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The Data Business Podcast with Fexingo: Analytics, Data Infrastructure, and Information Products
The Data Business Podcast with Fexingo: Analytics, Data Infrastructure, and Information Products
The Data Business Podcast with Fexingo explores how organizations turn raw data into durable products and infrastructure, covering analytics stacks, data pipelines, and information platforms that generate recurring revenue. Hosts Lucas and Luna dissect real cases, such as Snowflake's cloud-data dominance, dbt's standardization of transformation, and the competition between Fivetran and Airbyte in the extraction market. Each episode examines a concrete tension—open-source versus proprietary, speed versus accuracy, or self-service versus centralization—and grounds it in the metrics and decisions that matter. Designed for data engineers, analytics leaders, and product managers, the show avoids hype and highlights principles that survive tool churn.
Episodes
How Data Teams Monetize Privacy Without Selling Out
In October 2026, the regulatory landscape for data monetization has shifted from broad bans to precise governance. We examine how modern data teams are using differential privacy and synthetic data generation to create profitable information products without compromising individual user trust. Lucas breaks down the specific architecture choices that allow companies like Alphabet and Amazon to shar
How Data Teams Turn Metadata Into Profit
Most data teams treat metadata as overhead, but a new wave of operators is building internal marketplaces where metadata becomes the product. We look at how leading analytics organizations are pricing access to data catalogs, enforcing quality through contracts, and monetizing governance itself rather than just raw datasets. This episode explores the shift from cost-center metadata management to r
The Hidden Cost of Data Lineage
In this episode of The Data Business Podcast, Lucas and Luna examine how modern data teams are moving beyond simple ETL pipelines to build comprehensive data lineage. They explore why knowing exactly where data comes from and how it transforms is critical for trust, compliance, and efficiency. Using a specific case study of a mid-sized fintech company that reduced debugging time by forty percent t
How Data Teams Build Self-Healing Pipelines
Data pipelines break, but the teams building them are changing how they respond. We look at a specific case where a major fintech firm replaced manual alerting with automated remediation scripts that caught and fixed schema drift before it hit production. The shift from reactive firefighting to proactive self-healing is reshaping data infrastructure budgets in October 2026. Lucas and Luna discuss
How Data Teams Build Trust Through Provenance
Most data teams still treat provenance as a compliance checkbox, but it is actually the core product feature that unlocks high-value analytics. In this episode, we look at how modern organizations are shifting from simple lineage tracking to full-spectrum data trust frameworks. We explore why raw accuracy is no longer enough when AI models require explainable decision paths. Lucas and Luna discuss
How Data Teams Measure Trust Not Just Accuracy
In Episode 210, Lucas and Luna dissect the hidden crisis of data trust in enterprise systems. While most organizations obsess over model accuracy or pipeline uptime, they ignore the deeper issue: do decision-makers actually believe the numbers? We explore a specific case where a major retailer’s analytics team faced a paradox—perfect metrics but zero adoption—and how shifting focus from technical
Why Data Teams Are Killing the ETL Pipeline
We explore why modern data teams are abandoning traditional Extract Transform Load pipelines in favor of data contracts and event-driven architectures. Using a specific case study of a mid-sized logistics firm that cut their integration costs by forty percent, we examine how shifting from synchronous batch processing to asynchronous message queues improves resilience and reduces latency. This epis
Why Data Teams Are Killing the ETL Pipeline
We look at how modern data architectures are replacing rigid extract-transform-load pipelines with event-driven streaming. Using a concrete example of a logistics firm processing real-time inventory shifts, we explain why batch processing is becoming a liability for speed-to-insight. We break down the specific technical debt incurred by legacy systems and how moving to continuous data integration
Why Data Contracts Are Replacing ETL Pipelines
The old model of data engineering is breaking under the weight of real-time AI demands. Lucas and Luna explore how data contracts shift responsibility from the pipeline to the producer, using a specific case study of a major logistics firm that cut reconciliation errors by sixty percent. They discuss the mechanics of schema enforcement, the role of the FOMC in setting interest rates that make chea
Why Data Teams Are Ditching Real-Time Processing
In October 2026, the obsession with sub-second data latency is hitting a wall. Lucas and Luna dissect why leading enterprises are pivoting from real-time streaming to batch processing for most use cases. We look at the specific energy costs, the architectural simplicity of overnight jobs, and the surprising ROI gains when teams stop chasing millisecond precision that customers rarely notice. This
Why Data Teams Are Killing the ETL Pipeline
In this episode of The Data Business Podcast, Lucas and Luna dissect why major enterprise data teams are abandoning traditional Extract, Transform, Load pipelines in favor of event-driven architectures. We explore how companies like Netflix and Spotify have moved from batch processing to real-time streaming, reducing latency from hours to milliseconds. The conversation focuses on the operational o
The Real Cost of Data Lineage in Modern Architecture
We talk about the hidden complexity of tracking data from source to dashboard. With companies like Snowflake and Databricks scaling fast, maintaining accurate lineage is critical. Lucas and Luna explore why simple metadata isn't enough and how teams are using automated tools to prevent errors. We look at a specific case where poor lineage caused a costly reporting error, and discuss practical step
The Hidden Tax of Dirty Data in Enterprise Systems
Most companies treat data quality as a backend maintenance task, but the financial leakage from unclean data is staggering. This episode examines how inconsistent customer records and missing metadata create hidden liabilities that erode margins by up to five percent annually. We look at the specific mechanics of data decay and why modernizing legacy systems requires more than just new software.
