The Data Business Podcast with Fexingo: Analytics, Data Infrastructure, and Information Products

Lucas and Luna dive into the growing use of synthetic data in enterprise AI. Lucas explains how companies like JPMorgan Chase and Microsoft are generating artificial datasets to train models when real data is scarce, privacy-sensitive, or biased. He breaks down the three main techniques: generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models. Luna pushes back on the risk of synthetic data amplifying existing biases, and they discuss the concept of 'data bankruptcy' and why some synthetic data strategies fail. The episode grounds these ideas in a concrete case: a mid-sized insurance firm that used synthetic data to cut model training time by 40% while maintaining accuracy. They close on the open question of whether synthetic data will ever fully replace real-world data for mission-critical models. #SyntheticData #DataScience #MachineLearning #AI #EnterpriseData #DataStrategy #GANs #VAEs #DiffusionModels #DataPrivacy #ModelTraining #JPMorganChase #Microsoft #DataQuality #BusinessAndTechnology #FexingoBusiness #BusinessPodcast #DataInfrastructure Keep every episode free: buymeacoffee.com/fexingo

Episode 59 of The Data Business Podcast. Lucas and Luna examine how enterprise data teams are adopting feature stores to solve machine learning governance and reproducibility challenges. They break down the real-world case of a financial services firm that cut model validation time by 40 percent after implementing a central feature registry. The hosts discuss the tension between data science flexibility and auditability, how feature stores overlap with data catalogs, and why some teams hit adoption roadblocks when engineers resist structured feature definitions. They also explore the emerging pattern of feature stores as a bridge between data engineering and ML operations. If you run a data team or build ML products, this episode gives you a concrete framework for deciding whether a feature store solves your governance problem or just adds another tool to the stack. #FeatureStore #MLGovernance #DataEngineering #MachineLearning #DataScience #ModelValidation #DataGovernance #FeatureRegistry #Reproducibility #FinancialServices #Business #Technology #DataInfrastructure #MlOps #DataArchitecture #FexingoBusiness #BusinessPodcast #DataBusiness Keep every episode free: buymeacoffee.com/fexingo

Episode 58 of The Data Business Podcast. Lucas and Luna dive into one of the trickiest problems in enterprise data: how to assign a price to internal data products. They explore the tension between cost-plus models and value-based pricing, using a real example from a European bank that tried both. Lucas breaks down the three main approaches — cost allocation, market proxy, and willingness-to-pay — and shares why the bank eventually landed on a hybrid model tied to compute consumption and business outcome metrics. Luna pushes back on whether internal pricing creates perverse incentives, like data teams hoarding high-value datasets. They also touch on the role of data product SLAs and chargeback systems. If you are building a data platform or running a data product team, this episode gives you a concrete framework to start pricing conversations with your stakeholders. #DataProducts #DataPricing #DataMonetization #InternalDataMarketplace #DataEconomy #DataPlatform #Chargeback #CostAllocation #ValueBasedPricing #DataProductManager #DataGovernance #DataStrategy #EnterpriseData #BusinessTechnology #Business #FexingoBusiness #BusinessPodcast #TheDataBusinessPodcast Keep every episode free: buymeacoffee.com/fexingo

Data teams have long struggled to justify their budgets. But a new approach—data valuation—is giving CFOs a framework to assign dollar figures to internal data assets. Lucas and Luna walk through the methodology pioneered by a mid-sized retailer called Beacon Supply, which used a discounted cash flow model to value its customer transaction dataset at $47 million. The episode covers the three main valuation methods (cost-based, market-based, income-based), why the income approach wins for most use cases, and the pushback from data engineers who argue that pricing data misses the point. We also hear how Beacon Supply used its valuation to secure a $2 million analytics infrastructure investment that previously had been denied three times. A concrete look at how the language of finance is reshaping data strategy in 2026. #DataValuation #DataStrategy #EnterpriseData #DataEconomics #DataInfrastructure #CFO #DataAssets #BeaconSupply #DataMonetization #DataTeams #BusinessAndTechnology #FexingoBusiness #BusinessPodcast #Analytics #DataProducts #DataGovernance #DataCulture #DataROI Keep every episode free: buymeacoffee.com/fexingo

Episode 56 of The Data Business Podcast. Lucas and Luna explore the emerging role of the data product manager — the person who bridges data engineering and business outcomes. They look at how companies like Spotify and Intuit have formalized this role, the difference between a data PM and a traditional PM, and why treating datasets like products with roadmaps, user research, and SLAs is becoming essential for enterprise data teams. Specific examples include Spotify's internal 'data product' teams and Intuit's QuickBooks data monetization efforts. The hosts also discuss the skills gap, the tension between governance and speed, and how to measure success with metrics like data adoption rate and time-to-insight. #DataProductManager #DataProducts #DataStrategy #DataEngineering #Analytics #ProductManagement #Spotify #Intuit #DataMonetization #DataGovernance #DataTeams #EnterpriseData #DataCulture #DataRoadmap #DataSLA #BusinessTechnology #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo

Episode 55 of The Data Business Podcast. Lucas and Luna explore how data contracts are evolving beyond simple provider-consumer pairs into multi-party agreements that span entire data ecosystems. They examine the case of a major European retailer that implemented a three-party contract framework between its data engineering team, a third-party analytics vendor, and a regulatory compliance unit. The discussion covers the technical challenges of multi-party data contracts, how they differ from API-based agreements, and why they are becoming essential for regulated industries like finance and healthcare. Lucas brings data on how multi-party contracts reduce integration time by 40% compared to bilateral agreements, while Luna questions whether the added complexity is worth it for smaller organizations. The episode closes with a look at how the open-source community is building tooling to support these evolving standards. #DataContracts #MultiPartyAgreements #DataGovernance #DataEngineering #DataInfrastructure #BusinessAndTechnology #DataObservability #DataMesh #DataProducts #RegulatoryCompliance #OpenSource #DataIntegration #EnterpriseData #DataManagement #DataCatalog #FexingoBusiness #BusinessPodcast #TheDataBusinessPodcast Keep every episode free: buymeacoffee.com/fexingo

