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  • 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.

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    10 Min.
  • 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.
  • How AI Creates ‘Brand Brains’ That Outperform Teams
    May 9 2025
    Let’s start with a confession: The first time you crack open ChatGPT to churn out a week of social posts, it’s a little like biting into what you thought was a gourmet burger, only to find it’s all bun, no flavor. I’ve been there. Fresh off another late-night email blitz, turnover pizza slice in hand, drowning in tasks that felt both urgent and pointless, my passion for marketing started losing its sizzle. But what if I told you the most powerful asset you have isn’t another analytics dashboard—it’s the mind-numbing time you spend repeating yourself? I’m peeling back the curtain on how reclaiming that lost time (and sprinkling in the *right* AI) can change everything for you—and the humans around you.The daily grind: Where did all your hours go?Ever feel like you're drowning in tasks but making zero progress on what actually matters? You're not alone."When I worked as a marketing manager at a mid-sized software company, my days followed a predictable pattern," shares a marketer who lived the burnout cycle firsthand.A Day in the Life of the Modern Marketer8:30 AM: You arrive, coffee in hand, optimistic about tackling your strategic projects today.8:35 AM: You open your inbox. Fifteen new requests overnight. Three from your boss demanding campaign metrics. Four from sales wanting custom content. Two product announcements needing immediate promotion.9:15 AM: Your carefully planned day? Already derailed. That quarterly strategy you've been trying to work on for three weeks? Pushed aside. Again.Instead, your day dissolves into:* Updating social posts across five platforms* Tweaking ad copy that never feels quite right* Pulling performance reports from multiple platforms* Reformatting everything into executive-friendly presentationsLunch? That's just another meeting about email open rates or landing page conversions while you eat at your desk.The Brutal Numbers Behind Marketing BurnoutThe average marketer's 55-hour workweek breaks down in a way that should terrify us:* 40% on content creation - endless blogs, social updates, and newsletters* 25% on reporting/analysis - pulling data from multiple platforms into cohesive stories* 20% on campaign adjustments - constant tweaking of ads, bids, and targeting* 11% on meetings that rarely produce actionable decisions* Just 4% (about 2 hours) on actual strategic thinkingMeanwhile, your campaigns show a 30% increase in cost per acquisition and a 15% drop in conversion rates. The market's getting more competitive, but you have zero time to develop a thoughtful response.The Real Toll of Task-Driven MarketingThis isn't just about being busy—it's about the invisible cost of tactical overwhelm:* Physical and mental exhaustion from working nights and weekends* Consistently missed deadlines despite working overtime* Strategic projects that remain permanently "on deck"* Zero headspace for the creative thinking that could transform resultsYou implement quick fixes for short-term gains because you simply don't have time to develop sustainable strategies. Your competitive analysis? Just a few forgotten bullet points in a document you rarely open.The most frustrating part? You feel constantly busy but never productive in ways that actually matter—either for your company's growth or your own career advancement.This isn't just an occasional bad day. For many marketers, this is every single day.How Time Audits Sparked A-ha Moments (And Why You Need One)Ever feel like you're working non-stop but getting nowhere? That was me—constantly busy but missing deadlines. Something had to change."I decided to track exactly how I was spending my time. The results shocked me."My Eye-Opening Time ExperimentAfter a particularly brutal month of working every weekend yet still falling behind, I decided to get radical. I tracked every single minute of my workday for an entire week.The process was simple but revealing:* Log each task as I completed it* Note how long it took* Categorize as either "tactical" or "strategic" workI thought I was being strategic. I was wrong.The Shocking Truth: Where Did My Time Go?Out of a 55-hour workweek (yes, you read that right), I spent a measly two hours on actual strategic thinking.That's less than 4% of my time going to high-value projects.The rest? Swallowed by quick-fix tactics and repetitive tasks that felt productive but weren't moving the needle.From Personal Discovery to Department-Wide RevelationWas it just me? I had to know.So I expanded the experiment, asking everyone in marketing to log their tasks for two weeks. The department-wide trend was even more alarming:* 72% of our collective time disappeared into tactical, repetitive tasks* 43 hours per week consumed by content creation across the team* 38 hours weekly spent on campaign management and reportingNo wonder our competitors were starting to outpace us! While we were stuck in the tactical weeds, they were publicly discussing their AI initiatives in earnings calls.The Strategic ...
