Most companies build data catalogs that nobody uses. In this episode, Lucas and Luna examine why metadata initiatives so often collapse — and what teams like Airbnb and Netflix did differently. Lucas walks through the distinction between 'inventory' and 'catalog,' citing a Gartner stat that 80 percent of data catalog projects stall within 18 months. Luna pushes back on whether tools like Alation or DataHub actually solve the trust problem. The conversation lands on three practical shifts: embedding cataloging into engineering workflows, treating metadata as code, and measuring catalog adoption with the same rigor as product metrics. If you work in data engineering, analytics, or data product management, this episode gives you a lens to diagnose a failing catalog before it's too late.

#DataCatalog #MetadataManagement #DataEngineering #DataGovernance #DataQuality #Airbnb #Netflix #Alation #DataHub #Gartner #Analytics #DataInfrastructure #DataProduct #DataTeam #BusinessAndTechnology #FexingoBusiness #BusinessPodcast #DataBusiness

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Lucas and Luna explore how data contracts—formal agreements between data producers and consumers—can prevent the chaos of broken pipelines and untrustworthy datasets. Using Instacart's real-world implementation as a case study, Lucas explains how a grocery delivery company saved its analytics team from drowning in bad data by adopting schema enforcement, ownership SLAs, and automated testing. Luna raises the tension between agility and governance, and they discuss whether data contracts are the next must-have practice for data teams or just another over-engineered solution. Tune in for a practical look at reducing friction in modern data stacks.

#DataContracts #Instacart #DataEngineering #DataQuality #Analytics #DataGovernance #SchemaOnRead #SchemaOnWrite #DataPipelines #ETL #SLA #Business #Technology #DataInfrastructure #FexingoBusiness #BusinessPodcast #TheDataBusinessPodcast #DataTeam

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Every data team swears their pipeline is clean — until the CFO asks why revenue is down 2 percent and nobody can agree on the number. In this debut episode, Lucas and Luna look inside a single malfunction at a company you've definitely heard of: how a stale address file at a major US bank silently misrouted $400 million in marketing spend over nine months. They trace the bug from a forgotten ETL job through a cascading failure in the downstream analytics layer, and they name the one practice — ownership tagging at the column level — that would have stopped it. Along the way they define the show's territory: not the hype around data, but the gritty, unglamorous work of keeping it honest. No theory. Just the story behind the glitch.

#DataQuality #EnterpriseData #ETL #DataEngineering #Analytics #Banking #MarketingSpend #DataGovernance #ColumnLevelOwnership #PipelineFailure #BusinessIntelligence #DataInfrastructure #Finance #Technology #FexingoBusiness #BusinessPodcast #TheDataBusinessPodcast #PodcastDebut

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