MBA Training

MBA Training Data

A Management , Business and Education podcast

About the show

MBA Training Data. Strategy in data governance, architecture, analytics and AI, for data leaders and aspiring CDOs. New episodes every week, transcripts at mba-training.com.

Newest episodes

Brave Search API lawsuit: what it means for AI data buyers

Nov 10, 2026 10 min

The New York Times, Daily News, Chicago Tribune and Orlando Sentinel have sued Brave, saying it scraped their journalism into a 30-billion-page index and sold it through its Search API to AI companies including Anthropic, Perplexity, Mistral, xAI and Cohere. The hosts ask whether buyers of web data share the blame. One argues the buyers belong in the dock; the other says the evidence against them is thin and a supplier lawsuit is the wrong place to settle it. You come away able to add provenance, crawler transparency and vendor indemnity to search API procurement for RAG and agents, and to treat a feed that could be enjoined as a dependency risk. 0:00 Times test returns its own article 0:41 Who sued Brave and who buys its API 3:01 Brave's unnamed crawler and robots.txt 3:52 Brave's fair use defence and News Corp 5:01 Should Anthropic and Perplexity be liable 8:05 Search API procurement, provenance and indemnity

Patient matching: cap overlays before chasing duplicate rates

Nov 3, 2026 10 min

Hospitals are pushed to cut duplicate patient records, and the fastest route is more aggressive auto-merging. This episode asks what that does to overlay risk. Using OneFlorida's 99.3% matching precision (7 false merges per 1,000), the BMJ Quality and Safety duplicate mortality study, an ECRI overlay case, the MATCH IT Act and TEFCA, the guest argues for capping overlays at one false merge per 1,000 automated merges, while the host pushes back on duplicate deaths. Listeners leave able to set an overlay ceiling, split matches into auto-merge, human review and link-only bands, set per-source thresholds as Intermountain did, sample past merges, and weigh AHIMA cost figures and equity risks for clinical AI. 0:00 Duplicate records vs overlays, BMJ mortality study 1:41 OneFlorida's 7 false merges per 1,000 3:55 MATCH IT Act and match rate standards 4:30 Capping overlays versus clearing duplicate backlogs 6:26 Overlay costs and linking instead of merging 8:02 Clinical AI and uneven matching errors

Reddit login wall: a price tag on its AI data licensing

Oct 30, 2026 8 min

Is Reddit's move to make most of the site login-only a panic response to Google AI Mode cutting search referrals, or a pricing move? The episode argues the wall matters less for casual readers than for AI companies, since it closes the free logged-out route scrapers use while Reddit renegotiates its data licensing deal with Google. The host pushes back that lurkers are the top of the funnel. You come away able to separate a vanity metric like daily active uniques from revenue, see how one archive supports two money streams (targeted ads to logged-in users and licensing to model builders, including OpenAI and Google), and weigh the flywheel risk of a stale archive and what it means for brands' SEO. 0:00 Google AI Mode cuts Reddit search traffic 1:40 Who the Reddit login wall targets 2:27 Reddit's data licensing talks with Google 4:07 Daily active uniques versus engaged users 6:13 Ads, licensing and the data flywheel 6:54 What the wall means for SEO

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All episodes

Brave Search API lawsuit: what it means for AI data buyers

Nov 10, 2026 10 min

Patient matching: cap overlays before chasing duplicate rates

Nov 3, 2026 10 min

Reddit login wall: a price tag on its AI data licensing

Oct 30, 2026 8 min

ETS data breach: one stolen SFTP login exposed student records

Oct 27, 2026 9 min

How did one compromised login give an intruder nearly four months inside the SFTP server where California swapped student test data with Educational Testing Service (ETS)? The files held names, test results, disability status, Section 504 status and records on students who left as far back as 2006. The hosts split on blame: ETS for the dwell time, or the state for pooling every child's record at one vendor behind one login. You walk away able to audit your own vendor file drops: name an owner, check whether MFA is on, enforce data minimisation with a scheduled deletion job, and treat contract clauses as no substitute for controls that block a bulk download at midnight. 0:00 ETS breach via stolen SFTP login 1:29 Disability and Section 504 records exposed 2:33 Records since 2006 and data minimisation 4:03 Blame debate: ETS or state design 6:12 Notification delays, lawsuits and fallout 7:26 Unowned file drops and missing MFA