Lucas and Luna explore how data clean rooms are transforming how companies share and analyze customer data without compromising privacy. Lucas breaks down the technical architecture — secure multiparty computation and differential privacy — and walks through a real-world case: a major retailer and an ad platform jointly analyzing purchase patterns without ever exposing raw customer IDs. Luna challenges whether this is just a compliance checkbox or a genuine strategic tool. They discuss why clean rooms gained traction after iOS 14's IDFA changes, how Snowflake and AWS are commoditizing the infrastructure, and what it means for data teams building internal products. No hype, just the mechanics and trade-offs. #DataCleanRooms #PrivacyTech #SecureMultipartyComputation #DifferentialPrivacy #Snowflake #AWS #IDFA #AdTech #RetailAnalytics #DataSharing #Compliance #DataInfrastructure #DataProducts #FexingoBusiness #BusinessPodcast #BusinessAndTechnology #DataEngineering #PrivacyEngineering Keep every episode free: buymeacoffee.com/fexingo

In this episode of The Data Business Podcast, Lucas and Luna dive into the growing trend of reverse ETL—syncing data from data warehouses back into operational systems for real-time personalization. They discuss a case study of a mid-sized e-commerce company that used reverse ETL to power personalized product recommendations, resulting in a 15% lift in conversion rates. The hosts unpack the technical architecture, common pitfalls, and why this approach is becoming a key part of the modern data stack. They also touch on how reverse ETL complements traditional ETL pipelines and the role of data teams in enabling business agility. #ReverseETL #DataEngineering #RealTimePersonalization #ModernDataStack #DataWarehouse #EcommerceData #DataPipelines #CustomerExperience #BusinessTechnology #DataInfrastructure #FexingoBusiness #BusinessPodcast #DataStrategy #MarketingAnalytics #ProductRecommendations #DataOps #EventDrivenArchitecture #CDP Keep every episode free: buymeacoffee.com/fexingo

Episode 52 of The Data Business Podcast. Lucas and Luna explore how enterprise data catalogs are evolving from simple metadata registries into full-featured search and discovery platforms — essentially becoming the 'Google for your company's data.' They discuss a real-world case: how a Fortune 500 retailer with 15,000 data assets cut discovery time from hours to seconds by implementing a catalog with natural language query and automated classification. Along the way, they cover the rise of active metadata, the role of knowledge graphs in connecting datasets, and why data catalogs are now a prerequisite for any serious AI initiative. Listeners will learn one concrete number: a typical analyst wastes 30–40% of their time just finding the right data — and modern catalogs can cut that to near zero. #DataCatalog #EnterpriseSearch #ActiveMetadata #DataDiscovery #KnowledgeGraph #DataGovernance #Fortune500 #RetailData #Analytics #DataInfrastructure #ArtificialIntelligence #DataLake #BusinessIntelligence #DataManagement #DataEngineering #Business #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo

In this episode of The Data Business Podcast, Lucas and Luna dive into reverse ETL—the practice of syncing transformed data from a data warehouse back into operational tools like CRMs, email platforms, and ad servers. They explore why forward-thinking data teams are using reverse ETL to power real-time personalization at scale, citing the example of a mid-market e-commerce company that reduced cart abandonment by 18% within two months of implementation. Lucas breaks down the technical architecture—how tools like Census and Hightouch fit into modern data stacks—while Luna questions the operational challenges, including data quality and latency trade-offs. They also discuss the shift from batch to streaming reverse ETL and how it's enabling customer 360 views that update in minutes, not days. The episode closes with a reflection on whether reverse ETL is a stopgap or a permanent layer in enterprise data infrastructure. #ReverseETL #DataEngineering #RealTimeData #Customer360 #DataStack #Census #Hightouch #DataPipeline #Personalization #CustomerDataPlatform #DataWarehouse #OperationalAnalytics #DataProduct #BusinessAndTechnology #FexingoBusiness #BusinessPodcast #DataInfrastructure #DataTeams Keep every episode free: buymeacoffee.com/fexingo

Episode 50 of The Data Business Podcast. Lucas and Luna dive into federated learning — a technique where machine learning models train across decentralized data without raw data ever leaving its source. They explore a concrete case: how a major hospital network used federated learning to collaborate with pharmaceutical partners on drug discovery while keeping patient records legally compliant. The episode breaks down the technical architecture, the governance implications, and why this model is gaining traction in regulated industries like healthcare and finance. Lucas explains how federated learning flips the traditional data-centralization paradigm, and Luna pushes on the practical challenges: coordination overhead, statistical drift, and the thorny question of who owns the trained model. If you're building data products or running a data team in a regulated environment, this episode gives you the vocabulary and the framework. The Data Business Podcast is part of the Fexingo Business network. #FederatedLearning #DataStrategy #EnterpriseData #MachineLearning #DataPrivacy #HealthcareAI #DrugDiscovery #RegulatoryCompliance #DataGovernance #ModelTraining #DecentralizedData #DataPartnerships #FLArchitecture #RiskManagement #BusinessAndTechnology #DataInfrastructure #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo

In this episode, Lucas and Luna explore the emerging trend of data teams building internal AI copilots—custom conversational interfaces trained on proprietary enterprise data. They examine the case of a mid-size logistics company that built a 'Supply Chain Brain' using its own shipment, weather, and inventory data, cutting query time from hours to seconds. The hosts discuss the key architectural choices: vector embeddings, RAG (retrieval-augmented generation), and the importance of data quality and governance. They also touch on the pitfalls, including hallucination risks and the need for human-in-the-loop validation. The conversation ends with a forward-looking question: will every data team eventually need to become a product team shipping conversational interfaces? #AI #DataEngineering #DataProducts #DataTeam #Copilot #RAG #VectorDatabases #LLM #EnterpriseData #Business #Technology #FexingoBusiness #BusinessPodcast #DataInfrastructure #InternalTools #Governance #Logistics #SupplyChain Keep every episode free: buymeacoffee.com/fexingo

Lucas and Luna explore how data products — self-contained, reusable data assets with defined quality SLAs — are displacing traditional enterprise software. They examine a real case: how a mid-market retailer replaced its customer analytics suite with an internal data product built on Snowflake and dbt, cutting costs by 40 percent while giving business teams self-serve access. They also discuss why data products require new governance models, the tension between centralization and federation, and what this means for enterprise software vendors. Along the way, they address why listener support matters for keeping the show ad-free. #DataProducts #EnterpriseSoftware #DataMesh #Snowflake #dbt #DataGovernance #SelfServeAnalytics #DataInfrastructure #DataContracts #DataObservability #BusinessAndTechnology #FexingoBusiness #TheDataBusinessPodcast #BusinessPodcast #DataStrategy #DataEngineering #Analytics #DataProductMarketplaces Keep every episode free: buymeacoffee.com/fexingo

