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

Episode 79 of The Data Business Podcast. Lucas and Luna explore how forward-thinking data teams are using data contracts not just for reliability but for precise cost allocation. They dive into a case at a mid-size fintech that cut its cloud data warehouse bill by 28 percent by attaching consumption tags to contract clauses. Lucas explains the mechanics of 'cost-attributed schemas' and Luna questi...

Data contracts are getting a lot of attention as a way to enforce schema and quality guarantees between producers and consumers. But a new practice is emerging: applying contract testing — borrowed from software engineering — to data pipelines. Lucas and Luna explore how companies like Monzo and others are using consumer-driven contract tests to catch breaking changes before they hit production. T...

Episode 77 of The Data Business Podcast explores the rise of data clean rooms—secure environments where companies can join datasets for analytics without exposing raw data. Lucas and Luna dissect a specific case: how a major retailer and a CPG brand used a clean room to measure ad effectiveness without sharing customer-level data. They walk through the architecture, the trade-offs (query performan...

In Episode 76 of The Data Business Podcast, Lucas and Luna explore the growing trend of semantic layers — a middle layer between raw data and business tools that lets non-technical users query metrics like 'monthly recurring revenue' without knowing SQL. They examine how companies like Airbnb and Intuit have implemented semantic layers using tools like Looker's LookML and Apache Calcite to reduce...

Episode 75 of The Data Business Podcast dives into the rising practice of query cost forecasting — a technique data teams use to predict and control cloud spending before bills arrive. Lucas and Luna break down how a mid-market fintech company used a simple cost-per-query model to reduce unexpected overages by 40% in Q1 2026. They explore the difference between reactive cost monitoring and proacti...

Episode 74 dives into reverse ETL — the practice of syncing data from a warehouse back into operational tools like CRMs, ad platforms, and customer support systems. Lucas and Luna unpack how companies like Airbnb and Uber use this approach to activate customer data in real time, without building bespoke integrations. They discuss the rise of reverse ETL platforms like Hightouch and Census, and why...

In this episode, Lucas and Luna explore the emerging practice of data product contracts—formal agreements between data producers and consumers that specify schema, freshness, quality SLAs, and pricing. They use the example of a mid-sized e-commerce company that reduced data incidents by 40 percent after implementing contracts for its top 20 data products. The hosts discuss how these contracts diff...

Lucas and Luna dive into a surprising case from a mid-sized fintech called LendRight, which lost over $400,000 in a single quarter because of a data lineage failure that cascaded from a renamed column in a source system through 17 downstream dashboards, models, and reports. They break down how the incident happened, why traditional lineage tools missed it, and what LendRight's data team did differ...

Data contracts are formal agreements between data producers and consumers that specify schema, freshness, semantics, and SLAs. In this episode, Lucas and Luna break down how data teams at companies like Uber and Spotify use contracts to prevent pipeline breaks, reduce debugging time, and build trust in data products. They discuss the trade-offs between schema enforcement and flexibility, how contr...

In Episode 70, Lucas and Luna explore how data teams are shifting from passive metadata catalogs to active metadata systems that trigger real-time actions. They use Gartner's prediction that by 2026, 70% of new data and analytics deployments will incorporate active metadata. The conversation focuses on a case study from a mid-size e-commerce company that implemented active metadata to automaticall...

Data teams are building internal feature stores — a central library of reusable machine learning features — to cut duplication and speed up model development. Lucas and Luna explore how companies like Uber, Airbnb, and a European fintech called Weavr are structuring feature stores, from versioning and governance to serving latency. They discuss why storing feature definitions is harder than storin...

Episode 68 of The Data Business Podcast with Fexingo explores how data teams are adopting SaaS-like pricing for internal data products. Lucas and Luna break down the shift from flat cost allocations to consumption-based models, using Databricks' unit-based pricing and Snowflake's credits as real-world analogs. They discuss why the 'free lunch' mindset fails, how data product managers are setting p...

Most data platforms are designed for reading, not writing. But a new pattern called writeback is changing how enterprise teams interact with their analytics — turning dashboards into action surfaces. Lucas and Luna unpack how companies like Airbnb and a mid-sized retailer are using writeback to close the gap between insight and execution. They walk through the architecture, the tooling (including...

In episode 66 of The Data Business Podcast, Lucas and Luna explore how synthetic data is becoming a practical tool for privacy compliance. They discuss a case where a European insurance company used synthetic data to share customer insights with regulators without exposing real personal information. The conversation covers the 2025 EU AI Act requirements, the difference between fully synthetic and...

Lucas and Luna explore how data observability platforms have evolved beyond simple monitoring into full debugging tools. They examine a case study where a mid-size fintech cut incident response time by 70 percent using column-level lineage and automated root cause analysis. The hosts discuss the shift from reactive alerting to proactive data health scoring, and why some teams are still resistant t...

Lucas and Luna dive into a real case where a single SQL query at a mid-sized logistics company triggered a million dollars in unplanned compute costs. They explore how incomplete data lineage — the inability to trace which upstream tables and downstream dashboards a query touches — led to months of undetected waste, a blame game between engineering and analytics, and ultimately a rebuild of the co...

Episode 63 of The Data Business Podcast: Lucas and Luna explore how enterprise data teams are using machine learning to automate data classification at scale. They break down the shift from manual tagging to AI-driven systems, using a case study from a large financial institution that reduced classification time by 85 percent using an open-source tool called Squirro. They discuss the challenges of...

Episode 62 of The Data Business Podcast. Lucas and Luna drill into column-level lineage—why tracking data at the column level is becoming a must-have for enterprise data teams debugging complex pipelines. They unpack how a mid-market fintech used column-level lineage to cut a five-hour incident triage to 18 minutes, why column-level lineage is a prerequisite for effective data contracts, and how t...

Lucas and Luna explore the emerging practice of monetizing internal data APIs within enterprises. They focus on the case of a mid-sized financial services firm, Portico Analytics, which launched an internal API marketplace in late 2025. By June 2026, the marketplace has generated $2.3 million in internal revenue, funded by chargebacks to business units. The hosts break down the pricing model—per-c...

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...

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 flexi...

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 —...

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...

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 e...

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. Th...