NAV error threshold: back-solving reconciliation tolerances

Oct 20, 2026 10 min

Who pays when a fund's NAV is wrong and the manager never saw the error? Under CSSF Circular 24/856 the investment fund manager corrects and compensates, even when the administrator calculated the price. The episode argues that managers should own one position and pricing record, backed by a written pricing policy, rather than inheriting whatever the administrator used. Listeners learn to back-solve reconciliation tolerances from the 0.5% NAV materiality threshold for fixed income UCITS funds: a tenth of it per break for same-day ownership, a quarter of it in open breaks as a NAV gate. Also covered: the aggregation clause, EU T+1, Invesco's identifier consolidation, circular matching, Bloomberg pricing drift and the SEC's Madison Capital Funding settlement. 0:00 Who pays for NAV errors under CSSF 24/856 0:59 Custodian, administrator and IBOR disagree on positions 3:23 Back-solving tolerances from the NAV threshold 5:56 NAV gate versus T+1 cut-offs 7:04 Identifiers, circular matching and price challenge 9:40 Reporting breaks to the board

Asos hack: why marketing tools hold your Snowflake keys

Oct 13, 2026 9 min

How did hackers send a ransom note through the real Asos app? Reporting points to a conned employee and, per researcher Kevin Beaumont, Simon AI, a composable customer data platform owned by Monetate that reads straight from Snowflake with MFA optional and off by default. That route is unconfirmed. The episode argues the real failure is ownership: marketing bought it, data engineering wired it to the warehouse, and nobody owned the login. You leave with a concrete audit: list every third-party tool holding warehouse credentials, check whether MFA is enforced or only available, check whether it can message customers, and fix the risky ones with mandatory MFA or single sign-on before Friday. 0:00 Asos app push notification ransom note 1:55 Social engineering and the Simon AI CDP 3:31 Optional MFA on Snowflake-connected tools 6:17 Why composable CDPs are a data leader problem 7:31 Breach scale, extortion deadline, open questions 8:28 Auditing third-party warehouse credentials this week

dbt State and agent-ready data: why declared meaning comes first

Oct 13, 2026 10 min

Agents dominated dbt Summit, but does smart orchestration or a context layer make data agent-ready on its own? The episode argues no. Fanatics Betting and Gaming saw about 0.2% model reuse from dbt State until it declared source freshness and model-level SLAs, which raised reuse to roughly 15%. The same pattern runs through Fivetran's Context Layer, AI audit trails and token costs. Listeners learn how dbt State is billed per daily active target table, including tests, why the Context Layer's beta status needs checking in writing, how git history serves as an audit trail, and how dbt models served through the MCP server cut Gong transcript tokens by 20x or more. 0:00 Fanatics and the 0.2% dbt State reuse rate 2:21 dbt State pricing and per-test charges 3:53 Fivetran Context Layer: early access risks 6:26 Auditing AI agents without a rulebook 7:49 Why token costs belong to data teams 9:17 Declare meaning before agents act

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

Oct 9, 2026 10 min

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

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

Oct 6, 2026 9 min

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

dbt State: lag_tolerance is now a budget decision

Oct 6, 2026 9 min

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

SB 947: proving who reviewed an AI firing call

Oct 2, 2026 10 min

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

Churn models in telecom: why uplift beats accuracy

Oct 1, 2026 4 min

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

AI slop detectors: why filtering training data backfires

Sep 30, 2026 4 min

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

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

Sep 29, 2026 5 min

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

Amazon's data flywheel: twelve years of engineering

Sep 27, 2026 4 min

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

Boeing component traceability: pay for the birth record

Sep 26, 2026 4 min

When a 737 MAX fastener is installed without a birth record, who owns the defect? The answer is the assembler, not the tier-3 shop that made the part. This episode takes apart Boeing's multi-year traceability program, the digital genealogy record that follows one component from raw metal to installed part, and why three-quarters of the cost sits in supplier process rather than software. DBT Labs' claimed 30% reconciliation tax is weighed against MIT Sloan Management Review's manufacturing data work. You leave knowing how to write supplier clauses that pay for clean records and penalize broken ones, how to scope traceability to your five most recall-critical parts, and why orphaned part numbers break chains of custody. 0:00 Why a 737 fastener lacks a birth record 0:48 Digital genealogy across 12,000 suppliers 1:23 Orphaned records and the reconciliation tax 2:22 Why buying a traceability platform fails 3:10 What a mid-sized parts maker should copy 3:57 Cost of building traceability versus ignoring it