Episode 47 of The Data Business Podcast dives into the hidden failure of data contracts at a Fortune 500 retailer. Lucas and Luna examine why this well-hyped pattern for governing data exchange between teams fell apart in practice — and what actually fixed the pipeline. The episode centers on a specific case: a major retailer with over 200 data producers and 1,500 consumers. After a year-long rollout of data contracts, they saw no reduction in pipeline failures. The hosts unpack three root causes — brittle schema definitions, lack of runtime enforcement, and incentive misalignment — and then explore the surprising solution that emerged: a lightweight observability feedback loop that treated contracts as living documents, not static agreements. Listeners learn one concrete principle: data contracts only work when they're coupled with runtime validation and team-level ownership. No fluff, just the operational lesson. #DataContracts #DataEngineering #DataObservability #PipelineFailures #EnterpriseData #DataGovernance #DataMesh #SchemaEvolution #Fortune500 #RetailData #DataQuality #FeedbackLoop #RuntimeValidation #TeamIncentives #BusinessAndTechnology #FexingoBusiness #BusinessPodcast #TheDataBusinessPodcast Keep every episode free: buymeacoffee.com/fexingo

Data contracts are becoming a practical tool for reducing integration time between teams, and Lucas and Luna dig into the specifics. They look at how companies like Uber and a mid-sized logistics firm cut months off data-sharing projects by defining schemas, SLAs, and expectations upfront. Lucas explains the three components of a data contract—schema guarantees, semantic rules, and service-level objectives—and how they prevent the classic 'works on my machine' problem. Luna asks whether contracts add bureaucracy, and Lucas points to a case where a retailer shaved 40 percent off its data pipeline deployment time. They also touch on the tension between flexibility and standardization, and how contracts evolve in real time. No hype, just a clear look at what data contracts actually do in production. #DataContracts #EnterpriseData #DataIntegration #SchemaGuarantees #ServiceLevelObjectives #DataEngineering #DataMesh #Uber #Logistics #Retail #DataGovernance #DataQuality #DataObservability #Business #Technology #FexingoBusiness #BusinessPodcast #DataInfrastructure Keep every episode free: buymeacoffee.com/fexingo

In episode 45 of The Data Business Podcast, Lucas and Luna tackle a question that keeps data engineers up at night: why do data pipelines break silently and what's the fix? They drill into the concept of data observability with a feedback loop back into production — moving beyond monitoring dashboards to automated corrective actions. Using a concrete example from a mid-size e-commerce company that reduced data incident resolution time by 70 percent, they explore how modern data teams are integrating observability tools with reverse ETL and feature stores to create a continuous improvement cycle. Lucas explains why most observability platforms are still 'read-only' and how adding a write-back capability changes the game for data quality. The hosts also discuss the tension between data team autonomy and centralized governance in implementing feedback loops. A natural donation segment ties the episode's theme of continuous improvement to listener support for the show. Tune in for a practical look at how data observability is evolving from a monitoring tool to an operational feedback system. #DataObservability #FeedbackLoop #DataEngineering #ReverseETL #DataQuality #DataPipelines #DataInfrastructure #BusinessPodcast #FexingoBusiness #DataBusinessPodcast #DataOps #FeatureStore #DataGovernance #Monitoring #Automation #IncidentResponse #DataMesh #DataArchitecture Keep every episode free: buymeacoffee.com/fexingo

Lucas and Luna explore the rise of reverse ETL — the practice of syncing data from a warehouse back into operational tools like Salesforce, Zendesk, and HubSpot. They break down how companies like Sanity and a fictional e-commerce brand use reverse ETL to power real-time customer personalization, cut query costs, and reduce engineering bottlenecks. With specific numbers on latency reduction and team productivity gains, they explain why reverse ETL is becoming a standard layer in modern data stacks, and how it differs from traditional ETL and ELT. The episode also covers common pitfalls like data staleness and workflow misalignment, and offers practical advice for teams considering the shift. #ReverseETL #DataEngineering #DataStack #BusinessTechnology #FexingoBusiness #BusinessPodcast #DataInfrastructure #RealTimeData #Customer360 #Salesforce #HubSpot #Zendesk #DataWarehouse #Analytics #DataOps #ModernDataStack #OperationalAnalytics #ETL Keep every episode free: buymeacoffee.com/fexingo

Episode 43 explores how the data mesh operating model is transforming enterprise data teams. Lucas and Luna break down the four core principles of data mesh—domain ownership, data as a product, self-serve data platform, and federated governance—using a detailed example from a global retailer. They discuss how this shift changes team structures, reduces bottlenecks, and enables domain-specific data products. The episode also covers common pitfalls and why data mesh is gaining traction in 2026 as companies move beyond centralized data lakes and warehouses. Specific numbers: the retailer cut time-to-insight from six weeks to under three days. A practical, concrete look at a paradigm that's moving from hype to real-world adoption. #DataMesh #DataGovernance #DataProducts #DomainOwnership #SelfServeData #FederatedGovernance #EnterpriseData #DataArchitecture #DataPlatform #DataTeams #BusinessAndTechnology #DataStrategy #DataLakehouse #DataObservability #DataContracts #FexingoBusiness #BusinessPodcast #DataManagement Keep every episode free: buymeacoffee.com/fexingo

Episode 42 of The Data Business Podcast dives into data observability—the practice of monitoring data pipelines for quality, freshness, and lineage in real time. Lucas and Luna explore a specific case: how a fintech company reduced data incident resolution time from four hours to eleven minutes using open-source tools like Great Expectations and custom dashboards on Databricks. They discuss the difference between monitoring (checking if a system is up) and observability (understanding why data broke), the role of telemetry and automated root-cause analysis, and why the market for observability platforms is growing at over 30 percent annually. Luna challenges the cost argument for small teams, and Lucas explains how observability shifts the data team's culture from reactive firefighting to proactive engineering. The hosts also touch on the cultural resistance data teams face when adopting observability and why it matters for regulatory compliance in 2026. A donation segment early in the episode ties the topic to listener support for ad-free content. #DataObservability #DataEngineering #DataPipelines #GreatExpectations #Databricks #Fintech #DataQuality #DataLineage #Monitoring #RootCauseAnalysis #Telemetry #DataCulture #DataInfrastructure #BusinessAndTechnology #EnterpriseData #DataIncident #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo

Episode 41 of The Data Business Podcast. Lucas and Luna explore why internal data product adoption is the new frontier for enterprise data teams. They examine a case study from a global retailer that built a cross-functional data marketplace for its own business units, achieving 40% faster time-to-insight. The hosts discuss the key design principles: treating internal users like external customers, embedding self-service analytics, and measuring adoption through product KPIs rather than just technical uptime. They also touch on the cultural shift required to move from data-as-a-service to data products. A practical look at what works and what doesn't when your data team becomes an internal product shop. #DataProducts #InternalAdoption #EnterpriseData #SelfServiceAnalytics #DataMarketplace #DataInfrastructure #BusinessTechnology #DataStrategy #DataTeam #ProductMindset #DataDriven #RetailCaseStudy #LucasAndLuna #DataBusiness #DataMeasurement #Analytics #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo

Episode 40 of The Data Business Podcast explores the shift from centralized data platforms to the data mesh operating model. Lucas and Luna unpack Zhamak Dehghani's original 2019 thesis, contrast it with earlier lakehouse and data warehouse approaches, and examine how organizations like Intuit and PayPal have adopted domain-oriented data ownership. They discuss the four core principles of data mesh—domain ownership, data as a product, self-serve data infrastructure, and federated computational governance—and drill into a real-world case: how a large European retailer cut time-to-insight from weeks to hours after reorganizing around data domains. The hosts also address common pitfalls, including the trap of treating data mesh as a technology purchase rather than an organizational change. No hype, just practical takeaways for data leaders evaluating this model in mid-2026. #DataMesh #DataPlatform #DataArchitecture #DomainDrivenDesign #DataProduct #DataGovernance #BusinessAndTechnology #FexingoBusiness #BusinessPodcast #DataEngineering #DataStrategy #ZhamakDehghani #Intuit #PayPal #SelfServeData #Analytics #DataInfrastructure #DataOperatingModel Keep every episode free: buymeacoffee.com/fexingo

Data contracts—agreed-upon schemas and SLAs between data producers and consumers—are quietly reshaping how large companies manage data pipelines. This episode unpacks why enterprises are moving from informal data handoffs to legally-binding data contracts, using examples from a major European retailer that cut integration time by 40% after adopting them. We explain how data contracts reduce miscommunication, enforce data quality at the source, and create a shared language for data teams and business stakeholders. Lucas and Luna also explore the trade-offs: the upfront investment in defining contracts, the risk of over-engineering, and whether this approach scales beyond large tech-forward firms. For anyone building or managing data infrastructure, this is a practical look at a trend that's moving from niche to mainstream. #DataContracts #DataGovernance #DataEngineering #EnterpriseData #DataIntegration #DataQuality #SchemaManagement #DataObservability #DataProducts #DataLineage #DataMesh #DataCatalog #Business #Technology #FexingoBusiness #BusinessPodcast #DataInfrastructure #DataOps Keep every episode free: buymeacoffee.com/fexingo

Episode 38 of The Data Business Podcast: Lucas and Luna dig into why a growing number of enterprise data teams are replacing traditional REST APIs with data contracts — formal agreements between data producers and consumers that specify schema, semantics, SLAs, and expected usage. They unpack a real case from a mid-sized fintech that cut downstream incidents by 40 percent after switching to contract-driven data sharing. Lucas explains how data contracts differ from both APIs and data catalogs, why they reduce integration costs for analytics and AI pipelines, and where the approach still falls short. Luna questions whether the model scales across thousands of datasets and brings in a counterexample from a consumer-goods company that tried and reverted. The episode includes a listener-support mention for keeping the show ad-free. Tune in for a focused comparison between two approaches to data exchange. #DataContracts #API #DataIntegration #DataGovernance #DataProduct #Fintech #SchemaEvolution #SLA #DataProducers #DataConsumers #Business #Technology #DataEngineering #DataPipelines #Analytics #AI #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo

Feature stores have quietly become one of the most important pieces of data infrastructure for machine learning teams. Lucas and Luna explore how companies like Uber, Airbnb, and DoorDash use feature stores to avoid duplicating work, reduce time-to-deployment, and operationalize ML at scale. They dive into the specific problem of 'training-serving skew', the rise of open-source tools like Feast, and why feature stores are becoming a standard part of the enterprise data stack. Along the way, they discuss how feature stores connect to data version control and data contracts, and what this means for data teams building production ML systems in mid-2026. If you're a data engineer, ML engineer, or technical manager wondering whether a feature store is worth the investment, this episode gives you the concrete use cases and trade-offs to make that call. #FeatureStore #MachineLearning #DataEngineering #MLInfrastructure #Uber #Airbnb #DoorDash #Feast #TrainingServingSkew #FeatureEngineering #DataScience #MLOps #DataVersionControl #DataContracts #EnterpriseData #Business #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo

In this episode, Lucas and Luna explore how metadata lakehouses are emerging as a new architectural layer that unifies data discovery across fragmented enterprise data landscapes. They discuss how companies like Atlassian and Uber have used this approach to reduce data discovery time by 40 percent, and contrast it with earlier metadata management strategies like data catalogs and data mesh. The conversation delves into the technical underpinnings, including open table formats like Apache Iceberg and the role of machine learning in auto-tagging, and considers whether metadata lakehouses represent a genuine paradigm shift or just another tool in the stack. #MetadataLakehouse #DataDiscovery #DataSilos #ApacheIceberg #Atlassian #Uber #DataCatalog #DataMesh #DataGovernance #OpenTableFormats #MachineLearning #AutoTagging #EnterpriseData #DataArchitecture #BusinessAndTechnology #FexingoBusiness #BusinessPodcast #DataInfrastructure Keep every episode free: buymeacoffee.com/fexingo

