The Data Playbook Podcast

In this episode of The Data Playbook, Jelena Grujic (Dataminded) explains why the divide between data scientists and data engineers still exists and how to finally break it.We dive into real-world conflicts around unit tests, notebooks, production data access, documentation, and overengineered solutions. Jelena shares pragmatic alternatives like data testing, functional pipelines, and purpose-based access that actually work in production.A must-listen for data leaders and practitioners who want fewer debates and better data products.🎧 Topics include data testing, notebooks, production data, functional programming, and team collaboration.🎧 Listen to more episodes of The Data Playbook for real-world stories on data platforms, GenAI, data products and cloud independence from Europe’s leading data practitioners and leaders.🌐 More at https://www.dataminded.com/resources#DataScience #DataEngineering #DataLeadership #DataTeams #DataPlatform #AnalyticsEngineering #DataInProduction #MachineLearning #ModernDataStack #TheDataPlaybook

Most data leaders know the statistic: the majority of big data initiatives never deliver the value they promised. In this episode, Kris sits down with Frederic Vanderveken from Dataminded to unpack a practical framework to choose and validate the right data use cases.We cover:Why so many data initiatives fail before they even startHow to anchor your work in business strategy, not technologyHow to run problem discovery interviews that surface real headaches, not minor annoyancesPrioritising solutions using five lenses: customer, growth, money, pragmatic feasibility and differentiatorsBuilding a quantifiable business case and defining success upfrontIf you’re a data leader or product owner deciding where to place your next big bet, this episode gives you a structured way to reduce risk and ship data solutions that actually move the needle.Follow The Data Playbook for more episodes on data platforms, data products and making AI useful in real life.🌐 More at ⁠www.dataminded.com⁠ and subscribe to our channel.⏱️ Chapters:00:00 Introduction to Data Solutions Framework09:03 Effective Problem Discovery Techniques17:53 Mapping Customer Journeys26:39 Collaborative Solution Brainstorming32:27 Testing Solutions and Integration44:39 Final Thoughts and Key Takeaways

Kris Peeters sits down with Amaury Anciaux, founder of River Solutions, to tackle a painful reality for data leaders: critical decisions still depend on fragile Excel models.They explore why Excel won’t disappear, how River turns spreadsheets into visual, explainable and reliable decision models, and what happens when you bring data quality checks, testing and documentation into the analyst workflow.Topics include:Why 99% of models in organisations are still built in ExcelSilent errors, risk, and the real cost of debugging formulasVisual flow-based modelling and model maps inside ExcelBuilt-in checks for missing data, duplicates and broken lookupsHow AI copilots helped build River, and why AI won’t replace transparent modelsThe evolving role of analysts and managers in data-driven decisions🎧 Listen to more episodes of The Data Playbook for real-world stories on data platforms, GenAI, data products and cloud independence from Europe’s leading data practitioners and leaders.🌐 More at https://www.dataminded.com/resources Chapters: 00:00 – Intro & episode setup00:45 – Amaury’s background & consulting career02:00 – The hidden reality of Excel decision models04:00 – Why “just get it out of Excel” doesn’t scale05:10 – What River Solutions does in Excel06:40 – Visual model maps for explainable models08:40 – Removing formulas & adding data quality checks10:50 – Why Excel errors are so risky for big decisions13:15 – Who River is for: analysts, Excel gurus & managers16:05 – Why Amaury started River now & building with Copilot19:00 – Will AI copilots replace River and Excel modelling?22:51 – How River works as an Excel add-in (UX & interactivity)26:25 – How River changes the analyst role (less debugging, more thinking)28:10 – Roadmap: community, cloud, AI & connecting to data warehouses31:14 – Biggest lesson learned: software is easy, change is hard