Semantic layers: one revenue number for the board

Sep 25, 2026 4 min

What happens when finance, sales and product each compute revenue their own way? The episode opens on a board meeting where three presenters defend three different quarterly numbers, and argues that the cost is not bad data but frozen decisions while teams re-litigate whose spreadsheet counts. Finance recognizes revenue on fulfillment, sales on signature, product on ship date, and each team's bonus depends on its version. You come away with a build sequence for a semantic layer: inventory the 12 to 15 metrics that appear in board decks and budget fights, lock each to one written formula with finance and sales signing off together, then choose tooling. The discussion weighs a dbt Labs vendor claim against MIT Sloan Management Review's work on trust in shared numbers, and covers churn, customer acquisition cost, and the CDO's refereeing role. 0:00 Three revenue numbers in one board deck 0:47 Why finance, sales and product disagree 1:24 What a semantic layer actually is 2:03 Inventory metrics and lock the formulas 2:40 dbt Labs claims and the trust problem 3:20 Shipping five metrics before committee kills it

Identity resolution in pharma: one HCP, six records

Sep 24, 2026 4 min

Why does a single cardiologist show up as six separate entities across a pharma company's CRM, purchased claims data and analytics warehouse, and what does that cost? The position here is blunt: identity resolution is not a tool purchase, it is plumbing. Bad matching splits one high prescriber into six low ones, which skews sample allocation, wastes temperature controlled product, and can hide adverse event patterns in pharmacovigilance reporting that regulators will not excuse. You get a working approach: make the federal NPI registry the source of truth, run nightly matches, write down survivorship rules, and name one accountable owner with budget for the prescriber master record. References include DBT Labs and MIT Sloan Management Review, plus a master data example at Novartis. 0:00 Six records for one cardiologist 1:09 Sampling waste from split prescriber records 2:02 Pharmacovigilance risk and regulator warning letters 2:53 Identity as plumbing: NPI golden record 3:51 Naming an owner for prescriber master data 4:12 Monday morning audit of top 10 prescribers

AI agents querying your warehouse: measure before rebuilding

Sep 23, 2026 4 min

Fivetran and dbt Labs used dbt Summit to argue that AI agents, not human analysts, are now the main consumer of enterprise data. The position here is that the architectural shift is real but the urgency is a sales pitch, and that re-platforming off a keynote is the expensive mistake. You come away able to test dbt Labs' claim that 30% of warehouse queries are machine-generated against your own logs, spot the failure mode where agents pass nonsense results downstream with no scar tissue, make the case for a written semantic layer over new tooling, and set a query budget before an agent touches production. References include O'Reilly Radar and MIT Sloan Management Review. 0:00 Agents as the new data consumer 0:44 What changes when a bot queries 1:12 dbt's 30% machine-generated query claim 2:03 Metadata gaps and the semantic layer 3:04 Compute costs and query budgets 3:49 Check service account query share

AML pipelines: three design calls examiners test

Sep 22, 2026 5 min

Why do fintech AML pipelines fail regulatory examination when the models themselves are sound? The position here is that examiners do not score area under the curve, they ask reproducibility questions, and most data architectures cannot rebuild a March alert with March data. Three design decisions decide the outcome: immutable timestamped feature storage, documentation that records who approved a feature and which regulation it maps to, and explanation captured at inference time rather than manufactured later. You walk away knowing why dbt column level lineage maps transformations but not business intent, why SHAP values may be plausible rather than faithful, and how to run a one hour reproduction test on your last high value alert. 0:00 The March alert nobody could explain 1:08 Why model accuracy does not pass exams 2:03 Lineage tooling cannot show business intent 3:18 Post hoc explainers versus inference-time logging 4:16 Retrofit costs and a Monday reproduction test