Episode 35 of The Data Business Podcast dives into a quiet revolution in enterprise compliance: data lineage. Lucas and Luna explore how the European Central Bank's new digital reporting framework and the SEC's 2025 cybersecurity disclosure rules are forcing companies to track every data point from ingestion to dashboard. They break down why a major European insurer recently spent $12 million retrofitting lineage into legacy systems, and why startups like Spline and Metaphor are seeing 300% revenue growth. The hosts discuss how lineage tools are moving from mere data governance toys to board-level compliance necessities, with concrete examples of audit failures that cost firms millions. No hype, just the numbers and the strategy behind the shift. #DataLineage #Compliance #EuropeanCentralBank #SEC #DataGovernance #EnterpriseData #RegTech #Spline #Metaphor #BusinessPodcast #TechnologyPodcast #FexingoBusiness #BusinessPodcast #DataManagement #Audit #DataInfrastructure #Analytics #DataObservability Keep every episode free: buymeacoffee.com/fexingo

Episode 34 of The Data Business Podcast explores how data version control systems—inspired by software engineering's Git—are becoming critical for enterprise AI teams. Lucas and Luna discuss the real-world case of a fintech company that used data versioning to recover from a corrupted training dataset, saving six months of rework. They break down the key players, from DVC to LakeFS, and explain why versioning is now a non-negotiable part of the machine learning lifecycle. If you're building AI products, this is the toolchain shift you can't ignore. #DataVersionControl #EnterpriseAI #MachineLearning #DataEngineering #MLOps #DVC #LakeFS #DataLineage #Reproducibility #Fintech #DataInfrastructure #ModelTraining #GitForData #DataLake #BusinessAndTechnology #FexingoBusiness #BusinessPodcast #DataOps Keep every episode free: buymeacoffee.com/fexingo

Episode 33 of The Data Business Podcast explores how enterprise data teams are shifting from custom one-off integrations to buying and selling data products through internal and external marketplaces. Lucas and Luna examine Snowflake's Marketplace, which now hosts over 2,000 third-party data listings and processes millions in monthly transactions, and compare it to AWS Data Exchange. They discuss why procurement teams are treating data like SaaS subscriptions, how data valuation is influencing contract terms, and what this means for data engineering roadmaps in 2026. The hosts also touch on the rise of data product managers and the tension between governance and speed. This episode is grounded in real examples and avoids hype — perfect for data leaders evaluating marketplace strategies. #DataMarketplaces #DataProducts #Snowflake #AWSDataExchange #EnterpriseData #DataProcurement #DataEngineering #BusinessPodcast #FexingoBusiness #DataBusiness #DataValuation #DataGovernance #ProductMarketplace #SaaS #DataStrategy #Analytics #TechProcurement #DataInfrastructure Keep every episode free: buymeacoffee.com/fexingo

Episode 32: Lucas and Luna dive into how data contracts are transforming enterprise software pricing. Through the lens of Snowflake's recent shift to consumption-based pricing and Databricks' new unit-based model, they explore why static subscription tiers are giving way to dynamic pricing tied to data volume and quality. They discuss the role of data contracts in enabling this shift, the challenges of unpredictable costs for customers, and what this means for CFOs and data teams in 2026. #DataContracts #EnterprisePricing #Snowflake #Databricks #ConsumptionPricing #DataVolume #DataQuality #CFO #DataTeams #BusinessTechnology #DataInfrastructure #Podcast #FexingoBusiness #BusinessPodcast #DataBusiness #Analytics #CloudData #PricingModels Keep every episode free: buymeacoffee.com/fexingo

Episode 31 explores a surprising shift in the data world: data catalogs are evolving from passive inventory tools into the active operating layer for enterprise data. Lucas unpacks how Databricks, Snowflake, and startups like Alation and Atlan are racing to build the 'control plane' for data, embedding governance, discovery, and lineage directly into the query path. Luna challenges whether this is just old metadata management rebranded. They dive into a concrete case: how a major European bank uses a catalog to reduce data incident response time from hours to under five minutes. The episode examines the technical and organisational forces pushing this shift, including the rise of data mesh and lakehouse architectures, and asks whether the catalog is finally becoming the single source of truth companies have been chasing for two decades. #DataCatalog #EnterpriseData #MetadataManagement #DataGovernance #DataMesh #Lakehouse #Databricks #Snowflake #Alation #Atlan #DataLineage #DataDiscovery #BusinessTechnology #DataEngineering #DataInfrastructure #DataStrategy #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo

Episode 30 of The Data Business Podcast. Lucas and Luna explore the emerging discipline of data valuation and how it's reshaping mergers and acquisitions. They dive into the 2025 acquisition of a mid-market AI firm where the target's synthetic data library was valued at $47 million — more than its software stack. The hosts discuss the SEC's evolving stance on data as a balance-sheet asset, the accounting firm battle over valuation standards, and why private equity is hiring data economists. If you work in data or M&A, this episode gives you a framework for thinking about what data is actually worth. #DataValuation #MergersAndAcquisitions #DataEconomy #DataAssets #SyntheticData #BusinessValuation #PrivateEquity #SEC #DataAccounting #DataLibraries #FinancialReporting #DataEconomics #EnterpriseData #BusinessAndTechnology #FexingoBusiness #BusinessPodcast #DataBusiness #DataDriven Keep every episode free: buymeacoffee.com/fexingo

In this episode of The Data Business Podcast, Lucas and Luna explore how data lakehouses are quietly resolving the long-standing war between data warehouses and data lakes. Anchored by the recent Databricks acquisition of Arcion for $100 million, they unpack what the lakehouse architecture actually is, why enterprises are adopting it, and how it changes the economics of data infrastructure. They walk through a concrete example: a mid-size e-commerce company migrating from a Snowflake warehouse and an S3 data lake onto a unified lakehouse platform, cutting their total data infrastructure costs by 38 percent while reducing time-to-insight from hours to minutes. The conversation also touches on how lakehouses affect data engineering roles, vendor lock-in concerns, and why this might be the last major architectural shift in enterprise data for a while. If you're building or running a data team, this is the architectural decision you can't ignore. #DataLakehouse #Databricks #Arcion #DataArchitecture #DataWarehouse #DataLake #EnterpriseData #DataInfrastructure #DataEngineering #DeltaLake #Snowflake #ApacheIceberg #OpenTableFormats #DataGovernance #BusinessAndTechnology #FexingoBusiness #BusinessPodcast #DataBusiness Keep every episode free: buymeacoffee.com/fexingo