What happens when a bank decides that AI and IP are so strategic they must be built in-house - then actually follows through for more than a decade?In this episode of The Data Playbook, Dr. Barak Chizi, Chief Data & Analytics Officer at KBC Group, joins Kris Peeters to reveal how KBC built one of Europe’s most mature AI organisations and what it took to bring Kate, their AI assistant, to life, and keep her evolving for 5 years.You’ll hear how KBC:Grew from early machine learning to 2,000+ AI use cases in productionDeveloped an AI-driven anti-money laundering platform and commercialised it for other banksScaled Kate, now celebrating 5 years and upgraded with GPT.Uses the U-model to govern AI safely from idea to productionKeeps ROI at the centre of every AI projectStays vendor-independent while still leveraging hyperscaler LLMsBuilds diverse, high-calibre AI teams with a rigorous recruitment approachExplores soft logic and modelling customer intent as the next frontier of financial AIIf you want to understand how to turn AI from experiments into a true competitive advantage, this conversation is your playbook.🌐 More at www.dataminded.com and subscribe to our channel.Show notes:The Foundation of Soft Logic👉 https://link.springer.com/book/10.1007/978-3-031-58233-2Dan Ariely – Predictably Irrational👉 https://www.amazon.com/Predictably-Irrational-Revised-Expanded-Decisions/dp/0061353248/⏱️ Chapters00:00 – Intro to The Data Playbook & today’s guest01:15 – Barak’s backstory: 25 years in AI & high-dimensional data03:02 – What a CDAO does at KBC & enabling 24/7 AI-assisted service04:55 – Towards continuous, machine-supported customer journeys06:37 – The U-Model: KBC’s framework for data & AI projects08:35 – Flagship AI products, finite project lifecycle & retraining10:07 – Prioritising AI use cases across 5 countries12:31 – ROI mindset, conservative risk culture & data as an asset14:21 – Why KBC keeps AI in-house & limits external consultants18:17 – Beyond data warehouses: from reporting to prediction22:21 – AI-driven AML platform & the creation of SKY25:30 – Patents, AI IP and KBC’s competitive positioning27:25 – Generative AI at KBC since 2018 & early transformer experiments29:11 – Pragmatic tech choices: LLMs vs ML vs simple automation31:42 – Avoiding GenAI hype and focusing on customer value33:03 – Why KBC built Kate: 24/7 banking & impatient customers35:28 – From FAQ bot to execution engine: Kate’s end-to-end capabilities37:07 – Customer reactions, branches vs digital & Kate’s 2026 roadmap39:24 – Multi-LLM strategy, vendor independence & design partnerships40:44 – Inside Kate’s architecture: NLU, open source & KBC-built layers42:37 – Proactive AI: timing, context and personalised offers44:51 – Soft logic, consciousness & modelling customer intent49:19 – Building a diverse, 24-nationality AI team at KBC51:37 – Recruitment process, tests & how candidates are evaluated55:21 – What KBC looks for in modern data scientists57:15 – Lessons after 10 years at KBC & book recommendation

EU clouds without the hype. Niels Claeys (Partner & Lead Data Engineer at Dataminded, and our technical hiring lead) breaks down data sovereignty vs. Cloud Act, GDPR realities, and a portable, Kubernetes-first stack with Iceberg, Trino, and Airflow. We compare Scaleway, OVH, Exoscale, UpCloud, look at cost drivers, encryption/KMS, egress policies, and how to avoid vendor lock-in plus when best-of-breed beats all-in-one and why “keep it simple” still wins.What you’ll learn:When EU clouds make more sense than hyperscalers (and when they don’t)Designing a portable platform: Terraform/Tofu for infra, Argo CD for appsTable formats 101: why Apache Iceberg over plain Parquet/CSVQuery layer choices: Trino for open SQL across object storage & DBsOrchestration in practice: Airflow patterns, dependencies, SLAsSecurity & governance: OPA for fine-grained policies, IAM, catalogsCost & ops: egress, managed services gaps, version lag, troubleshootingTeam skills: what to hire for, and the “hard questions” Niels asks in interviews🌐 More at ⁠www.dataminded.com⁠ — and subscribe!Chapters00:00 Intro & why EU clouds now04:40 Compliance & legal: GDPR, Cloud Act, sovereignty11:55 Platform blueprint: Kubernetes + Iceberg + Trino + Airflow20:30 Catalogs, OPA, IAM & access control27:10 EU providers deep dive: Scaleway, OVH, Exoscale, UpCloud36:20 Cost, encryption/KMS, egress & performance43:10 Best-of-breed vs all-in-one (and glue work)51:00 Getting started: IaC, Argo CD, day-2 ops56:40 Hiring: interview signals & practical takeawaysKeywordsEU cloud, European cloud providers, data sovereignty, GDPR, Cloud Act, Kubernetes data platform, Apache Iceberg, Trino, Airflow, vendor lock-in, OPA, Argo CD, Terraform, Exoscale, Scaleway, OVH, UpCloud