DuckDB and text-to-SQL: redesign the analytics team

Sep 21, 2026 4 min

If a manager can type "show me last quarter's churn by region" and get an answer in ten seconds, what is the analytics team for? The position here is that DuckDB plus language models that write SQL is a workforce redesign, not a tooling upgrade. The ad hoc request queue goes first, dashboard building is half gone, and the analytics engineer who owns the semantic layer becomes the centre of the team. You get a concrete redesign of a team of eight, the reason a model will compute churn three contradictory ways, the caveat on dbt Labs and O'Reilly survey numbers, and a Monday test: count how many live definitions exist for your five most quoted metrics. 0:00 Why analysts fear natural-language BI 0:41 What changed: DuckDB plus text-to-SQL 1:25 The standard analytics team org chart 2:21 Why dashboards and metric definitions survive 3:42 Redesigning a team of eight 4:18 Monday morning: audit your five metrics

Grey market leaks: Richemont's serial number fix

Sep 20, 2026 5 min

Genuine Cartier and IWC pieces were surfacing in unauthorised Asian markets at 20 to 35 percent discounts, and the villain was Richemont's own distributors. The position taken here is blunt: anti-counterfeiting gets the attention, but the grey market is the larger financial wound, and distribution integrity is a data governance problem wearing a legal costume. You get the sequence Richemont followed to build a product-level data layer where each serial number is a live record updated at every handoff, the 40-odd maison fight over one shared definition of a serial event, and why enforcement worked as deterrence rather than analytics. Referenced sources include dbt Labs on 18-month data modeling rebuilds, O'Reilly Radar and MIT Sloan Management Review on ownership failures. 0:00 Genuine IWC watches discounted in Bangkok 1:00 Why wholesale arbitrage is hard to admit 1:57 The governance cost of one serial schema 2:57 Deterrence beat detection in the grey market 3:44 Define the product record before buying tooling 4:25 Trace ten units of your top product

Data flywheel: why loop latency beats data volume

Sep 19, 2026 4 min

Board decks show a circular arrow labelled data flywheel, but few teams can say what spins it. This episode separates compounding from speed: a faster pipeline processes the same low value data quicker, while a real flywheel makes each new record more valuable because of the data already held, as Google search ranking does with click data. It examines the dbt Labs claim that pipeline automation cuts time to insight by around 60 percent, plus findings from MIT Sloan Management Review on flattening accuracy gains and O'Reilly Radar on warehouses that never feed decisions. You leave with three failure modes to test for, data saturation, data decay and a loop that never reaches the product, and a single metric to measure this week: the days between data arriving and a decision changing. 0:00 What the data flywheel actually claims 0:51 dbt Labs pipeline speed claim, examined 1:55 Three places the flywheel breaks 3:14 Why Amazon and Netflix loops compound 4:00 Measure your decision latency Monday

MES and ERP integration: the real Siemens Amberg win

Sep 18, 2026 5 min

Siemens Amberg is usually told as a robotics story, with 99.99885% quality and a heavily automated line. The position here is different: the recent gains came from making the Manufacturing Execution System and ERP agree on reality, not from more sensors. The route was treating manufacturing data as a product with owners, contracts and quality checks, rather than chasing one monolithic platform. You get the specific failure mode to look for, where MES counts a unit complete when it leaves the line and ERP counts it when booked to inventory, and what that gap does to supply forecasts. References include MIT Sloan Management Review, O'Reilly Radar and dbt Labs, plus a first project any CDO can start with a meeting. 0:00 What the Siemens Amberg case studies leave out 1:09 Why more automation is the wrong lesson 2:13 Hours instead of weeks: the real Amberg effect 2:47 Dashboards fail without data contracts 3:59 Does this transfer to mid-sized plants? 4:33 Monday morning MES and ERP unit count

ML in production: why models die after the notebook

Sep 17, 2026 4 min

Why do most machine learning projects never leave the lab? The position here is blunt: the model is about ten percent of the work and the other ninety percent is pipelines, monitoring and ownership. The episode walks through the three failure points at the notebook to production handoff, data drift, unowned pipelines after launch, and systems never designed for retraining, and why a drifting model costs money quietly instead of paging you at midnight. You come away able to name the roles that fix this, the ML engineer and the data engineer, read vendor claims from DBT Labs against O'Reilly Radar and MIT Sloan Management Review, apply data as a product, and ask the accountability question before funding an AI project. 0:00 Why ML projects die after the notebook 1:10 Three places the handoff snaps 2:02 The ML engineer nobody budgets for 2:25 Data as a product, not byproduct 3:25 Why buying a platform does not help 4:07 The one question to ask before greenlighting