Lucas and Luna explore the hidden problem of data leakage in machine learning — when information from the future accidentally leaks into training data, inflating model accuracy and causing catastrophic failures in production. They examine a specific case: a major retail bank that launched a fraud detection model showing 98 percent accuracy in testing, only to see it fail in the real world because the data pipeline had inadvertently included transaction timestamps and future labels. The episode breaks down the three most common types of leakage — target leakage, train-test contamination, and feature leakage — and explains how companies like Uber and Airbnb have built systems to detect it. Lucas shares the one metric engineering teams should monitor, and Luna presses on why most organizations don't catch leakage until it's too late. #DataLeakage #MachineLearning #FraudDetection #MLEngineering #DataScience #FeatureEngineering #ModelValidation #Airbnb #Uber #RetailBanking #ProductionML #DataPipelines #BusinessAndTechnology #FexingoBusiness #BusinessPodcast #DataInfrastructure #MLOps #DataQuality Keep every episode free: buymeacoffee.com/fexingo

Podcast hosts Lucas and Luna explore how data clean rooms are transforming the way companies share customer data without compromising privacy. This episode focuses on a specific case: a major retailer and a consumer packaged goods brand using a clean room to run joint audience analysis while keeping raw data siloed. Lucas explains the technical architecture — differential privacy, query constraints, and audit logs — while Luna questions whether this is just a workaround for consent fatigue. They discuss the $5 billion market opportunity projected by 2028, the role of cloud providers like Amazon and Google in offering clean room services, and why companies like Snowflake and LiveRamp are betting big on this infrastructure. The hosts also touch on regulatory tailwinds from GDPR and CCPA that are pushing advertisers toward these privacy-safe environments. If you work in data engineering, advertising, or privacy compliance, this episode offers a concrete look at a technology that is quietly becoming standard practice. #DataCleanRooms #Privacy #Advertising #DifferentialPrivacy #GDPR #CCPA #Snowflake #LiveRamp #Amazon #Google #RetailMedia #CustomerData #DataInfrastructure #Business #Technology #FexingoBusiness #BusinessPodcast #DataEngineering Keep every episode free: buymeacoffee.com/fexingo

Lucas and Luna unpack the rise of data version control (DVC) as a critical infrastructure layer for enterprise AI. They explore how tools like DVC and LakeFS are bringing Git-like versioning to datasets, enabling reproducibility, auditability, and collaboration at scale. The hosts walk through a concrete example: how a pharmaceutical company used data version control to track training data for a drug discovery model across 40 versions, cutting debugging time from weeks to hours. They discuss why traditional data warehouses and data lakes fail to handle the versioning needs of modern machine learning pipelines, and how data version control is becoming as essential as source control for code. Luna challenges Lucas on the overhead of maintaining yet another tool in the stack, and Lucas argues that the cost is outweighed by the gains in model governance and regulatory compliance. The episode also covers how data version control integrates with feature stores and data observability, and why it's becoming a standard requirement in MLOps platforms. A must-listen for data engineers, ML practitioners, and anyone building AI systems that need to be reproducible and auditable. #DataVersionControl #DVC #LakeFS #EnterpriseAI #MLOps #DataEngineering #MachineLearning #Reproducibility #DataGovernance #DataLineage #FeatureStores #ModelObservability #PharmaceuticalAI #DrugDiscovery #DataInfrastructure #BusinessAndTechnology #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo

When a major European bank discovered that its data lineage was manually tracked in spreadsheets, the compliance team spent 14 months untangling a single loan-origination pipeline. This episode examines how graph databases are replacing traditional catalog tools for data governance, using a real case from a financial institution that cut policy-audit time from weeks to hours. Lucas and Luna break down why property graphs outperform relational models for lineage, how Neo4j and Amazon Neptune are competing in this niche, and what the rise of data-contract enforcement means for engineering teams building governance automation. They also discuss the tension between open standards like OpenLineage and vendor lock-in, and why the graph approach is especially relevant for organizations dealing with GDPR and AI-regulation requirements. If you're evaluating data-governance tooling or building policy-as-code infrastructure, this episode offers a concrete framework for when graph technology makes sense and when it doesn't. #DataGovernance #GraphDatabases #DataLineage #PolicyEnforcement #Neo4j #AmazonNeptune #OpenLineage #GDPR #DataContracts #DataEngineering #ComplianceAutomation #BusinessTechnology #Business #DataArchitecture #DataPolicy #FexingoBusiness #BusinessPodcast #DataBusinessPodcast Keep every episode free: buymeacoffee.com/fexingo

Episode 24 of The Data Business Podcast: Lucas and Luna explore the shift from data-as-a-service to data product thinking in enterprise procurement. They break down how Snowflake's Marketplace and Databricks' Delta Sharing are turning raw data into packaged, priced products with SLAs. The conversation focuses on the concept of 'data product managers' and how companies like Fidelity and JPMorgan are restructuring teams around product-based data delivery. Lucas explains the pricing shift from per-GB to per-query and outcome-based models, citing Snowflake's consumption pricing and the rise of data contracts. Luna challenges whether this is just rebranding, and Lucas counters with evidence of real procurement changes. The episode ends with a look at how data product thinking affects vendor negotiation and internal team dynamics. #DataProducts #EnterpriseData #DataProcurement #Snowflake #Databricks #DeltaSharing #DataMarketplace #DataContracts #ConsumptionPricing #DataProductManager #Fidelity #JPMorgan #DataAsAProduct #DataMesh #BusinessAndTechnology #BusinessPodcast #FexingoBusiness #DataEngineering Keep every episode free: buymeacoffee.com/fexingo