Why Data Teams Are Killing the ETL Pipeline
In this episode of The Data Business Podcast, Lucas and Luna explore why modern data teams are moving away from traditional Extract Transform Load pipelines toward real-time streaming architectures. With market pressures mounting as of September thirtyth two thousand twenty-six, companies like Confluent and Fivetran are leading the charge in shifting from batch processing to event-driven systems.
How Data Teams Build Self-Healing Pipelines
We are looking at how leading data organizations are moving away from manual monitoring toward self-healing infrastructure. This episode examines the shift from reactive alerting to autonomous remediation, using a specific case where automated schema adjustments reduced incident response time by ninety percent. We discuss the architecture of these systems, the risks of blind automation, and why hu
Why Data Teams Are Killing the ETL Pipeline
We talk about how modern data teams are abandoning traditional Extract Transform Load pipelines in favor of real-time streaming architectures. Using a specific case from a major retail logistics firm, we break down why batch processing is becoming a liability and how event-driven design changes everything. The episode covers the technical shift, the cost implications, and the operational risks of
How Data Teams Sell Access Not Insights
Episode 199 explores why leading data teams are shifting from selling raw data or static dashboards to licensing API access and usage-based entitlements. We examine the specific mechanics of metered data access, using a mid-market logistics firm as our anchor case study to show how they monetized metadata without exposing proprietary algorithms. This episode breaks down the pricing models, technic
How Data Teams Audit AI Hallucinations
In this episode of The Data Business Podcast, Lucas and Luna tackle the growing crisis of data model drift and the hidden costs of unverified AI outputs. With September 26, 2026 marking a pivotal shift in how enterprises handle synthetic data, we explore why traditional monitoring is no longer enough. We break down a specific case where a major retail analytics firm lost fifteen percent of its rev
Why Data Teams Are Killing the ETL Pipeline
The era of batch ETL is ending. We break down why leading data teams are abandoning rigid extract-transform-load pipelines in favor of event-driven architectures and real-time streaming. Using a specific case from a mid-sized fintech that reduced latency by ninety percent, we explore the operational shift from moving files to moving state. Lucas and Luna discuss the trade-offs in consistency versu
How Data Teams Sell Insights Without Raw Data
Data teams are shifting from selling raw data exports to monetizing derived insights and decision-support products. This episode examines how organizations like Spotify and Netflix have built internal markets for analytics, turning queries into product features that generate revenue without exposing sensitive customer information. We explore the legal, technical, and cultural shifts required to ma
How Data Teams Stop Selling Raw Data
Most data teams treat their warehouse as a dumping ground, selling access to rows and columns rather than solving specific business problems. In this episode, we look at how leading organizations are shifting from raw data licensing to information products that embed insights directly into workflows. We examine the structural shift required to move from cost center to value driver, focusing on the
How Data Teams Audit AI Hallucinations
As generative models reshape data workflows, the risk of confident falsehoods is rising. This episode examines how leading engineering teams are implementing rigorous audit trails and verification layers to catch model drift before it reaches production. We look at specific techniques for measuring confidence intervals and establishing ground truth in synthetic datasets. Discover why trusting your
How Data Teams Turn Queries Into Product Features
Most data teams treat queries as internal utilities, but a growing number are embedding analytical logic directly into customer-facing applications. This episode explores the architectural shift from static dashboards to real-time, query-driven product features. We examine how companies like Stripe and Intercom have integrated data insights into their core user experience, driving engagement throu
How Data Teams Monetize Metadata
In this episode of The Data Business Podcast, Lucas and Luna explore the emerging revenue stream that most data teams ignore: metadata. While everyone is obsessed with selling raw data or synthetic datasets, a growing number of organizations are finding that the context around their data—the lineage, quality scores, and access logs—has independent market value. We look at how companies like Palant
How Data Teams Prove ROI Without Guesswork