Data literacy: measure decision quality, not training

Sep 16, 2026 4 min

Why do data literacy programs teach medians, filters and chart types and still leave the business running on gut feeling? The position here is blunt: knowledge and behavior are different things, and almost nobody measures the second one. The fix is to build training around real recurring decisions like pricing, hiring and inventory, and to require anyone proposing a decision to state in advance what evidence would change their mind. You come away with a way to score decision quality across ten recurring decisions before and after a program, a read on Booking.com's experimentation default, and evidence from MIT Sloan Management Review, O'Reilly Radar and DBT Labs on why culture and trust, not skills, are the bottleneck. 0:00 Why most data literacy programs fail 1:01 Designing training around real decisions 1:50 Booking.com experimentation and workflow learning 2:22 Measuring decision quality over six months 3:15 Executive committee is the real bottleneck 4:06 Monday morning test for any decision

GDPR retention and minimization: the real compliance gap

Sep 15, 2026 4 min

Is a consent banner and a privacy notice enough to call a GDPR program finished? The position here is no. Consent management got budget because customers see it, while retention and data minimization stayed invisible and unfunded. Modern data tooling in 2026, including lineage work pushed by vendors like dbt Labs, now makes every copy of a customer email visible, and research from O'Reilly and MIT Sloan Management Review points to personal data being kept with no defined deletion schedule. You walk away able to treat unused personal data as a liability, defend against data subject access requests, hash or aggregate identifying fields at ingestion, and set a retention schedule on your largest personal data table. 0:00 Why GDPR compliance claims are self-deceived 0:54 How 2026 data lineage tools expose hoarding 1:48 Retention with no deletion schedule 2:19 Storage is cheap but breaches are not 3:15 Minimization at ingestion beats deleting later 4:06 Monday morning: set one retention schedule

Alternative credit scoring: how Tala lends without bureaus

Sep 14, 2026 5 min

Can you underwrite a loan for someone who has no credit bureau file at all? Tala says yes, and the episode explains how: permissioned smartphone data such as contact counts, airtime top-up timing and money movement frequency, used as behavioral proxies for the habits a bureau score normally captures. The position taken here is that Tala's edge was never a cleverer algorithm. It was owning a data source nobody else collected, a point MIT Sloan Management Review has argued for years. You come away knowing how model drift erodes a live lending model, why retraining and freshness tests matter more than model choice, and the limit of behavioral data once borrowers start performing for the algorithm. It closes with a one-week audit of your three most important data sources. 0:00 Lending to 2 billion people without credit files 0:42 Smartphone data as a behavioral proxy 1:24 Why the data pipeline is the moat 2:14 Model drift and testing your inputs 3:27 Can a copycat buy this data advantage 4:21 Audit your three most important sources

Mistral's $3.5B raise: why open weights favor portability

Sep 13, 2026 4 min

Mistral's September 2026 raise of $3.5 billion made open-weight models a credible enterprise option, and most data leaders read that as a signal to stop buying and start building. The position here is the opposite: capability parity raises the ceiling, not the floor, and the hard part was never the model. It is the data plumbing and the retrieval layer, a point O'Reilly's 2026 adoption research supports. You get the three conditions that justify self-hosting, the token volume crossover point DBT Labs published (with the caveat that they sell data tooling), and the MIT Sloan Management Review argument that open weights are worth having for optionality. You leave with a lock-in audit to run on your top three AI use cases. 0:00 Mistral's $3.5 billion raise in context 0:29 Open weights versus open source explained 0:56 The hidden cost of self-hosting models 2:07 Three conditions for building in-house 2:41 Token volume crossover: API versus owning hardware 3:19 Optionality as insurance and Monday's audit

Decision intelligence: engineer the decision, not the dashboard

Sep 11, 2026 4 min

Why do organisations with hundreds of dashboards still decide badly? The answer here is that the numbers arrive nowhere near the moment of choice. A support agent deciding on a refund needs lifetime value, refund history and churn risk inside the ticket, not in a tool they have no login for. Embedded analytics is the plumbing; decision intelligence is treating the recurring decision as the thing you design around, then working backwards. You walk away with three tests for picking a decision worth engineering (frequent, consequential, currently made blind), a rule for what to leave alone, and a warning on reading DBT Labs vendor figures, with supporting points from O'Reilly Radar and MIT Sloan Management Review. 0:00 Why dashboards get admired and ignored 1:04 Embedded analytics versus decision intelligence 2:09 Why full automation is the trap 2:43 DBT Labs numbers and vendor caution 3:30 Three tests for decisions worth engineering 4:13 Sit with the decision maker Monday

