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MBA Training Data. Daily strategy in data governance, architecture, analytics and AI, for data leaders and aspiring CDOs. New episode every day at mba-training.com.

  1. 1d ago

    ARTEX bank breaches: why side-door data access is a CDO issue

    How did an attacker using ARTEX, an open-source agentic AI penetration-testing tool, take data on more than 67,000 customers from seven South Korean firms, including Shinhan, KB Kookmin and Hana, without touching core banking? The episode argues the failure was data access design. Broker portals and employee apps returned income, loan limits and resident registration numbers to callers who did not need them, despite nearly 124 billion won in security spending. You come away knowing what the Financial Services Commission ordered, how CrowdStrike linked the attack to Claude Code and DeepSeek, and why basic cyber hygiene matters more than an AI defense product. You also get a concrete plan to inventory every externally reachable system that returns customer data and remove unneeded fields. 0:00 Shinhan breach through a loan broker portal 1:13 ARTEX agentic AI tool and the human attacker 2:38 Side-system data access as a CDO problem 4:42 FSC emergency order on exposed systems 6:06 Hygiene failure or AI-driven attack speed 8:16 Weekly task: audit customer data endpoints

  2. 4d ago

    UK GDPR Article 21: 44,000 objections hit NHS Palantir

    What happens when a data platform is legally defensible, operationally embedded and publicly distrusted at the same time? More than 44,000 people have filed Article 21 objections under UK GDPR against NHS England's Palantir-powered Federated Data Platform, live in 142 trusts, weeks before a February 2027 contract break clause comes into reach. The position here: objection handling is a throughput problem, not a policy document. You come away able to pressure-test your own platform. Check the published benefits claims, 117,000 extra operations, a 14.3% cut in discharge delays, against Foxglove FOI data showing 13 of 41 trusts doing fewer operations, and name the owner and service level for mass objections before someone else does. 0:00 44,000 Article 21 objections to NHS data 1:40 What the Federated Data Platform runs 2:31 Palantir politics versus legal mechanism 3:01 Why patients cannot opt out of FDP 4:09 Testing Palantir's NHS benefit claims 7:28 What transfers to any CDO

  3. 5d ago

    dbt State: lag_tolerance is now a budget decision

    dbt State went generally available in September 2026, so rebuilding every model on a cron schedule is now a choice rather than a default. The position here: the tool cannot tell you how stale each model is allowed to be, and the lag_tolerance config most teams set once and globally is a business decision about decision cadence, not an engineering setting. You leave with a method: meter wasted rebuilds using BigQuery INFORMATION_SCHEMA.JOBS or Snowflake credit consumption, tie tolerance to the decision each model feeds, keep source freshness checks running, read the Explain tab in audit mode, and cap agent queries with resource monitors and maximumBytesBilled. Includes the RxBenefits 59% result, dbt's 15 to 30% range, FinOps Foundation and Flexera 2026 findings. 0:00 Why 60% of scheduled model builds repeat 1:13 What dbt State GA actually changes 2:57 Metering wasted rebuilds before flipping the switch 4:18 Silent semantic drift when models are reused 5:41 Committed spend contracts hide the savings 6:57 Capping autonomous agents in the warehouse

  4. Oct 2

    SB 947: proving who reviewed an AI firing call

    California's No Robo Bosses Act bars employers from disciplining or firing a worker on an automated system's output alone, and from 1 July 2027 a human must independently corroborate that output in writing. The position here is that this reads as an HR matter but lands on data teams, because the statute covers statistical modeling and data analytics, which means attrition models and attendance scores, not only frontier AI. You come away knowing what SB 947 and SB 951 demand, how Cal-WARN notices must now name AI-driven displacement, what the Chamber of Commerce and Senator McNerney argued, why the SB 7 inventory clause was dropped, and how to audit every system scoring a named employee. 0:00 Why SB 947 is a data team problem 0:53 What the No Robo Bosses Act requires 1:52 Proving model lineage for firing decisions 4:17 SB 951 and Cal-WARN AI layoff notices 5:46 Chamber opposition and the vetoed SB 7 7:40 Enforcement odds and your logging checklist

