuofie, Analytics Engineer at Monzo, shares what it really takes to rebuild trust in your models - inside one of the UK’s fastest-growing digital banks. From inheriting 1,000+ undocumented DBT models to challenging the purpose of legacy tests, John walks through the decisions his team is making to improve data quality, reduce noise, and future-proof their platform. It’s a candid, practical conversation about choosing clarity over complexity, and learning to say no to work that doesn’t deliver value. 👉 In this episode, you’ll learn how to: Rebuild inherited models by starting with real-world concepts, not assumptions Reduce alert fatigue by testing only what matters (and ignoring the rest) Prepare your data architecture before layering on AI or self-serve tools Design systems that future engineers can actually understand Foster a culture where bottom-up innovation drives real change Watch the full episode on YouTube, or listen on Spotify and Apple Podcasts. 👤 John Napoleon-Kuofie on LinkedIn👤 Aaron Phethean on LinkedIn🎧 Data Matas Podcast🌐 Matatika Website

    36 分鐘
  2. S2E5 - From Learning the Tool to Designing the System: How Engineers Actually Grow

    5月22日

    S2E5 - From Learning the Tool to Designing the System: How Engineers Actually Grow

    How do analytics engineers grow from writing SQL to designing entire data systems? And why do most companies still confuse tool mastery with real engineering skill? In this episode of Data Matas, Oleg Agapov (Senior Analytics Engineer at Hiive) shares what it actually takes to go from junior to senior in data—beyond bootcamps, tools, and job titles. He breaks down the core shifts that matter most: thinking in systems, mastering data modelling, and creating structure that scales. You’ll hear how Oleg is helping build self-serve analytics inside a fast-moving fintech startup, why most data work fails without discovery, and how AI is changing the role—but not replacing the role—of the analytics engineer. 🎙 Guest: Oleg Agapov, Senior Analytics Engineer at HiiveOleg has spent over 15 years in data roles, moving from analyst to engineer to analytics architect. Now at Hiive—a marketplace for private stock—he’s helping design scalable data models and BI tooling that enable business teams to self-serve. Oleg also mentors junior engineers and shares career guidance on LinkedIn weekly, offering a rare combination of technical depth and practical coaching. ⏱ Episode Takeaways & Timestamps 03:40 – Why analysts become engineers (and what tools don’t teach you)Why Oleg moved from analytics into engineering, and how messy data triggered a career pivot. 08:15 – What junior vs senior actually looks like in analytics engineeringFrom DBT basics to architecture thinking—how your role shifts as you grow. 12:30 – Data modelling isn’t a feature, it’s a disciplineWhy writing queries isn’t enough—and why most engineers only realise this at scale. 17:45 – Building analytics in a three-sided marketplace startupHow Oleg is helping Hiive build self-serve data for a unique financial model. 24:00 – How AI fits into the modern data workflow (and where it fails)Why LLMs are better reviewers than creators—and why trust still starts with humans. 28:40 – The hidden risk of AI assistants in BI toolsWhat happened when an AI assistant hallucinated a metric—and nearly caused a decision error. Who Should Listen?If you’re an analytics engineer, data modeller, or anyone growing a data team inside a startup or scale-up, this episode will help you move beyond dashboards and into strategic, scalable thinking. Especially valuable for those navigating the shift from IC to senior roles. 📢 Like this episode?Subscribe to the Data Matas YouTube channel for weekly insights from real data leaders.Hit the bell to get notified when new episodes go live.💬 What’s one skill you think separates senior engineers from juniors? Let us know in the comments. 🔗 Links & Resources 👤 Oleg Agapov on LinkedIn: https://www.linkedin.com/in/oleg-agapov👤 Aaron Phethean on LinkedIn: https://www.linkedin.com/in/aaron-phethean/🌐 Matatika Website: https://www.matatika.com🎧 Data Matas Podcast: https://www.matatika.com/podcasts📺 Data Matas YouTube Channel: https://www.youtube.com/@matatika/podcasts