Lucas and Luna explore how data contracts are cutting integration costs for enterprise teams. They examine a case study from a major European retailer that reduced data pipeline failure rates by 60 percent within six months by implementing schema-level agreements between producers and consumers. The episode digs into why data contracts matter, how they differ from traditional service-level agreements, and the practical challenges of enforcement at scale. Lucas shares specific numbers on debugging time saved and the reduction in unplanned rework, while Luna questions whether contracts can adapt to rapidly changing business requirements without becoming a bottleneck. The conversation also touches on tooling choices, governance implications, and the surprisingly simple cultural shift that made the biggest difference. No fluff, just concrete lessons for data teams evaluating whether data contracts are worth the investment. #DataContracts #EnterpriseData #DataEngineering #DataGovernance #IntegrationCosts #SchemaManagement #DataQuality #PipelineReliability #BusinessAndTechnology #FexingoBusiness #BusinessPodcast #DataPodcast #CostReduction #DataAgreements #CulturalChange #EuropeanRetailer #DebuggingTime #DataTeams Keep every episode free: buymeacoffee.com/fexingo

Lucas and Luna explore how data contracts are shifting enterprise software pricing from seat-based to usage-based models. They examine Snowflake's consumption pricing, Databricks' DBU model, and how companies like Fivetran and dbt Labs are adopting data contract principles to tie costs to value. The episode unpacks the economic logic behind usage-based pricing and the risks it introduces for both vendors and customers, including bill shock and cost predictability. #DataContracts #UsageBasedPricing #Snowflake #Databricks #Fivetran #dbtLabs #EnterpriseSoftware #DataEngineering #DataInfrastructure #DataPricing #ConsumptionPricing #CloudData #BusinessTechnology #BusinessPodcast #FexingoBusiness #DataBusiness #DataObservability #DataMesh Keep every episode free: buymeacoffee.com/fexingo

Lucas and Luna explore the rise of data marketplaces—platforms where companies buy and sell third-party datasets—and how they're reshaping business intelligence. They focus on Snowflake's Marketplace, launched in 2020, which now hosts over 2,500 datasets from providers like Knoema and SafeGraph. Lucas explains how this model lets companies enrich their analytics without building data pipelines from scratch. Luna raises concerns about data quality and pricing inconsistency. They discuss the shift from custom data-sharing agreements to standardized marketplaces, and what this means for data engineers and analysts. The episode includes specific examples, such as how a mid-sized retailer used foot-traffic data from SafeGraph to optimize store locations, and how Knoema's economic data feeds are used by hedge funds for macroeconomic analysis. They also touch on the governance challenges—how do you audit the lineage of third-party data? If you find this kind of analysis useful, the show is ad-free thanks to listener support at buy me a coffee dot com slash fexingo. #DataMarketplaces #Snowflake #BusinessIntelligence #DataEngineering #SafeGraph #Knoema #ThirdPartyData #DataGovernance #DataSharing #Analytics #DataPipelines #DataQuality #DataPricing #DataLineage #Business #Technology #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo

Data observability is the practice of monitoring data pipelines for quality, freshness, and lineage in real-time. In this episode, Lucas and Luna explore how companies like Uber and Snowflake use observability tools to catch data quality issues before they cascade into expensive outages. The hosts break down the three pillars of observability—freshness, volume, and schema—and discuss why traditional monitoring falls short in modern data stacks. They also examine the case of a major bank that avoided a $100 million trading error by catching a schema drift in their risk models. The conversation touches on open-source projects like Great Expectations and how they compare to commercial platforms. Listeners will learn why data observability is quickly becoming a must-have for any data-driven organization. #DataObservability #DataEngineering #DataQuality #DataPipelines #GreatExpectations #Snowflake #Uber #DataFreshness #SchemaDrift #DataLineage #DataCulture #DataOps #BusinessTechnology #FexingoBusiness #BusinessPodcast #DataInfrastructure #DataMonitoring #RealTimeData Keep every episode free: buymeacoffee.com/fexingo

In this episode, Lucas and Luna dive into the quiet revolution in enterprise software pricing driven by data contracts. They explore how companies like Snowflake and Databricks are moving from consumption-based to value-based pricing, using data contracts to define what customers actually pay for. The hosts examine a case study from a mid-sized fintech that saved 40% on data costs by implementing contract-based governance, and discuss why this trend is redefining vendor relationships. Listen to learn how data contracts aren't just for pipeline reliability anymore—they're a lever for cost control and negotiation power. #DataContracts #EnterprisePricing #Snowflake #Databricks #DataGovernance #Fintech #DataCosts #ValueBasedPricing #DataEngineering #BusinessAndTechnology #FexingoBusiness #BusinessPodcast #DataBusiness #DataInfrastructure #CostOptimization #VendorManagement #CloudData #DataAnalytics Keep every episode free: buymeacoffee.com/fexingo

Lucas and Luna explore how synthetic data is transforming enterprise AI training, focusing on the case of a mid-size insurance company that slashed its model development cycle by 60 percent using synthetic data from a vendor called Mostly AI. They break down the economics — training on synthetic data cut their data-labeling costs by $1.2 million annually — and the technical trade-offs, including fidelity ceilings and bias preservation. The episode also touches on the regulatory grey zone: the SEC and FINRA haven't issued clear guidelines on synthetic data in model governance, leaving compliance teams in a bind. Lucas argues that synthetic data is less a replacement for real data and more a strategic multiplier for data-scarce or privacy-sensitive use cases. Luna presses on the reproducibility problem: if two teams generate synthetic data from the same source, will their models converge? The answer has implications for everything from fraud detection to credit scoring. Specific, grounded, and forward-looking. #SyntheticData #EnterpriseAI #DataEngineering #MostlyAI #ModelTraining #DataPrivacy #InsuranceTech #Compliance #SEC #FINRA #DataLabeling #CostReduction #FidelityCeiling #FraudDetection #CreditScoring #Business #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo

Data mesh promised to decentralize data ownership and scale analytics across enterprises. But most implementations stall after the first six months. Lucas and Luna unpack why — looking at the gap between the architectural ideal and the organizational reality. They examine a specific case: a Fortune 500 retailer that spent $12 million on a data mesh platform only to see fewer than 15 percent of domains actually publish usable datasets. The hosts trace the root causes: confusion over data product definitions, missing governance guardrails, and a cultural mismatch between centralized IT teams and business-domain owners. They also explore what people often get wrong about Zhamak Dehghani's original principles — particularly the notion that domain ownership means total autonomy. By the end, listeners will understand why the hottest data architecture trend of the past decade may actually be a team-structure problem in disguise. #DataMesh #DataArchitecture #DataGovernance #DataProduct #DomainOwnership #ZhamakDehghani #Fortune500 #DataEngineering #DataStrategy #OrganizationalDesign #Business #Technology #DataInfrastructure #Analytics #DataTeam #FexingoBusiness #BusinessPodcast #TheDataBusinessPodcast Keep every episode free: buymeacoffee.com/fexingo

