Why fixing your data beats building an AI lab?

Why fixing your data beats building an AI lab?

Most enterprises are racing to build AI labs while the real problem sits in their data layer. Santiago Guillotti, Chief Data and Analytics Officer at Chubb LATAM, leads a 130-person data organization and his conclusion is direct: no vendor can fix your data problems for you. AI platforms are replaceable. The data you generate is not. Santiago outlines for Saket how GenBI is creating outsized efficiency gains in mature companies where 35 to 40% of FTEs are still tied to BI-related tasks, and where cloud migration is only the beginning of a much larger transformation. Topics discussed: * GenBI as an efficiency lever in mature enterprise organizations * Cloud migration from on-prem legacy systems to Azure and Databricks * Building data teams differently in startups vs. large regulated companies * Hiring for cultural fit across Latin America, Asia, and the US * Scaling from 10-person to 100-person data organizations * AI's impact on data engineering roles and intern programs * Why data, not AI platforms, is the real competitive advantage * Three pillars of AI transformation in the insurance sector * Automating end-to-end claims flows with conversational AI * Getting POCs approved in slow-moving regulated industries

Look before you merge: catching downstream impact before it breaks production

What happens when data breaks in production and no one sees it coming? At Recce, Dori Wilson is building the systems to make data reviews as systematic as software code reviews, and the gap between those two worlds is bigger than most teams realize. Dori Wilson, Head of Data at Recce, walks Saket through how data validation has historically lagged behind software engineering, why dashboards are becoming obsolete, and what it actually takes to build stakeholder trust when the numbers tell a story no one wants to hear. From her time at Uber, Mux, CircleCI, and a YC-backed startup she co-founded, Dori brings a practitioner's lens to every layer of the data stack. Topics discussed: * Comparing data validation gaps to software DevOps and CI/CD maturity * How Recce's AI data review agent surfaces lineage impact before merging code * Using human-in-the-loop review alongside automated distribution checks * Why dashboards are becoming obsolete and what replaces them * Segmenting feature adoption data to avoid selection bias in retention analysis * Building stakeholder trust through anticipatory storytelling and appendix slides * Using skills and contextual memory layers to improve AI agent performance * Career advice for data professionals: abundance mindset and not self-limiting

How a healthcare startup migrated from Azure to GCP and kept production running

Jo Hjersman has spent over five years at a healthcare XR startup wearing every hat on the data stack, from research data scientist to Head of Data. In this episode, he walks Saket through how he built and scaled a behavioral analytics platform for mental health care, navigated a full cloud migration from Azure to GCP with critical systems kept online throughout, and keeps the whole system running efficiently on a startup budget. The conversation gets into the real mechanics: how intent-driven data design changes what you can extract from immersive environments, how to handle schema evolution without breaking downstream consumers, and why the data scientist and ML engineer roles are converging toward an ownership model rather than a specialization model. Topics discussed: * Designing intent-driven data capture in immersive healthcare environments * Building a bronze/silver/gold architecture from scratch at a startup * Managing schema evolution across multi-device data sources * Planning and executing a full Azure-to-GCP cloud migration with minimal downtime * Handling HIPAA-adjacent compliance through UUID abstraction at the schema level * Why data scientists and ML engineers are converging toward a full-stack ownership model * What bootcamps miss about real-world data engineering at scale * Evaluating infrastructure trade-offs from both a technical and business cost perspective

The language mistake data leaders make when presenting to executives

Christine Pierce spent over 20 years at Nielsen building and running the data systems behind a $600 billion advertising ecosystem. She now consults on media measurement, AI deployment, and data monetization. Her take on enterprise AI cuts through the noise: most deployments fail not because of the technology, but because of change management and misaligned incentives. Christine outlines how the shift from panels and surveys to transactional and big data fundamentally changed the measurement challenge, why deduplication across devices remains unsolved, and how organizations can spot the real barriers to AI adoption before they invest. Topics Discussed: * Shift from survey-based to transactional data in media measurement * Deduplication challenges across devices and platforms * Independent vs. platform-owned measurement in programmatic advertising * Why enterprise AI deployments fail: change management over technology * Individual productivity vs. enterprise-level AI transformation * Using synthetic data and agentic AI for brand research * Framework for advising companies on data monetization * Communicating data ROI in business outcomes, not technical outputs * Agentic models as the next evolution of programmatic advertising

Context Is the Differentiator, Not the Model

Most organizations have thousands of dashboards and still can't get a simple answer from their data. Francois Lopitaux, SVP of Product Management at ThoughtSpot, argues that the problem was never the data, it was a fundamental misunderstanding of who analytics tools were actually built for. In this episode, Saket and Francois trace the full arc from dashboard factories to agentic BI, and why the shift from self-service analytics to proactive insight delivery is finally within reach. Francois walks through how ThoughtSpot's semantic layer approach, built years before LLMs arrived, is now the foundation for its agentic product Spotter. Rather than using text-to-SQL and accepting hallucination risk, ThoughtSpot translates natural language into search tokens first and then generates deterministic SQL, preserving consistency and giving business users a way to verify every answer. The conversation goes deep on context engineering, how to enrich a semantic model with business rules and memory, and why the LLM is only as good as the context layer surrounding it. Topics discussed: * Why dashboards failed business users from the start * ThoughtSpot's semantic layer and search token approach * How agentic BI differs from conversational analytics * Why text-to-SQL introduces trust problems at scale * Combining structured, enterprise, and unstructured data sources * MCP integration for real-time data and automated actions * Context engineering as the new governance layer * Automating semantic model enrichment with AI * The evolution from reactive dashboards to proactive agents * How data leaders need to rethink their role in an agentic world