    33 分鐘
  3. S2E4 - Data Engineers Don’t Burn Out from Work, They Burn Out from Pointless Work

    5月1日

    S2E4 - Data Engineers Don’t Burn Out from Work, They Burn Out from Pointless Work

    What if your data team’s biggest risk isn’t technical debt - but mental exhaustion? In this brutally honest conversation, Nik Walker (Co-op) shares how a culture of low-value work and constant reactivity burns out even the best data teams, and what to do about it. About this episodeData engineers aren’t struggling because the tech is hard—they’re struggling because the work often isn’t worth doing. In this episode, Nik unpacks why discovery matters more than dashboards, how to protect your team from the myth of speed, and why AI means nothing if no one trusts the data. You’ll get practical, real-world insights from someone scaling data infrastructure in one of the UK’s most complex legacy organisations—without breaking the team or the bank. About the guestNik Walker is Head of Data Engineering at Co-op, leading data transformation across a massive enterprise with deep legacy tech and community-first values. Known for his human-centric leadership style and vocal advocacy for neurodiversity in data teams, Nik brings humour, candour, and serious experience to the conversation.🔗 Nik on LinkedIn Timestamps and Key Learnings06:15 – How Co-op builds safe, structured teams that don’t burn out→ Create psychological safety with process, not platitudes 09:27 – Why neurodiversity awareness isn’t optional in data teams→ 60% of data professionals are neurodivergent—your leadership style should reflect that 18:26 – What it really takes to trust your AI outputs→ If the maths is off or the data’s wrong, no one will use your model 20:28 – Stop syncing everything in real-time→ You don’t need real-time pipelines—you need right-time pipelines 28:07 – Discovery over delivery: how to stop wasting time and money→ Methodical work delivers more value than rushed builds Why listenIf you’re a data leader tired of firefighting, low-value tasks, or untrusted dashboards, this episode is for you. Nik offers tangible advice on building better systems, defending your team’s time, and navigating real-world transformation without breaking your people. Subscribe and join the conversationLike what you heard? Hit subscribe and tap the bell so you don’t miss future episodes. Have you faced burnout in your data team? Drop a comment below and share your experience, we’d love to hear from you. Links and Resources🔗 Nik Walker on LinkedIn: https://www.linkedin.com/in/nikolaswalker/🔗 Aaron Phethean on LinkedIn: https://www.linkedin.com/in/aaron-phethean/🔗 Matatika: https://www.matatika.com🎙️ Data Matas: https://www.matatika.com/podcasts/📺 Data Matas YouTube channel: https://www.youtube.com/@matatika

    29 分鐘
  4. S2E3 - Three Lessons Every Data Leader Should Steal from Quantum-Inspired Thinking" at IRIS

    4月10日

    S2E3 - Three Lessons Every Data Leader Should Steal from Quantum-Inspired Thinking" at IRIS