Lucas and Luna dive into the growing field of model observability—how companies monitor machine learning models in production beyond just accuracy metrics. They discuss the 2025 Aporia/WhyLabs survey showing 72% of enterprises have suffered a model-degradation incident costing over $200,000, and why traditional data observability tools miss ML-specific issues like data drift, concept drift, and feature skew. The episode centers on a case study: how a mid-size e-commerce company caught a 15% revenue drop from a model that silently retrained on corrupted data, saved by real-time drift detection. They explore the emerging stack: WhyLabs, Arize AI, Evidently AI, and the shift from batch monitoring to streaming observability. Lucas argues that as ML models become more embedded in core business logic, observability is shifting from a data-engineering concern to a boardroom priority. Luna questions whether the tooling is mature enough for non-tech enterprises. The episode closes with a reflection on the cost of not knowing what your model is doing. #ModelObservability #MLMonitoring #DataDrift #ConceptDrift #MachineLearning #DataEngineering #ArizeAI #WhyLabs #EvidentlyAI #Aporia #FeatureSkew #ProductionML #DataQuality #BusinessAndTechnology #FexingoBusiness #BusinessPodcast #TheDataBusinessPodcast #DataInfrastructure Keep every episode free: buymeacoffee.com/fexingo

Episode 15 of The Data Business Podcast explores a surprising shift: companies are starting to price data products based on the quality and reliability of the data, not just its volume. Lucas and Luna examine how firms like Snowflake and Databricks are experimenting with tiered pricing tied to data freshness and completeness, and what this means for data teams building internal dashboards and customer-facing data products. The episode drills into a 2025 survey by Monte Carlo that found 42% of data leaders now tie pricing to data quality guarantees, up from just 12% two years prior. Lucas argues this changes the incentive structure for data teams, while Luna questions whether it could price out smaller companies. The conversation also touches on how data contracts — the topic of prior episodes — enable this shift by providing formal guarantees about data shape and timeliness. A concrete example from a mid-market SaaS company illustrates the practical implications. #DataPricing #DataQuality #DataContracts #Snowflake #Databricks #MonteCarlo #DataProducts #EnterpriseData #BusinessPodcast #FexingoBusiness #DataBusiness #DataEngineering #SaaS #DataMonetization #DataFreshness #RevenueModel #DataInfrastructure #DataObservability Keep every episode free: buymeacoffee.com/fexingo

Episode 14 of The Data Business Podcast. Lucas and Luna explore how data contracts—formal agreements between data producers and consumers—are moving from niche engineering teams to enterprise-wide adoption. They examine a concrete case: a mid-size fintech that cut data pipeline failures by 40 percent after implementing schema-level contracts. The hosts discuss the shift from reactive data quality checks to proactive guarantees, the tension between agility and rigor, and what this means for data teams building trust with business stakeholders. Specific numbers, real trade-offs, no theory without application. #DataContracts #DataQuality #DataEngineering #EnterpriseData #SchemaEvolution #Fintech #DataGovernance #DataPipelines #DataObservability #DataManagement #BusinessAndTechnology #Podcast #FexingoBusiness #BusinessPodcast #DataInfrastructure #Analytics #DataTeams #DataProducers Keep every episode free: buymeacoffee.com/fexingo

Episode 13 of The Data Business Podcast: Lucas and Luna dive into data observability — the operational discipline that separates companies that catch pipeline failures before they reach production from those that discover a broken revenue report at the quarterly board meeting. They explore why traditional monitoring isn't enough, how the five pillars of observability (freshness, volume, distribution, schema, and lineage) form an early warning system, and walk through a real 2025 case where a fintech company avoided a $70 million mis-pricing event thanks to a distribution anomaly alert. The hosts also unpack the economics: observability tools cost roughly 5–10 percent of a data engineering budget, but the cost of one undetected outage can be ten times that. Listeners learn the concrete difference between 'is my pipeline running?' and 'is my data trustworthy?' — and why the second question is the one that keeps chief data officers up at night. If you work in data infrastructure, analytics, or information products, this episode gives you a framework for convincing your organization that observability is insurance, not overhead. #DataObservability #DataEngineering #DataInfrastructure #DataQuality #DataReliability #Monitorama #MonteCarloData #DataPipelines #AnalyticsEngineering #DataTrust #DataOps #BusinessPodcast #TechnologyPodcast #FexingoBusiness #BusinessPodcast #DataDriven #DataGovernance #DataCulture Keep every episode free: buymeacoffee.com/fexingo

Lucas and Luna explore how feature stores — centralized repositories for machine learning features — have moved from infrastructure nicety to operational necessity. They dive into the concrete case of a mid-sized fintech that cut model development time by 60 percent after adopting Feast, and discuss why feature reuse and consistency matter more than most teams realize. The episode also touches on the quiet tension between data engineering and ML teams, and how the feature store acts as a shared contract between them. With specific metrics and a real team's timeline, this is a grounded look at an infrastructure layer that's quietly becoming as important as the data warehouse. #FeatureStore #MachineLearning #DataEngineering #MLOps #Feast #FeatureEngineering #DataInfrastructure #Fintech #Business #Technology #DataScience #ModelDevelopment #DataPlatform #FeatureReuse #TrainingServingSkew #DataGovernance #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo

Lucas and Luna explore the rise of data unions — collective organizations that pool personal data to negotiate with tech giants. They examine the Swash union, which has aggregated browsing data from over 100,000 members to sell insights to ad platforms, and discuss the regulatory implications under GDPR and the EU Data Act. The episode drills into the economics of data as labor, comparing union bargaining to individual consent fatigue, and asks whether data unions can shift the power balance in the attention economy. Specific numbers include a 60% revenue share model and a projected $500 million market for personal data marketplaces by 2027. #DataUnions #PersonalData #DataEconomics #Swash #GDPR #EUDataAct #DataAsLabor #AttentionEconomy #DataBargaining #ConsentFatigue #TechRegulation #DataCollectives #Privacy #DataMarketplaces #BusinessAndTechnology #DataInfrastructure #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo