DataScience Show Podcast Podcast Por Mirko Peters arte de portada

DataScience Show Podcast

DataScience Show Podcast

De: 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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Become a supporter of this podcast: https://www.spreaker.com/podcast/datascience-show-podcast--6817783/support.Mirko Peters Economía
Episodios
  • Revenue Forensics: An Executive Playbook to Detect, Attribute, and Stop AI‑Driven Margin Leakage

    Revenue Forensics: An Executive Playbook to Detect, Attribute, and Stop AI‑Driven Margin Leakage
    Mar 15 2026
    Hidden margin leaks from AI—silent mis-calibrations, feedback loops, misrouted decisions, and integration drift—eat profitability long before dashboards raise alarms. This episode opens with a concise C-suite vignette where a personalization stack quietly reduced average order value across a key cohort. Mirko then delivers a non-technical, actionable executive playbook: rapid detection signals to ask for (dollarized deviation curves, cohort delta maps, inference-to-revenue crosswalks), pragmatic attribution patterns to separate model, data, and orchestration causes, and prioritized remediation lanes (contain, compensate, tactical hotfix, funded redesign). Listeners get a 30–90 day pilot blueprint to instrument one revenue-critical flow, board-ready KPIs (leak velocity, attribution confidence, cost-to-remediate), procurement levers to demand financial observability from vendors, and three executive actions to convert transient alerts into funded decisions. Practical, finance-aligned guidance so leaders stop blaming noise and start recovering measurable margin—subscribe to DataScience.Show for the one-page Revenue Forensics checklist. That’s the difference between models and value.

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

    I share practical AI leadership notes on LinkedIn — the kind you can forward internally or reuse in executive discussions.
    Follow Mirko on LinkedIn if you want decision-ready frameworks, not hype.
    Más Menos
    8 m
  • Customer Redress & Remediation: An Executive Playbook for Funded, Trust-Preserving Responses to AI Failures

    Customer Redress & Remediation: An Executive Playbook for Funded, Trust-Preserving Responses to AI Failures
    Mar 14 2026
    AI failures inevitably touch customers—wrong decisions, unfair outcomes, privacy leaks, or harmful recommendations. Boards demand more than apologies: they need an auditable, funded remediation playbook that limits balance‑sheet exposure and repairs trust. This episode opens with a concise C‑suite vignette where an automated decision harmed a customer cohort and public remediation costs ballooned. Mirko then delivers a non‑technical executive playbook: a taxonomy of remediation modes (compensate, correct, reverse, rehabilitate), simple rules to dollarize harm and set remediation tiers, customer-communication scripts that preserve compliance and brand, and operational runbooks (detection → triage → remedy → verification). Listeners get board‑ready KPIs (time-to-remedy, remediation cost-per-incident, recidivism rate), procurement and vendor clauses to demand remediation support, and a prioritized 30–90 day pilot to stand up a Redress Lane for one product. Practical, decision-focused actions so leaders fund fixes that restore value. Subscribe to DataScience.Show for the Redress Lane templates—That’s the difference between models and value.

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

    I share practical AI leadership notes on LinkedIn — the kind you can forward internally or reuse in executive discussions.
    Follow Mirko on LinkedIn if you want decision-ready frameworks, not hype.
    Más Menos
    9 m
  • Consent as Code: An Executive Playbook to Govern Customer Consent Lifecycles for AI

    Consent as Code: An Executive Playbook to Govern Customer Consent Lifecycles for AI
    Mar 13 2026
    Customer consent is no longer a legal footnote—it’s the control plane that determines what AI systems can and cannot do. This episode opens with a concise C‑suite vignette where inconsistent consent handling forced a product rollback and regulatory briefing. Mirko delivers a non‑technical, actionable playbook for implementing Consent-as-Code: standardizing consent schemas, versioned provenance, runtime enforcement, revocation workflows, downstream propagation, and contract clauses that require vendor attestation. The monologue explains how to dollarize consent risk (business exposure from misuses), design a prioritized 30–90 day pilot for one product line, and produce board‑ready KPIs (consent coverage, revocation latency, downstream compliance rate). Leaders leave with a practical checklist and negotiation language to embed consent gates into procurement and governance. Subscribe to DataScience.Show to get the Consent-as-Code template and board brief—That’s the difference between models and value.

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

    I share practical AI leadership notes on LinkedIn — the kind you can forward internally or reuse in executive discussions.
    Follow Mirko on LinkedIn if you want decision-ready frameworks, not hype.
    Más Menos
    8 m
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