    In this episode, we reveal how IRIS Software is borrowing principles from quantum computing to build data systems that are adaptable, explainable, and innovation-friendly—without compromising business-as-usual. This conversation with David Draper offers a rare inside look at how a major enterprise balances legacy systems with forward-thinking experimentation. You’ll learn how modular architecture, protected innovation time, and explainable AI are reshaping the way IRIS builds its data infrastructure. If you’re a data leader trying to scale without spiralling into chaos, this is the episode to watch. David Draper is the Data Science Manager at IRIS Software Group, where he leads a high-performing team at the intersection of engineering, analytics, and emerging tech. With a background in education and a passion for quantum computing, David brings a unique lens to the challenges of modern data strategy. Timestamps and Takeaways✔️04:42 – From Classroom to Data StrategyDavid’s journey from education to enterprise data science→ Leverage curiosity and teaching mindset to lead technical teams with clarity and empathy ✔️09:15 – Why Quantum Thinking Isn’t Just for PhysicistsHow quantum logic helps IRIS reimagine compute and decision-making→ Adopt non-linear thinking to structure smarter, more adaptable infrastructure ✔️14:08 – Designing Modular Systems for Innovation Without RiskHow IRIS builds infrastructure that allows safe experimentation→ Create sandbox-style systems to test and deploy without affecting BAU ✔️19:22 – Ring-Fencing Innovation Time Inside a Busy EnterpriseBalancing research and delivery with “go wide, then narrow” phases→ Allocate structured exploration time to prevent constant firefighting ✔️24:50 – The Real ROI of Explainable AIWhy clarity builds trust and momentum across the business→ Choose tools your stakeholders understand to drive adoption and reduce resistance ✔️30:30 – Building Teams That Experiment ResponsiblyHow culture, structure and trust shape IRIS’s approach to innovation→ Foster autonomy while staying aligned to business goals Why Listen This episode is for data leaders, architects, and CTOs who want to scale responsibly, embed innovation, and prepare for what’s next—without being distracted by hype. It’s a tactical, grounded conversation that offers immediately applicable ideas. 🔔 Subscribe to Data Matas for more real conversations with data leaders.💬 What’s one lesson from David’s approach you’d apply to your own team? Drop your thoughts in the comments.🎧 Available on YouTube, Spotify, Apple Podcasts, and all major platforms. Links🔗 David Draper on LinkedIn: https://www.linkedin.com/in/david-draper-b715aa46/🔗 IRIS Software Group: https://www.linkedin.com/company/iris-software-group/🌐 Listen to more episodes: https://pod.link/1763791020📚 Matatika resources: https://www.matatika.com/library/📺 Watch more episodes: https://www.matatika.com/podcasts/

    52 分鐘
  5. S2E2 - What Crypto Data Teams Do Differently with Emily Loh

    3月27日

    S2E2 - What Crypto Data Teams Do Differently with Emily Loh

    What Crypto Data Teams Do Differently with Emily LohEpisode SnapshotIn this illuminating conversation, Emily Loh reveals how leading a data team in the volatile crypto space has forced innovations in resource allocation, strategic prioritisation, and AI implementation that data leaders across all industries can apply for greater impact. Guest IntroductionEmily Loh serves as Director of Data at MoonPay, where she leads a 15-person team spanning data engineering, data science, and machine learning in a fast-evolving crypto environment. With previous experience at Coinbase and a surprising background in literature rather than computer science, Emily brings a unique storytelling perspective to data leadership that emphasises business outcomes over technical outputs. Conversation HighlightsThe discussion takes an unexpected turn when Emily reveals her humanities background, noting that her literature studies prepared her surprisingly well for data leadership: "I never thought I'd be in data, but specifically I studied literature. And in my career right now, I'm just like, 'oh, this is just really just storytelling.'" This insight evolves into a thoughtful exchange about how effective data work requires understanding human needs first, with technical implementation second. A particularly candid moment occurs when Emily confesses, "Full disclosure, I am a formed people pleaser," sparking an authentic discussion about the struggle many data leaders face in saying "no" to low-value requests. Aaron and Emily explore how this seemingly simple act requires both courage and strategic frameworks to implement effectively. Actionable Insights1. Implement the 20/40/40 resource allocation framework - Emily's team divides their time into 20% BAU (business as usual), 40% building, and 40% research, creating space for innovation even during challenging market periods. Practical Application: Start by auditing your team's current time allocation, then gradually shift toward this balanced model using clear opportunity sizing frameworks to evaluate potential projects. 2.Break free from the "service trap" by transforming request handling - Instead of immediately building requested dashboards, train your team to ask "What decisions are you trying to make?" to focus on outcomes rather than outputs. Practical Application: Develop a structured intake process that guides stakeholders toward better request formulation and establishes clear value criteria for accepting work. 3. Use AI strategically to eliminate team drudgery - At MoonPay, tools like Cursor help automate tedious tasks such as YAML file management, freeing analyst time for strategic work. Practical Application: Survey team members about their most tedious regular activities, then select appropriate AI tools that can automate these specific tasks while measuring success through time savings. 4. Design data systems for uncertain futures - In crypto's rapidly changing landscape, Emily's team builds flexible architectures that can adapt to regulatory shifts and market changes. Practical Application: Implement modular data models that can evolve without complete rebuilds while maintaining strong data quality foundations that support agility regardless of specific technologies. Industry ContextThis conversation arrives at a critical inflection point for data leaders across industries, as generative AI promises transformation while many teams still struggle with the fundamental challenge of delivering strategic value rather than reactive reporting. Emily's experience navigating crypto's extreme volatility provides a stress-tested framework applicable to any data team facing resource constraints and rapid change. Why ListenThis episode is essential for mid to senior-level data leaders who feel trapped in reactive work cycles and are seeking practical frameworks to increase their strategic impact. Whether you're in a traditional enterprise struggling with legacy approaches or a high-growth startup trying to balance immediate demands with future needs, Emily's battle-tested insights provide immediately applicable strategies for transformation. Episode DetailsLength: 37 minutesRelease Date: March 2025Episode Number: #127 This conversation with Emily Loh offers rare insight into how crypto's extreme conditions have forced innovation in data team management—innovations that can give any data leader a competitive advantage in today's rapidly evolving landscape.