Most data teams struggle to justify their budget because they measure activity, not outcomes. In this episode, we look at how a mid-market logistics company shifted from tracking query volume to measuring decision latency reduction. Lucas and Luna break down the specific framework for linking data infrastructure spend directly to operational savings. We discuss the hidden costs of stale data, how
How Data Teams Prevent Model Drift
In this episode of The Data Business Podcast, Lucas and Luna explore how leading data teams are shifting from reactive monitoring to proactive guardrails against model drift. We examine the specific mechanics of statistical process control applied to machine learning pipelines, using a concrete case where a financial services firm reduced prediction errors by twenty-eight percent through continuou
How Data Teams Build Resilience Against Supply Chain Shocks
We explore how leading data organizations are treating data supply chains like physical logistics networks. Using the example of a major retailer facing real-time inventory discrepancies, we break down the concept of data dependency mapping and why knowing where your numbers come from is just as critical as knowing where your products do. This episode focuses on operational resilience in informati
How Data Teams Shift From Cost Centers To Profit Engines
On this episode of The Data Business Podcast, Lucas and Luna dissect how leading data organizations are transforming from internal cost centers into direct profit drivers. We examine the specific case of a mid-sized fintech firm that turned its transaction logs into a licensed benchmark index, generating $15 million in annual revenue while reducing latency by forty percent. Lucas breaks down the s
The Hidden Cost of Data Quality
We explore why data teams are shifting from measuring volume to auditing trust. Using the example of a major retailer’s inventory mismatch, we break down how contract testing and schema validation prevent costly operational failures. Lucas and Luna discuss the specific mechanics of building self-healing pipelines and the ROI of preventing bad data from reaching decision-makers.
#DataQuality #Cont
Why Data Teams Are Auditing AI Hallucinations
Data teams are shifting from passive monitoring to active auditing of generative AI outputs, specifically focusing on hallucination detection in embedded insights. Lucas and Luna explore how companies like Microsoft and Adobe are implementing ground-truth verification layers before these insights reach end-users. We break down the specific mechanics of prompt injection attacks against data pipelin
How Data Teams Monetize Insights Without Selling Raw Data
Most data teams are stuck in the 'cost center' trap, justifying their budget by counting queries or uptime. But what if they could monetize insights without selling raw data? In this episode, we look at how leading companies are embedding analytics directly into customer workflows, turning passive reports into active product features. We explore the shift from dashboard fatigue to value-driven ins
How Data Teams Turn Queries Into Product Features
Most data teams measure success by latency or uptime. But a growing cohort is treating internal analytics not as a cost center, but as an embedded product feature that drives user retention and upsell conversions. In this episode of The Data Business Podcast, Lucas and Luna examine how companies like Netflix and Spotify have long used recommendation engines to keep subscribers engaged, and how ent
How Data Teams Audit AI Hallucinations
Data teams are facing a new crisis: not broken pipelines, but trustworthy outputs. In this episode, we examine how leading organizations are moving beyond simple uptime metrics to audit the accuracy of agentic AI systems that query internal data. We look at specific frameworks for detecting hallucinations in financial reporting and customer analytics, focusing on the shift from monitoring latency
Why Data Teams Are Stopping Dashboard Builds
Data teams are hitting a hard ceiling with traditional dashboards. In this episode, we look at how organizations like Netflix and Spotify shifted from static reporting to proactive insight delivery. Lucas and Luna discuss the operational costs of maintaining thousands of unused charts, the hidden tax on data engineers, and the specific metric—insight consumption rate—that new leaders are using to
How Data Teams Audit AI Hallucinations
This episode examines the emerging practice of automated hallucination auditing for enterprise LLM applications. We look at how data teams are moving beyond simple accuracy checks to implement rigorous evaluation pipelines that measure semantic drift and factual grounding in real-time. Using a specific case study of a major fintech provider, we explore the metrics that matter when deploying genera
Why Data Teams Stop Building Dashboards
Most data teams are still measuring success by the number of dashboards they build, but that metric is broken. We look at how leading organizations are shifting from output-based metrics to outcome-based accountability. Using a specific case study of a mid-sized fintech firm that reduced dashboard count by forty percent while increasing decision speed, we explore the mechanics of this shift. Lucas