GDPR versus GxP audit trails: the deletion trap

Sep 10, 2026 5 min

Pharma data sits under three rulebooks at once: GxP quality regulation, the FDA's 21 CFR Part 11 electronic records rule, and GDPR. The first two say keep everything and prove everything. The third says erase on request. This episode takes the position that the damage happens where they conflict, when a right to be forgotten workflow reaches into a Part 11 audit trail and makes it look modified to an inspector. You come away able to audit your own deletion path, apply pseudonymization with a separately held key, use the GDPR research exemption by design, and argue why the chief data officer should own all three regimes. References include MIT Sloan Management Review and dbt Labs. 0:00 Three regimes pointing at one record 1:03 What GxP demands from the audit trail 1:54 GDPR erasure against permanent retention 2:39 How deletion scripts break Part 11 records 3:13 Pseudonymization and the GDPR research exemption 4:18 Who owns the conflict, and Monday's fix

Open data portals: publish fewer datasets as products

Sep 9, 2026 5 min

Government portals hold thousands of datasets that get downloaded once and never opened again. The position here is that dataset count is a vanity metric and repeat use is the real measure of a portal's health, citing Data.gov's 300,000 plus datasets and their dead long tail. The answer is to treat a dataset like a product with an owner, a release schedule and documentation, and to retire the rest. You will learn how to separate the needs of journalists, advocates and oversight bodies, why Transport for London's live feed produced CityMapper and tens of millions of pounds in value, how to read public records requests as a demand signal, and why a plain language data dictionary decides whether a dataset gets cited. 0:00 Why open data portals become graveyards 0:44 Repeat downloads beat dataset counts 1:12 Journalists, advocates and auditors want different things 2:08 Transport for London and CityMapper 3:20 Use records requests to pick datasets 3:46 Data dictionaries and named dataset owners

Fanatics data platform ROI: making the board care

Sep 8, 2026 4 min

How does a chief data officer get a board to fund a data platform instead of tolerating it? The answer here is attribution. Fanatics stopped presenting infrastructure cost and presented measurable business output, treating the platform as a profit and loss line. The episode walks through the use cases they attached numbers to, including pricing, inventory markdowns and fraud detection during big jersey drops, and flags that the widely cited dbt Labs figures come from a vendor and need cross-checking against sources like MIT Sloan Management Review. You finish able to pick one money decision, price the delta your data creates, and present it as a conservative, likely and optimistic range rather than a single hero number a CFO can dismantle. 0:00 How Fanatics won board attention for data 0:37 Reframing the data platform as a P&L line 1:34 Why vendor case studies need cross-checking 2:38 Building a defensible dollar figure from one decision 3:34 Accountability to the P&L changes data culture 4:18 The one number to bring your CFO

Load forecasting: why DER signals break utility models

Sep 7, 2026 5 min

Utility teams still treat load forecasting as a regression against temperature and industrial schedules. This episode argues that world is gone. Rooftop solar can flip the sign of the weather relationship, a passing cloud can return 40 megawatts in 90 seconds, and a midnight EV rate discount can synchronize 10,000 chargers into a spike that never existed before. Bolting DER signals onto the old model produces a more complicated wrong answer. You come away knowing where the money leaks, through NERC reliability reports and rate cases where a 2% error becomes a nine-figure conversation, why endogeneity breaks price-sensitive models, what probabilistic forecasting with confidence bands gives operators, and why dbt Labs style version control keeps models auditable without telling you they are wrong. 0:00 Why load forecasting stopped being solved 0:46 Rooftop solar flips the weather relationship 1:35 EV charging timers and rate-driven spikes 2:25 Forecast error in NERC reports and rate cases 3:30 Probabilistic forecasting and confidence bands 4:26 Audit your rate changes Monday morning