  5. Oct 1

    Churn models in telecom: why uplift beats accuracy

    Why do churn models produce accurate predictions that never move retention numbers? The position here is that most telcos predict churn the week it happens, when the customer has already price-shopped and gone. Telefonica's work came from years of accumulating unglamorous signals such as degraded data speed at a single cell tower, payment timing drift and the gap between plan and actual usage, combined with contract stage and competitor pricing by postcode. You walk away able to separate accuracy from uplift, size the movable middle, defend a holdout control group to a revenue VP, and wire a score into a pre-approved retention offer the agent can act on. References include MIT Sloan Management Review on unactionable analytics and dbt Labs on data cleaning time. 0:00 Catching churn twelve hours too late 1:00 Boring signals that predict churn early 1:54 Why years went into data plumbing 2:31 Accurate scores nobody can act on 3:10 Uplift and the movable middle 4:04 Monday check: did you run a control group

  6. Sep 30

    AI slop detectors: why filtering training data backfires

    Should you strip suspected AI-generated text out of a training set? The position here is no, not blind. Slop detectors are classifiers that flag one to five percent of clean human writing as fake, and they misfire on dense expert prose and standardized legal boilerplate, so teams in financial services and legal have deleted the backbone of their corpus and watched accuracy fall. A recent experiment found filtering degraded performance more than the contamination removed. You leave with a Monday plan: quarantine and hand-sample flagged items rather than deleting, tag provenance at ingest instead of at training time, and read the unknown-provenance estimates from dbt Labs and MIT Sloan Management Review with the vendor incentive in mind. 0:00 Why a filtered dataset made accuracy drop 0:50 How often detectors flag human writing 1:36 Contract summarization corpus gutted by filtering 2:26 Data provenance as a governance problem 3:28 Quarantine flagged data instead of deleting 4:00 Test the detector on known-good data

  7. Sep 29

    Cross-agency data: why 70% of failures are semantic

    When two government agencies define "household" differently, one by mailing address and one by shared cooking facilities, no integration platform reconciles the numbers. This episode argues that roughly 70 percent of cross-agency integration failures trace to the semantic layer rather than the plumbing, and that the median time to reach a trusted cross-agency data agreement is 18 months. You get a worked example from benefits coordination, where food assistance uses an economic household and Medicaid often uses the tax household, plus the trap of mapping household_id to household_id. You leave with a sequencing rule for governance spend, a named-owner test, and a three-entity definition exercise. References include MIT Sloan Management Review and dbt Labs. 0:00 Two agencies, two definitions of household 0:58 Why 70% of integration failures are semantic 2:03 Food assistance and Medicaid household mismatch 3:02 The 18-month median for data agreements 3:40 Shipping a signed definition, not a dashboard 4:18 Monday action: write definitions independently

  8. Sep 27

    Amazon's data flywheel: twelve years of engineering

    Does more data automatically compound into advantage? This episode argues it does not. Drawing on an MIT Sloan Management Review breakdown tracing Amazon's flywheel to its origins, the discussion shows the compounding came from roughly twelve years of feedback loop design, architecture and organizational choices, including teams owning data as a product with an internal consumer they answered to. You come away with a test for your own setup: time to feedback, meaning how long from a customer action until that signal changes what the next customer sees. You also get a check on modern data stack claims from tooling vendors such as dbt Labs, and a working definition of data monetization measured on the P&L. 0:00 Why the data flywheel needs building 1:14 More data does not improve decisions 1:45 Modern data stack is only half 2:53 Data monetization is internal, not external 3:26 Time to feedback as the metric 4:05 Wire one closed loop first

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MBA Training Data. Daily strategy in data governance, architecture, analytics and AI, for data leaders and aspiring CDOs. New episode every day at mba-training.com.

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