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

    1D AGO

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

    2D AGO

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

    3D AGO

    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
  4. Reviewer Market: An Executive Playbook to Build a Scalable Internal Marketplace for Human Oversight

    3D AGO

    Reviewer Market: An Executive Playbook to Build a Scalable Internal Marketplace for Human Oversight

    Human review is still the safety valve for high‑stakes AI, but ad‑hoc review pools are costly, inconsistent, and invisible to finance. This episode opens with an executive vignette where inconsistent reviewer quality caused a regulatory complaint and costly rework. Mirko then delivers a decision‑first playbook for creating an internal Reviewer Market: a lightweight marketplace that sells reviewer capacity to product teams, enforces quality via reputation and certification, prices oversight as a measurable input, and funds remediation lanes when SLA breaches occur. The episode explains market mechanics (supply, demand, dynamic pricing, protected quotas), governance (quality tiers, certification, dispute resolution), procurement style clauses for external review vendors, and a prioritized 30–90 day pilot to stand up the first market lane. Listeners leave with board‑read KPIs (coverage, cost-per-decision, reviewer accuracy, remediation burn), practical negotiation language, and three executive actions to turn human oversight from a cost center into a fundable, auditable capability. Subscribe to DataScience.Show to follow the playbook. 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. Feedback Loop Debt: An Executive Playbook to Detect, Quantify & Control Self‑Reinforcing AI Failures

    4D AGO

    Feedback Loop Debt: An Executive Playbook to Detect, Quantify & Control Self‑Reinforcing AI Failures

    Adaptive models and live interventions can create feedback loops that silently amplify bias, inflate costs, or erode customer trust—often long before monitoring alarms ring. This episode opens with a short C‑suite vignette where a personalization engine’s recommendations altered customer behavior and produced a runaway cohort drift that doubled churn. Mirko then delivers a pragmatic, non‑technical executive playbook: a taxonomy of feedback‑loop types (instrumentation, behavioral, economic), lightweight detection signals executives can demand (population elasticity, treatment‑response drift, uplift erosion), a simple method to translate loop dynamics into dollars and runway risk, and prioritized remediation lanes (contain, compensate, retrain, redesign). Listeners leave with a 30–90 day pilot blueprint to instrument one adaptive flow, board‑ready KPIs to track loop exposure, and concrete governance and procurement clauses to ensure vendors and teams cannot unknowingly weaponize product adaptivity. Practical, decision-focused steps so leaders keep adaptive AI an accelerant—not a liability. Subscribe to DataScience.Show to get the one‑page Feedback Loop register. 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
  6. Decision Latency Budgets: An Executive Playbook to Match AI Speed with Business Tempo

    5D AGO

    Decision Latency Budgets: An Executive Playbook to Match AI Speed with Business Tempo

    Executives fund accuracy and uptime but rarely budget for the other half of decision quality: latency. Wrong speed destroys outcomes—slow fraud decisions leak losses, instant personalization can trigger churn, and intermediate delays shift customer behavior. This episode opens with a concise C-level vignette where mismatched decision speed cost margin and customer trust. Mirko delivers a non-technical, executable playbook: classify decisions by tempo and impact (real-time, near-real-time, batched), translate latency into business cost and tolerance windows, set latency budgets and SLOs tied to funding gates, choose architectural and human-in-loop patterns that respect business tempo (sync vs async, canary buffering, degraded-mode defaults), and embed latency clauses into procurement and SLAs. Listeners get a prioritized 30–90 day pilot to instrument one decision flow, a one-page Latency Budget template to brief the board, and three executive actions to convert speed trade-offs into measurable funding and governance. Subscribe to DataScience.Show to get the template. 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
  7. Decision Value Chains: An Executive Playbook to Map, Attribute & Govern Multi‑Model Outcomes

    6D AGO

    Decision Value Chains: An Executive Playbook to Map, Attribute & Govern Multi‑Model Outcomes

    Enterprises increasingly stitch many models—routing, ranking, personalization, fraud, pricing—into single customer journeys. When outcomes deviate, leaders need to know which model, data feed, or orchestration decision produced the impact and who must fund the fix. This episode opens with a concise vignette where a multi‑model checkout flow produced unexpected churn because an upstream reranker amplified bias. Mirko delivers a pragmatic, non‑technical playbook to create a Decision Value Chain: catalog decision links end‑to‑end, define lightweight attribution rules (credit/blame windows, marginal uplift heuristics), surface board‑read signals that tie chain failures to dollars and reputational exposure, and operationalize remediation lanes (monitor, loan funded fix, vendor renegotiate, retire link). Listeners leave with a 30–90 day pilot blueprint to instrument one customer journey, a one‑page Decision Chain register template, and three executive actions to convert opaque model webs into accountable, fundable controls. Subscribe to DataScience.Show to get the Decision Chain register template. 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. Model Change Management: An Executive Playbook for Safe, Auditable Model Updates

    MAR 7

    Model Change Management: An Executive Playbook for Safe, Auditable Model Updates

    Model updates are routine engineering work until one upgrade misroutes revenue, exposes customer data, or breaks a compliance gate. This episode opens with a concise executive vignette where an uncoordinated model roll‑out cost weeks of remediation and lost margin. Mirko then delivers a non‑technical, decision‑first playbook for Model Change Management: define a release taxonomy (patch, retrain, fine‑tune, replacement), require board‑read change requests with risk scoring, align canary and staged rollout patterns to business exposure, mandate tamper‑evident change logs and rollback criteria, and budget a funded remediation runway. The episode gives a prioritized 30–90 day pilot to operationalize change gates for one high‑impact model, procurement language to capture vendor update obligations, and a communication script for customers and regulators. Leaders leave with concrete artifacts to demand from engineering and procurement and a clear next step: subscribe to DataScience.Show for more executive playbooks. 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
See All (53)

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
    53
  • Rating
    Clean
  • Copyright
    © Mirko Peters
  • Show Website
    DataScience Show Podcast