ELT stack: where dbt, ingestion and orchestration break

Sep 6, 2026 4 min

How do the three layers of a modern ELT stack actually connect, and why do pipelines still produce wrong numbers when every tool in them is good? The position here is that ingestion, dbt and orchestration are each mature, but the joints between them are untested, and that is where bad data comes from. You get a plain definition of extract, load, transform and why cheap storage in Snowflake, BigQuery and Databricks flipped ETL around. You also get the cost picture on Fivetran monthly active rows versus open source Airbyte, what dbt adds through version control, tests and documentation, what Airflow and Dagster decide about run order, and a one hour test to catch upstream schema changes. 0:00 What ELT means and why it replaced ETL 1:09 Ingestion tools: Fivetran pricing versus Airbyte 1:50 dbt: SQL transformations treated as software 2:44 Orchestration with Airflow and Dagster 3:20 Schema drift at the ingestion to dbt seam 4:15 The boundary test to build Monday

Data ROI to the board: why attribution models backfire

Sep 5, 2026 4 min

Why do chief data officers present cost savings and revenue attribution to the board and still lose budget? The position here is that the problem is the mental model, not the numbers. Attribution percentages like "our model drove 12% of pipeline" invite the board to argue about credit, and manufactured precision reads as hiding rather than rigour. Cost savings is the language of a department fighting for survival. You walk away able to reframe the conversation around decisions improved and bets de-risked, using a retailer example where a demand model caught a forecast inflated by 30% before a nine-figure inventory commitment, and able to read Snowflake and Databricks ROI calculators with proper scepticism. 0:00 Why cost savings dashboards lose budget 0:44 Justifying spend loses the frame 1:43 Retailer catches an inflated demand forecast 2:29 Selling capability instead of claiming credit 3:18 Why vendor ROI calculators mislead boards 4:10 One decision to bring to the board

Data contracts: how JPMorgan Chase named data owners

Sep 4, 2026 4 min

Who owns a dataset, and what happens when it breaks? JPMorgan Chase could not answer that for years across hundreds of business lines, so it replaced governance policy documents with data contracts covering more than 50 domains, each with a named person who signs off on format, quality, freshness and escalation. The position here: accountability without a name attached is theater. You get the design that avoids organizational paralysis, contracts strict at the core and light at the edges, where consumers subscribe to a published interface instead of renegotiating each request. You also get the failure mode being avoided, silent schema changes, the lineage case regulators care about, a caution on the vendor-quoted 60% cleanup figure from Monte Carlo and Anomalo, and a one-page contract to write this week. 0:00 What a data contract actually is 0:40 Why nobody owned the data 1:17 Named owners beat team accountability 2:19 Silent schema changes break dashboards 2:50 Lineage, regulators and analyst cleanup time 3:41 One-page data contract to start Monday

Data literacy programs: change decisions, not courses

Sep 3, 2026 4 min

Does data literacy training change how a business decides anything? The position here is blunt: around 80 percent of programs teach people to pass a quiz, and Gartner surveys keep finding chief data officers who cannot name a business metric their literacy effort moved. Vocabulary like statistical significance is not the same as a category manager killing a promotion because the lift sits inside the margin of error. You come away with a working method: pick the recurring decisions, baseline them, and put the number in front of the decision maker at the moment of choice. Examples include a European retailer's delisting rule worth low single-digit millions a year, and a bank that halved model overrides by requiring written justification. 0:00 Why most data literacy programs fail 1:06 Start from decisions, not the syllabus 1:52 Changing defaults instead of teaching people 2:30 When managers override the numbers 3:13 Attributing ROI and judging literacy vendors 4:00 Three decisions to fix Monday morning

Zero-trust data access: what CDOs need to check first

Sep 2, 2026 4 min

Zero-trust appears on every vendor slide, but most explanations stop at the network perimeter. This episode asks what the model means at the data layer, where a Chief Data Officer actually works, and argues that zero-trust is a posture you maintain rather than a product you buy. The position is blunt: classification comes before technology, and access rules written without honest data tiering create friction people route around. Listeners come away able to separate network zero-trust from data zero-trust, apply the three checks at the moment of access (identity, context, data sensitivity), tier data as public, internal, confidential or regulated, and run a five-dataset access audit. Includes Microsoft Purview figures on unlocated sensitive data and an insurer rollout that failed. 0:00 What zero-trust means before the marketing 1:03 Identity, context and data sensitivity at access 1:51 When authentication friction drives workarounds 2:21 Classifying data into four tiers first 3:05 Why rip and replace is unnecessary 3:52 The Monday morning access audit

Master data management: how Salesforce made MDM stick

Sep 1, 2026 4 min

Can a company that sells data quality run on fragmented records of its own? Salesforce did for years, inheriting duplicate customer and product records from Tableau, Slack, MuleSoft and Demandware until the same account appeared five times across five systems and quietly inflated its own pipeline numbers. The position here is that master data management fails the moment it is funded like a project with a ribbon cutting. You come away with the three moves Salesforce used: name one accountable owner per data domain, sequence the revenue critical records first, and embed duplicate checks at the point of entry. Also covered: Gartner's $13 million poor data quality estimate, the 10 to 1 prevention versus remediation ratio, and why large language models amplify bad master data rather than fix it. 0:00 Salesforce's own fragmented customer records 0:46 Why MDM dies as a project 1:15 Account hierarchies and inflated pipeline 2:14 Three fixes any CDO can copy 3:07 Why prevention loses budget to cleanup 3:36 Why AI amplifies bad master data

Feature stores and the ML data supply chain: what CDOs actually need to understand

Aug 30, 2026 4 min

Feature stores sit at the intersection of data engineering and machine learning operations, yet most organizations treat them as a tooling decision rather than a strategic one. This article explains how they work, why the architectural choice matters at the CDO level, and where the tradeoff between standardization and flexibility bites hardest.

Pricing and packaging a data product for external revenue

Aug 29, 2026 4 min

Most organisations that decide to monetise their data externally know what data they have, but stumble badly on how to price and package it. This article breaks down the mechanics of data product pricing: what actually drives willingness to pay, how to structure tiers, and where the common traps are.

Data observability: catching bad data before it reaches decisions

Aug 28, 2026 4 min

Bad data doesn't announce itself. This playbook shows CDOs how to build detection mechanisms that intercept data quality failures before they corrupt reports, models, and the decisions that follow.

Hub-and-spoke data teams: the model that sounds right and works badly

Aug 27, 2026 4 min

Hub-and-spoke has become the default answer when CDOs are asked how to balance central governance with business-unit agility. The reality in most organisations is slower decisions, diluted accountability, and data professionals caught between two bosses with conflicting priorities.

The data flywheel field guide: who built compounding advantage and what they actually did

Aug 25, 2026 4 min

The data flywheel is one of the most cited concepts in data strategy, and one of the least examined in practice. This field guide cuts through the abstraction and names the companies and moments that show what compounding data advantage actually looks like when it works.

Privacy-enhancing technologies in practice: a CDO playbook

Aug 24, 2026 4 min

Privacy-enhancing technologies have moved from research papers to production deployments, and CDOs who treat them as theoretical still carry unnecessary legal and competitive risk. This playbook walks through how to select, sequence, and embed PETs into your data architecture without stalling your analytics programme.

How Walmart proved data ROI to its board: lessons from a $1 billion bet on supply chain intelligence

Aug 23, 2026 4 min

Walmart's decision to invest heavily in data infrastructure and analytics for its supply chain gave its board a concrete, measurable case for data spending. The mechanics of how that case was built, and what CDOs at other organisations can borrow from it, are more instructive than the headline numbers.

Natural-language BI and the analyst's new role: why the "democratisation" story is only half true

Aug 22, 2026 4 min

Natural-language query tools promise to put business intelligence in everyone's hands, removing the analyst bottleneck. The reality is more complicated, and CDOs who act on the simple version of this story will make costly structural mistakes.

How Walmart's CDO built credibility and structure in the first 90 days

Aug 21, 2026 4 min

When Walmart reorganized its data function in the early 2020s, the incoming data leadership faced a familiar problem: scattered ownership, competing priorities, and a business that wasn't sure what to expect from a CDO. The choices made in those first three months set the terms for everything that followed.

Clean rooms in practice: a CDO's playbook for data collaboration without the risk

Aug 20, 2026 4 min

Data clean rooms promise the ability to share audience insights across company boundaries without exposing raw data. Here is a concrete sequence for CDOs who want to move from pilot anxiety to production-grade collaboration.

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Podcast details

Created by: MBA Training
Status: Active
Latest episode: Nov 10, 2026
Episodes: 96
Release period: Several times a week
Avg. episode length: 5 min
Explicit: No
Language: English

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