    34 分鐘
  6. S2E1 - Scaling Your Data Infrastructure with AWS's Jon Hammant

    3月13日

    S2E1 - Scaling Your Data Infrastructure with AWS's Jon Hammant

    Are you unknowingly overspending on cloud data infrastructure? Many businesses migrate to the cloud expecting cost savings and efficiency, but hidden costs, vendor lock-in, and inefficient ETL processes often result in ballooning expenses. Without a strategic approach, organisations risk wasting budget on unnecessary compute, storage, and manual data management. What You’ll Learn in This Episode In this episode of Data Matas, host Aaron Phethean speaks with Jon Hammant, Head of Compute for UK & Ireland at AWS, about the true cost of scaling data infrastructure and how businesses can optimise cloud spend. Jon shares his insights on avoiding pricing traps, reducing data migration costs, and leveraging AI-driven automation to improve efficiency. Key Insights & Timestamps 1.  - Reducing unnecessary data syncing to cut costsReal-time syncing is often overused, leading to excessive compute costs. Discover how batch processing can reduce ETL expenses by up to 50%. 2.  - Conducting cloud audits to eliminate wasteMany organisations pay for idle compute and unused storage without realising it. Learn how to audit cloud usage and remove unnecessary expenses. 3. - Avoiding vendor lock-in and costly renewal contractsRow-based ETL pricing can trap businesses into increasing costs as data volumes grow. Find out how switching to usage-based pricing can provide more control over cloud spend. 4.  - Automating data management with AIManual ETL processes drain resources and increase operational costs. Learn how AI-driven automation can streamline workflows, reduce errors, and improve efficiency. About Jon Hammant Jon Hammant is the Head of Compute for UK & Ireland at AWS, where he helps businesses optimise cloud infrastructure, modernise data strategies, and reduce operational costs. With extensive experience in AI, cloud computing, and high-performance networking, Jon has worked with some of the world’s largest enterprises to scale data infrastructure efficiently without unnecessary spend. Subscribe & Join the Conversation If you're looking to optimise cloud spend, reduce migration costs, or navigate vendor pricing models, this episode is for you. 🔔 Subscribe to Data Matas to get the latest insights on cloud cost optimisation, data infrastructure, and AI-driven efficiency. Resources & Links📌 Jon Hammant on LinkedIn: https://www.linkedin.com/in/jhammant/📌 Matatika Podcast & Resources: https://www.matatika.com/podcasts/s2e1-scaling-your-data-infrastructure-with-jon-hammant

    41 分鐘

  7. 2月13日

    Season 1 Highlights: 7 Data Strategies That Work - What the Best Data Teams Do Differently

    In this Season 1 Highlights episode, we break down seven proven strategies that help teams cut costs, improve data quality, and scale smarter. We’ve spoken with data leaders, engineers, and BI experts who have tackled real-world challenges—from legacy systems and broken workflows to AI risks and siloed teams. This episode is your actionable playbook to making your data work better. Key Takeaways:✅ Fix data chaos and create a single source of truth – Jessica Franks (Not On The High Street) shares how she aligned business and tech using Wardley Maps.✅ Rebuild trust in business intelligence – Joe Wright (CitySprint) explains how they solved reporting inconsistencies by consolidating systems.✅ Scale smarter without overcomplicating – Stéphane Burwash (Potloc) shows how open-source tools and a data champions programme transformed their approach.✅ Why ‘good enough’ beats perfection – Bethany Lyons explains how streamlining data pipelines saves time without sacrificing quality.✅ Make data quality everyone’s job – Adam Dathi (MVF) reveals how cross-team collaboration fixes unreliable reporting.✅ Using real-time data for better decision-making – Nick Bromley shares how transport data integration is driving smarter city planning.✅ AI without the risk – Murtaza Kanchwala (Amplify Capital) details how his team successfully implemented AI while staying compliant. 🚀 Whether you're a Head of Data, CTO, BI Manager, or Data Engineer, these practical insights will help you fix inefficiencies, scale with confidence, and build a high-impact data team. 🎧 Listen now and take your data strategy to the next level and 📩 Subscribe for more insights

    28 分鐘

  8. 2024/12/06

    S1E7 - Unlocking Gen.AI Potential in Financial Services With Murtaza Kanchwala

    In this conversation, Murtaza Kanchwala from Amplifi Capital discusses the integration of Generative AI (Gen.AI) in financial services, sharing insights on early applications, challenges faced, and the importance of regulatory compliance. He emphasizes the need for effective team organization and the selection of appropriate AI models to enhance productivity and efficiency. The discussion also touches on the future of AI in finance, predicting the emergence of personalized AI assistants and the ongoing evolution of AI technologies. Takeaways Murtaza has been involved in financial services since 2014. Gen.AI applications started with content generation in 2021. Feedback from users was gathered through personal discussions. AI hallucinations posed significant challenges in early implementations. Internal AI solutions were prioritized before customer-facing applications. Testing AI requires a different approach than traditional software. The maturity of Gen.AI use cases is improving over time. Amplifi Capital is building an AI Matrix platform for Gen.AI use cases. Choosing the right LLM is crucial for specific use cases. Regulatory compliance is essential in financial services AI applications. Sound Bites "AI is delivering something artificially." "The landscape is moving faster than we think." Chapters 00:00 Introduction to Gen.AI in Financial Services 06:27 Early Applications of Gen.AI in Finance 11:07 Maturity of Gen.AI Use Cases 16:51 Building the AI Matrix Platform 22:48 Regulatory Landscape for AI in Finance 29:37 Building Effective Squads in AI Projects 35:18 Future of AI in Financial Services

    41 分鐘
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簡介

A show to explore all data matters. From small to big, every company on the market, irrespective of their industry, is a data merchant. How they choose to keep, interrogate and understand their data is now mission critical. 30 years of spaghetti-tech, data tech debt, or rapid growth challenges are the reality in most companies. Join Aaron Phethean, veteran intrapreneur-come-entrepreneur with hundreds of lived examples of wins and losses in the data space, as he embarques on a journey of discovering what matters most in data nowadays by speaking to other technologists and business leaders who tackle their own data challenges every day. Learn from their mistakes and be inspired by their stories of how they've made their data make sense and work for them. This podcast brought to you by Matatika - "Unlock the Insights in your Data"

資訊

  • 創作者
    Matatika
  • 活躍年代
    2025年
  • 集數
    14
  • 年齡分級
    兒少適宜
  • 版權
    © 2025 Matatika