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DataScience Show Podcast

DataScience Show Podcast

Von: Mirko Peters
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 Welcome to The DataScience Show, hosted by Mirko Peters — your daily source for everything data! Every weekday, Mirko delivers fresh insights into the exciting world of data science, artificial intelligence (AI), machine learning (ML), big data, and advanced analytics. Whether you’re new to the field or an experienced data professional, you’ll get expert interviews, real-world case studies, AI breakthroughs, tech trends, and practical career tips to keep you ahead of the curve. Mirko explores how data is reshaping industries like finance, healthcare, marketing, and technology, providing actionable knowledge you can use right away. Stay updated on the latest tools, methods, and career opportunities in the rapidly growing world of data science. If you’re passionate about data-driven innovation, AI-powered solutions, and unlocking the future of technology, The DataScience Show is your essential daily listen. Subscribe now and join Mirko Peters every weekday as he navigates the data revolution! Keywords: Daily Data Science Podcast, Machine Learning, Artificial Intelligence, Big Data, AI Trends, Data Analytics, Data Careers, Business Intelligence, Tech Podcast, Data Insights.

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  • Scaling AI: An Executive Playbook for Measurable ROI

    Scaling AI: An Executive Playbook for Measurable ROI
    Jan 23 2026
    Many enterprises stall after promising AI pilots because experiments lack product rigor, clear ownership, and instrumented ROI. In this episode Mirko delivers a compact, practical playbook for executives to convert pilots into repeatable, revenue-driving products. He focuses on the decisions leaders must make: aligning outcome-level KPIs to business objectives, designing a minimum viable model product with deployment and monitoring, establishing funding and governance, and instrumenting ROI and risk from day one. To ground the framework, Mirko shares an anonymized vignette: a retail client that cut stockouts by 12% and improved gross margin by 3% within six months after productizing a demand-forecast model. Listeners will leave with a prioritized 90-day checklist, negotiation language to secure executive buy-in, and a concrete CTA to download a two-page AI Scaling Checklist. This episode avoids code-level how-tos and vendor hype, concentrating on leadership moves that produce measurable value.

    Become a supporter of this podcast: https://www.spreaker.com/podcast/datascience-show-podcast--6817783/support.
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    10 Min.
  • 4 Data Modeling Mistakes That Break Data Pipelines at Scale

    4 Data Modeling Mistakes That Break Data Pipelines at Scale
    Dec 10 2025
    Slow dashboards, runaway cloud costs, and broken KPIs aren’t usually tooling problems—they’re data modeling problems. In this episode, I break down the four most damaging data modeling mistakes that silently destroy performance, reliability, and trust at scale—and how to fix them with production-grade design patterns. If your analytics stack still hits raw events for daily KPIs, struggles with unstable joins, explodes rows across time ranges, or forces graph-shaped problems into relational tables, this episode will save you months of pain and thousands in wasted spend. 🔍 What You’ll Learn in This Episode
    • Why slow dashboards are usually caused by bad data models—not slow warehouses
    • How cumulative tables eliminate repeated heavy computation
    • The importance of fact table grain, surrogate keys, and time-based partitioning
    • Why row explosion from time modeling destroys performance
    • When graph modeling beats relational joins for fraud, networks, and dependencies
    • How to shift compute from query-time to design-time
    • How proper modeling leads to:
      • Faster dashboards
      • Predictable cloud costs
      • Stable KPIs
      • Fewer data incidents
    🛠 The 4 Data Modeling Mistakes Covered 1️⃣ Skipping Cumulative Tables Why daily KPIs should never be recomputed from raw events—and how pre-aggregation stabilizes performance, cost, and governance. 2️⃣ Broken Fact Table Design How unclear grain, missing surrogate keys, and lack of partitioning create duplicate revenue, unstable joins, and exploding cloud bills. 3️⃣ Time Modeling with Row Explosion Why expanding date ranges into one row per day destroys efficiency—and how period-based modeling with date arrays fixes it. 4️⃣ Forcing Graph Problems into Relational Tables Why fraud, recommendations, and network analysis break SQL—and when graph modeling is the right tool. 🎯 Who This Episode Is For
    • Data Engineers
    • Analytics Engineers
    • Data Architects
    • BI Engineers
    • Machine Learning Engineers
    • Platform & Infrastructure Teams
    • Anyone scaling analytics beyond prototype stage
    🚀 Why This Matters Most pipelines don’t fail because jobs crash—they fail because they’re:
    • Slow
    • Expensive
    • Semantically inconsistent
    • Impossible to trust at scale
    This episode shows how modeling discipline—not tooling hype—is what actually keeps pipelines fast, cheap, and reliable. ✅ Core Takeaway Shift compute to design-time. Encode meaning into your data model. Remove repeated work from the hot path. That’s how you scale data without scaling chaos.

    Become a supporter of this podcast: https://www.spreaker.com/podcast/datascience-show-podcast--6817783/support.
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    27 Min.
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