How Data Teams Build Self-Healing Pipelines
Most data teams still treat pipeline failures as emergencies to be managed, not symptoms of a design flaw. This episode examines how leading organizations are shifting from reactive alerting to autonomous remediation using self-healing data architectures. We look at the specific mechanics of schema drift detection, automated fallback logic, and the cultural shift required to trust systems that fix
How Data Teams Build Resilience Against Supply Chain Shocks
When global logistics fractures, data teams stop treating supply chain visibility as a backend reporting task and start building real-time decision engines. This episode examines how major retailers and manufacturers are shifting from static inventory dashboards to dynamic, event-driven architectures that can reroute shipments in minutes rather than days. We look at the specific architectural chan
How Data Teams Are Measuring the ROI of Synthetic Data
Synthetic data has moved from experimental curiosity to a core operational asset, but many data teams are still struggling to justify the spend. In this episode, Lucas and Luna explore how leading organizations are moving beyond simple cost-avoidance metrics to measure the actual return on investment generated by synthetic datasets. We look at a specific case study involving a major healthcare ana
How Data Teams Fix Broken Trust with Contract Testing
Data contracts are supposed to prevent pipeline breakages, but most teams treat them as bureaucratic checkboxes rather than engineering controls. In this episode, we look at how leading data organizations are using automated contract testing to enforce schema stability before code hits production. We examine a specific case where a retailer reduced incident response time by forty percent by shifti
How Data Teams Measure Value Per Query
Data teams are moving beyond cost recovery to measure the actual business value generated by each data query. This episode explores how organizations like Spotify and Netflix use usage-based pricing models internally, charging product teams for compute resources based on the revenue impact of their insights. We discuss the mechanics of value stream mapping in data infrastructure, the challenges of
How Data Teams Monetize Insights Without Selling Raw Data
Most data teams struggle to prove ROI because they sell raw access instead of outcomes. In this episode, we examine how leading organizations are shifting from selling datasets to selling embedded insights and decision intelligence. We look at the specific mechanics of value-based pricing for analytics products, why usage metrics often fail to capture true business impact, and the operational chan
Why Data Teams Are Betting on Synthetic Data
We break down why leading data teams are shifting from collecting more raw data to generating synthetic datasets for training AI models. Using the specific case of a major financial institution testing fraud detection algorithms, we look at how synthetic data solves privacy compliance without sacrificing model accuracy. We explore the technical mechanics of differentially private generation, the c
How Data Teams Are Pricing for Cost Recovery
Every data team knows the pain: stakeholders ask for more dashboards, more pipelines, more models, while the cloud bill keeps climbing and the team gets squeezed. In this episode, Lucas and Luna dig into a concrete trend that is changing how data teams operate in 2026: pricing data products for cost recovery. They walk through a real mid-market SaaS case where the data team moved from a free inter
How Data Teams Are Charging for Embedded Insights
Data teams are moving beyond dashboards and data-as-a-service subscriptions. In this episode, Lucas and Luna explore a fresh trend: charging for embedded analytics inside third-party products. They dig into the shift from cost centers to revenue centers, the pricing models that work—per-seat, per-query, or revenue share—and the real challenges of measuring value when your data is someone else's fe
How Data Teams Are Building Inner-Source Data Platforms
Ep 170 of The Data Business Podcast: Lucas and Luna dig into the quiet but powerful shift where data teams are borrowing open-source collaboration practices and applying them internally — building 'inner-source' data platforms. They explore a concrete case: a 300-person data org that cut onboarding time from six weeks to four days and reduced duplicate pipelines by 40% by treating internal data co
Why Data Teams Are Adopting Data Observability
On episode 169 of The Data Business Podcast, Lucas and Luna explore the rise of data observability — the practice of monitoring data pipelines for quality, freshness, and schema changes before they break downstream reports. They anchor the conversation in a concrete scenario: an e-commerce company that discovers its nightly inventory sync has silently failed for eight hours, costing it missed sale
How Data Teams Are Justifying Agentic AI Spend
Data teams are under pressure to show ROI on agentic AI projects, but traditional metrics like cost per query fall short. In this episode, Lucas and Luna discuss how teams are moving beyond raw efficiency to measure business outcomes, drawing on examples like a logistics company that cut failed deliveries by 40 percent. They explore the shift from cost per API call to value per resolved ticket, th
Why Data Teams Are Moving to Value Stream Mapping
In this episode of The Data Business Podcast, Lucas and Luna explore why data teams are adopting value stream mapping to cut waste and improve delivery. They dig into the specific case of a mid-sized fintech that shaved 30 percent off its analytics cycle time by visualizing every step from raw event to dashboard. The conversation covers how to identify bottlenecks, the difference between lead time
How Data Teams Are Rethinking Data Contracts
Data contracts are becoming the backbone of modern data engineering, but most teams are getting them wrong by treating them as static documents. In this episode, Lucas and Luna explore how forward-thinking data teams are turning contracts into living, enforceable agreements that evolve with their data products. They dig into a real-world example from a European fintech that cut its data incident r
Data Teams Are Measuring Value Per Query
This week on The Data Business Podcast, Lucas and Luna explore a fresh metric that data teams are using to prove their worth: value per query. Instead of just tracking cost per query, forward-looking teams are now attaching dollar figures to the insights their dashboards and pipelines actually generate. The episode centers on a specific example: a mid-sized retail chain that used value-per-query a
Why Data Teams Are Rewriting Their Value Proposition
On this episode of The Data Business Podcast, hosts Lucas and Luna explore a growing tension inside data teams: the difference between delivering data and proving its value. They dive into why traditional metrics like query volume and dashboard count are losing credibility, and how leading teams are shifting to outcome-based reporting. Using the example of a mid-sized retail company that reorganiz
Data Teams Are Shifting to Usage-Based Pricing Models
In this episode of The Data Business Podcast, Lucas and Luna explore how data teams are moving away from flat-rate subscription pricing toward usage-based models that better reflect the value of data products. They anchor the discussion in a real case: a mid-sized SaaS company that rebuilt its data API pricing around a per-query model, cutting churn by 18 percent and boosting revenue per customer
How Data Teams Are Building Data as a Service Offerings
In this episode, Lucas and Luna explore the rise of Data as a Service, or DaaS, as data teams shift from internal support to productized offerings. They dig into a concrete example: a regional bank that partnered with local retailers to build a small-business spending index, turning anonymized transaction data into a revenue stream. The conversation covers the tough decisions around pricing, secur
How Data Teams Are Using Data Marketplaces to Monetize Assets
In this episode of The Data Business Podcast, Lucas and Luna explore the rise of data marketplaces as a new revenue channel for data teams. They discuss the shift from internal data products to external monetization, using the example of a mid-sized logistics company that started selling anonymized route optimization data. The conversation covers the key decisions: how to price data, how to handle
The Hidden Cost of Data Lineage at Scale
Data lineage is a cornerstone of modern data engineering, but as pipelines grow, the cost of storing and querying lineage metadata can quietly explode. In this episode, Lucas and Luna explore how one mid-sized fintech company discovered that their lineage graph had become a bottleneck, driving up compute costs and slowing down impact analysis. They walk through the surprising economics of lineage:
The Rise of Data Clean Rooms for Privacy-Safe Collaboration
On this episode of The Data Business Podcast, Lucas and Luna dive into the world of data clean rooms — secure environments where companies can share and analyze data without exposing raw customer information. They explore how brands like Walmart and Disney are using clean rooms to power ad campaigns and measure performance while respecting privacy regulations. With the death of third-party cookies
How Data Teams Use Open Table Formats to Cut Costs
In this episode of The Data Business Podcast, Lucas and Luna explore the practical impact of open table formats like Apache Iceberg, Delta Lake, and Apache Hudi on data infrastructure costs. They discuss how a mid-sized SaaS company reduced storage and compute costs by 30 percent after migrating to Iceberg, the role of partitioning and compaction in cost optimization, and the trade-offs between th
How Data Teams Are Using Feature Stores for Real-Time Inference
Feature stores are quietly becoming the backbone of real-time machine learning, but most data teams still treat them as just another database. In this episode, Lucas and Luna dig into what a feature store actually is, how it differs from a traditional feature pipeline, and why the ones that succeed are the ones that treat features as versioned, governed products — not just cached values. They walk
Data Teams Are Adopting DataOps to Cut Waste
On this episode of The Data Business Podcast, Lucas and Luna explore how data teams are turning to DataOps—a set of practices borrowed from DevOps—to cut down on wasted effort, reduce errors, and speed up delivery. They dig into the real story of a mid-sized e-commerce company that cut its data pipeline failure rate by 70 percent in six months by adopting DataOps principles: version control for co
How Data Teams Are Using Lakehouse Architectures to Cut Costs
In this episode of The Data Business Podcast, Lucas and Luna explore how modern data teams are leveraging lakehouse architectures to slash storage and compute costs while improving query performance. They examine a real-world case of a streaming service that cut its data infrastructure bill by 30% by migrating from a traditional data warehouse to a lakehouse setup, and discuss the trade-offs invol
How Data Teams Are Adopting Metric Stores
Metric stores are becoming the backbone of consistent business metrics, bridging the gap between raw data and decision-making. In this episode, Lucas and Luna explore why companies like Airbnb and Shopify are turning to metric stores to define metrics once and reuse them across BI tools, feature stores, and reverse ETL pipelines. They break down the core concept—metrics as code—and walk through a
Data Teams Are Turning Data into Products That Earn Revenue
In this episode of The Data Business Podcast, Lucas and Luna explore how forward-thinking data teams are shifting from cost centers to revenue generators by building data products people actually pay for. Using the example of a regional bank that launched a portfolio risk API and a logistics company that turned shipment tracking into a premium subscription, they break down the real economics: pric
The Art of Data Pricing
How do you put a price on a data product? In this episode, Lucas and Luna dig into the messy economics of internal data marketplaces, from cost-plus transfer pricing to value-based models, and how a handful of companies are experimenting with dynamic pricing to balance demand and compute costs. They get concrete: the story of a logistics firm that cut its data warehouse bill by 18 percent by charg
Why Data Teams Are Moving to Cost per Active User
In this episode, Lucas and Luna explore how data teams are shifting their cost optimization strategy from cost per query to cost per active user. They discuss the limitations of granular cost metrics, the importance of aligning data costs with business value, and how a fintech company reduced its data spend per active user by 30 percent by refining its data architecture and query patterns. The hos
Data Teams Are Using Semantic Layers to Boost Query Performance
In this episode of The Data Business Podcast, Lucas and Luna explore how data teams are adopting semantic layers to cut query times and unify metrics across the organization. They dive into a real-world case: a mid-sized fintech that reduced dashboard load times by 40 percent after implementing a semantic layer on top of their lakehouse. The conversation covers the difference between semantic laye
How Data Teams Are Using Cost Per Query to Optimize Spend
In this episode of The Data Business Podcast, Lucas and Luna explore the rise of cost-per-query as a key metric for data teams. They discuss how attributing cloud costs to specific queries helps engineering teams identify waste, optimize expensive pipelines, and align data spend with business value. Using a real-world example of a media company that slashed its data warehouse bill by 30 percent af
How Data Teams Handle End-of-Life for Deprecated Data Products
On this episode of The Data Business Podcast, Lucas and Luna dig into a topic most data teams avoid until it bites them: the end-of-life of a data product. They use a real-world case—a mid-size fintech that had to retire a legacy customer-360 pipeline—to walk through the messy middle: when usage metrics drop, when downstream consumers ignore deprecation notices, and when the cost of running a data
How Data Teams Are Shifting Left with Data Quality Gates
In this episode of The Data Business Podcast, Lucas and Luna dive into the emerging practice of 'shifting left' for data quality — embedding automated checks directly into the data pipeline development process. They explore how a mid-sized fintech reduced data incidents by 45 percent by implementing data quality gates in CI/CD, catching issues before they hit production. The conversation covers th
Why Data Sharing Agreements Are the New Data Contracts
Data contracts have become a cornerstone of trustworthy data products, but what happens when data flows across organizational boundaries? In this episode, Lucas and Luna explore the rise of data sharing agreements (DSAs)—the legal and technical frameworks that govern how companies exchange data with partners, vendors, and customers. They unpack a real-world case: a mid-sized logistics firm that cu
How Data Teams Are Building Small Language Models
In this episode of The Data Business Podcast, Lucas and Luna explore the quiet shift from giant foundation models to small language models purpose-built for internal data work. They use the example of a mid-sized logistics company that replaced a massive API-based model with a 7-billion-parameter local model for warehouse incident reports, cutting inference cost per request by 92 percent while kee
How Data Teams Are Using Data Contracts for Real-Time Streams
In this episode of The Data Business Podcast, Lucas and Luna explore how a regional airline cut its baggage-mishandling reports by 30 percent by applying data contracts to its real-time streaming pipelines. They discuss the shift from schema-on-read to schema-on-write, the role of schema registries in enforcing contracts at the edge, and why this approach reduces downstream breakage without slowin
How Data Teams Are Using Reverse ETL to Activate Insights
In this episode of The Data Business Podcast, Lucas and Luna explore the growing role of reverse ETL in turning analytics into action. They open with a practical example: how a mid-sized e-commerce company used reverse ETL to sync customer segmentation scores from its warehouse into its email platform, cutting campaign preparation time from two days to ten minutes. The conversation covers the core
The Hidden Cost of Data Versioning
Data teams have embraced versioning their data like code, but the practice carries a hidden cost that most teams discover too late. In this episode, Lucas and Luna unpack a 2025 survey that found a third of data teams now version their data — and why 40 percent of them report pipeline failures after a version rollback. They dig into the operational burden of versioning: the backup bloat, the seman
How a European Bank Cut Query Latency by 60 Percent with Lakehouse Architecture
Discover how one of Europe's largest retail banks slashed query latency by 60% and storage costs by 40% by adopting a lakehouse architecture built on Delta Lake and object storage. Lucas and Luna break down what makes the lakehouse pattern different from traditional data warehouses and lakes, including ACID transactions, schema enforcement, and open table formats. They discuss the migration challe
How Data Teams Use Lineage for Automated Impact Analysis
When a fintech team changed a source column type last month, their data lineage tool automatically identified every downstream dashboard and model that would break — before the change went live. In this episode, Lucas and Luna unpack how automated impact analysis transforms data lineage from a post-mortem artifact into a proactive guardrail. They walk through a real-world example: a mid-sized fint
How Data Teams Use Machine Learning for Data Quality Monitoring
A large European bank recently caught a data quality issue that would have misstated its risk-weighted assets by 8%. They didn't catch it with a rule-based check—they caught it with a machine learning model trained on historical data distributions. In this episode, Lucas and Luna explore how data teams are building ML models to monitor data quality across thousands of tables, detecting anomalies i
Data Product Adoption Metrics That Matter
In this episode, Lucas and Luna explore how forward-thinking data teams apply product management principles to measure adoption, retention, and engagement of their internal data products. They walk through a case study of a large retailer that reduced time-to-insight by 40% by tracking metrics like weekly active users, query volume, and user satisfaction scores. They also discuss the role of data
How Data Marketplaces Cut Data Discovery Time by 40 Percent
A major insurance company revealed that its analysts were spending nearly a third of their time simply finding and accessing datasets. In response, they built an internal data marketplace — a catalog-driven platform with data product pages, quality scores, and one-click access. The result: average time from request to delivery dropped from two weeks to two days, a 40 percent improvement in discove
How Data Teams Are Using Feature Stores for ML
In episode 136, Lucas and Luna explore how data teams are adopting feature stores to solve the longstanding problem of feature engineering for machine learning. They break down the core idea: treating features as reusable, versioned, and governed assets — not one-off scripts. The discussion covers the open-source project Feast and the commercial platform Tecton, with a concrete example of how a fi
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