DataScience Show Podcast

Mirko Peters
  • 5.0 (2)
  • BUSINESS
  • UPDATED DAILY

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

  1. Synthetic Data Governance: An Executive Playbook to Certify, Procure & Trust Synthetic Training Data

    1D AGO

    Synthetic Data Governance: An Executive Playbook to Certify, Procure & Trust Synthetic Training Data

    Synthetic data is rapidly becoming a core input for training, testing, and privacy-preserving sharing—but it brings unique governance, provenance, and legal trade-offs that boards must fund and control. This non‑technical, executive‑grade monologue opens with two crisp vignettes: a synthetic augmentation that amplified bias in a high-value cohort, and a synthetic test set that masked a downstream production failure. Mirko then delivers a pragmatic playbook: a certification rubric (fidelity, representativeness, privacy leakage, lineage), minimal evidence packs to demand from teams and vendors, conservative heuristics to dollarize synthetic risk vs value, procurement clauses for attestations and sample‑escrow, and a 30–90 day pilot to certify one synthetic pipeline. Listeners leave with board‑read KPIs (synthetic‑coverage %, privacy-leakage score, model‑delta after synthetic augmentation), three immediate executive moves, and a clear subscribe CTA to access a one‑page Synthetic Data 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.

    10 min
  2. Uncertainty Accounting: A C‑Suite Playbook to Measure, Budget & Hedge Model Overconfidence

    4D AGO

    Uncertainty Accounting: A C‑Suite Playbook to Measure, Budget & Hedge Model Overconfidence

    Models don't just make mistakes—they can be confidently wrong. This non‑technical, executive‑grade monologue shows leaders how to turn abstract uncertainty into board‑read controls: a taxonomy of uncertainty (aleatoric, epistemic, distributional shift), minimal evidence packs to demand (calibration curves, prediction intervals, decision‑aware confidence histograms), pragmatic methods to dollarize overconfidence exposure, and a short menu of hedges (conservative defaults, staged funding reserves, human‑review corridors, insurance triggers). Mirko opens with two crisp vignettes—a loan decline with misplaced confidence and a recommender that confidently amplified churn—then outlines governance, procurement language to require uncertainty hooks from vendors, and a 30–90 day pilot to instrument one critical flow. Leaders leave with board KPIs (calibration gap, uncertainty burn rate, hedge coverage %) and three immediate moves. Subscribe to DataScience.Show to turn uncertainty into auditable capital. 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.

    9 min
  3. Purchased Data Signals: An Executive Playbook to Certify, Price, and Failover Third‑Party Feeds

    5D AGO

    Purchased Data Signals: An Executive Playbook to Certify, Price, and Failover Third‑Party Feeds

    Enterprises rely on purchased data signals—identity graphs, geolocation, enrichment feeds, credit scores—to power decisions, yet these feeds bring hidden quality, licensing, privacy, and continuity risks. This 20‑minute executive monologue equips C‑suite leaders with a compact, non‑technical playbook to govern third‑party signals as productized inputs: a simple certification rubric (freshness, provenance, licensing, sampling fidelity), economic patterns to price and chargeback signal cost vs. value, practical fallbacks and synthetic replacement lanes, and procurement clauses to demand attestations, audit access, and funded rollback credits. Mirko opens with two concise vignettes—a geo‑feed drift that misrouted delivery and a purchased enrichment that violated a consent clause—then walks listeners through executive KPIs to demand, a prioritized 30–90 day pilot to certify one critical feed, and three immediate moves to convert signal risk into funded executive controls. Subscribe to DataScience.Show for the one‑page Signal Certification 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.

    10 min
  4. Algorithmic Pricing Governance: A C‑Suite Playbook to Price with Models, Protect Margin, and Manage Fairness

    APR 5

    Algorithmic Pricing Governance: A C‑Suite Playbook to Price with Models, Protect Margin, and Manage Fairness

    Algorithmic pricing can turbocharge revenue but also quietly erode margin, invite regulatory scrutiny, and damage customer trust when incentives, data, or orchestration misalign. This 20‑minute executive monologue gives C‑level leaders a practical, non‑technical playbook to govern pricing models as a funded, auditable capability. Mirko opens with two concise vignettes—a dynamic discounting rule that collapsed gross margin and a personalized offer loop that triggered complaints—then walks listeners through a decision-first sequence: classify pricing lanes by leverage and legal sensitivity, demand minimal evidence packs from product and vendors (price provenance, simulation manifests, uplift holdouts), set monetary SLOs and exposure budgets, and budget a remediation runway for pricing failures. The episode supplies board‑read KPIs (price-exposure ratio, realized margin delta, fairness-disparity score), procurement snippets to require verifiable pricing contracts, and a prioritized 30–90 day pilot to govern one pricing lane. Listeners leave with three immediate executive moves and a subscribe CTA to access 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.

    9 min
  5. Model Concentration Risk: An Executive Playbook to Measure, Diversify, and Insure Single-Point AI Failures

    MAR 27

    Model Concentration Risk: An Executive Playbook to Measure, Diversify, and Insure Single-Point AI Failures

    Organizations increasingly rely on a small set of models, vendors, or datasets—creating concentration that can turn a single outage, vendor change, or model failure into enterprise-wide disruption. This 20‑minute executive monologue gives C-level leaders a compact, non-technical playbook to treat concentration as a measurable, fundable risk. Mirko opens with a concise vignette where a single third‑party reranker outage paused checkout across regions, costing market share and board time. Listeners will get a simple Model Concentration Index (MCI) to calculate exposure, mapped thresholds for board action, pragmatic diversification patterns (multi-vendor ensembles, internal fallbacks, synthetic backup flows), and contract/insurance tactics (performance corridors, escrowed artifacts, parametric cover). The episode closes with a 30–90 day pilot to map top-10 exposures, three board-ready KPIs, and concrete procurement language executives can present to counsel. Subscribe to DataScience.Show. 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.

    9 min
  6. Revenue Forensics: An Executive Playbook to Detect, Attribute, and Stop AI‑Driven Margin Leakage

    MAR 15

    Revenue Forensics: An Executive Playbook to Detect, Attribute, and Stop AI‑Driven Margin Leakage

    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.

    8 min
  7. Customer Redress & Remediation: An Executive Playbook for Funded, Trust-Preserving Responses to AI Failures

    MAR 14

    Customer Redress & Remediation: An Executive Playbook for Funded, Trust-Preserving Responses to AI Failures

    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.

    9 min
  8. Consent as Code: An Executive Playbook to Govern Customer Consent Lifecycles for AI

    MAR 13

    Consent as Code: An Executive Playbook to Govern Customer Consent Lifecycles for AI

    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.

    8 min
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Ratings & Reviews

5
out of 5
2 Ratings

About

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

Information

  • Creator
    Mirko Peters
  • Years Active
    2025 - 2026
  • Episodes
    58
  • Rating
    Clean
  • Copyright
    © Mirko Peters
  • Show Website
    DataScience Show Podcast