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    1 Std. und 30 Min.
  • The Business Leaders' Guide to AI 'Aha!' Moments
    May 8 2025
    A few years ago, I spent an entire week buried in a windowless conference room, wrestling quarterly data into something our CEO wouldn't immediately toss in the recycling bin. By Friday afternoon, my mind felt like overcooked spaghetti. Had you told me then that an AI could finish the same job in under an hour—maybe even noticing patterns my caffeine-soaked brain completely missed? I'd have laughed in your face. Yet here we are: AI is no longer a sci-fi sidebar—it's reshaping how we work, think, and compete. But here's the messy truth no one tells you: success with AI isn't about the tech—it's about leadership, culture, and seeing through the smoke and mirrors. Let’s pull back the curtain and unpack what MIT's George Westerman calls the true leadership challenge of AI (with a few embarrassing war stories along the way).The Grinding Reality: Where Data Analysis Goes to Die (and How AI Can Help)I still remember those nights. Bloodshot eyes staring at endless Excel sheets, the office eerily quiet except for the hum of my computer and occasional sighs. Another weekend sacrificed to the data gods. Another family dinner missed.Sound familiar?The Manual Data WastelandI'm not alone in this data purgatory. Financial teams across industries waste 40+ hours monthly just compiling reports. That's an entire workweek lost to data gathering rather than actual analysis! And the worst part? By the time these reports reach decision-makers, the insights are often shallow and outdated.Marketing departments aren't immune either. I've watched talented marketers spend days analyzing campaign performance data that AI could process in minutes. The same tragedy repeats in supply chain management, where humans manually review inventory and make forecasts based on limited patterns they personally recognize.The Hidden Cost of Human-Only AnalysisThe real tragedy isn't just time lost. It's the insights we never see.A manufacturing client of mine stubbornly clung to manual quality control reviews for years. Their defect rates remained mysteriously high despite endless analysis.When they finally implemented an AI powered analysis system, it immediately identified subtle correlations... connections that had remained hidden for years despite dedicated analysis.The AI discovered that particular supplier materials performed poorly under specific temperature conditions - something the team had completely missed. This single insight saved them $2 million annually and reduced defects by a staggering 23%.Beyond Speed: The Competitive EdgeSpeed alone isn't the whole story, tho it helps. The real advantage comes from:* Uncovering hidden patterns humans miss* Making faster strategic pivots* Deploying resources more effectivelyAs Mokrian notes with his "digital divide" concept - the more organizations invest in AI analytics, the wider the performance gap grows between them and competitors still stuck in manual processes.The question isn't whether your industry will be transformed by AI-powered analysis. It's whether you'll be among the transformers or the transformed.And trust me, as someone who's spent countless sleepless nights drowning in spreadsheets, there's a clear winner in that scenario.Burnout, Blind Spots, and the Things No Dashboard Tells YouLet me tell you what's really happening behind those pristine dashboards and impressive charts. I've seen it firsthand: brilliant analysts with specialized degrees and years of experience spending their days... copying, pasting, and cleaning spreadsheets.Eighty percent. That's how much of their time these talented people waste on mind-numbing data prep rather than solving the complex problems they were hired to tackle.The Human Cost We Don't DiscussI watched one of our best data scientists quit last month. Why? Not for more money, but because she couldn't bear another day of Excel gymnastics when she should have been building predictive models.This burnout isn't just an HR problem. It's a strategic catastrophe. The people walking out your door are precisely the ones with both technical skills and domain knowledge—a combination that takes years to develop.Leadership's Blind SpotsWhat keeps me up at night isn't just the talent drain, but what happens at the top. When executives only see what's easy to measure and compile manually, they develop dangerous blind spots.I call it "strategic blindness." It's when your retail team misses an entire customer segment because nobody could analyze enough behavioral data by hand to spot the pattern.This happened to a client last year. Only after automating their customer behavior analysis did they discover a high-value segment that had been completely invisible to their manual methods. This single insight increased their quarterly revenue by 12%.The AI Implementation Reality CheckBut here's where I need to be brutally honest: AI isn't a magic wand. Despite all the slick vendor presentations:"According to recent studies, between seventy, eighty five ...
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    1 Std. und 32 Min.