DatAInnovators & Builders

Nexla

DatAInnovators & Builders features Chief Data Officers and data leaders sharing real strategies for conquering data complexity and building AI solutions that work. Host Saket Saurabh, CEO of Nexla, delivers practical insights on tackling data variety, moving AI from pilot to production, and making transformation actually happen.

  1. Why a data catalog isn't ready for AI agents without a semantic layer

    22h ago

    Why a data catalog isn't ready for AI agents without a semantic layer

    What happens when a company built on 16 acquisitions tries to run on one trusted data model? At Precisely, Dave Shuman has spent six years building the data infrastructure needed to take the business from a $300M mid-market firm to a billion-dollar global software company, and the lessons from that journey cut straight to the heart of what makes or breaks an enterprise AI strategy. Dave outlines for Saket why most AI initiatives fail before they start: organizations skip the unglamorous work of cataloging, semantic alignment, and quality observability in a rush to get to the model. He breaks down how Precisely is building the semantic layer that lets agents operate autonomously, why data governance needs to feel like a supple leather glove rather than an iron fist, and what the CDO role has to become if it wants to stay relevant in an AI-first organization. Topics discussed: Why most transformative AI starts with a data catalog, not code Building a semantic layer so agents can operate autonomously Managing AI model overconfidence in production rollouts Distinguishing active governance from passive governance in agentic workflows Structuring data products across raw, component, and trusted zones Integrating unstructured documents into structured data pipelines What the CDO role must become in an AI-first organization Lessons from scaling through acquisitions and closing on one set of systems

    54 min
  2. Agent management: how do you govern AI you didn't build

    Jun 16

    Agent management: how do you govern AI you didn't build

    What happens when half the room at a Gartner executive workshop raises their hand to say they've shipped AI to production and tracked ROI? Conor Jensen, Global Field CDO at Dataiku, uses that moment to reframe the entire "AI is failing" narrative and get specific about what separates the companies making it work from the ones still stuck in prototype mode. Conor walks Saket through the compounding mistakes he sees across enterprise AI programs, from skipping legal and governance early, to misreading which problems data can actually solve, to deploying tools company-wide before proving a single use case. The conversation covers data products, agent management, the limits of code generation tools for data teams, and why the "citizen data scientist" framing has always been slightly wrong. Topics discussed: - Why close to half of executives at a recent Gartner workshop reported tracked AI ROI - Engaging legal and compliance early as a speed accelerator, not a blocker - Building a use case prioritization process as a core organizational capability - Treating AI outputs as data products that require ongoing ownership and maintenance - Semantic and context layers as the foundation for AI-ready data products - Managing agents deployed out of the box in enterprise platforms versus custom-built agents - Why code generation tools are less effective for data engineering than software development - The meteorologist reframe: domain experts gaining new tools, not becoming data scientists - Where 50 to 60 percent of data team backlogs can be self-served by the business - Why predictive analytics projects hit dead ends in non-tech companies with limited data volume

    56 min
  3. Why fixing your data beats building an AI lab?

    Jun 2

    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

    41 min
  4. Look before you merge: catching downstream impact before it breaks production

    May 19

    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

    47 min
  5. How a healthcare startup migrated from Azure to GCP and kept production running

    May 5

    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

    33 min
  6. The language mistake data leaders make when presenting to executives

    Apr 21

    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

    50 min
  7. Context Is the Differentiator, Not the Model

    Apr 7

    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

    54 min
  8. 15 AI Agents and Nothing to Show for It

    Mar 24

    15 AI Agents and Nothing to Show for It

    What happens when a company runs 15 AI agents across its processes but still cannot measure their impact on the top or bottom line? According to Yorck F. Einhaus, former Global CDO at Liberty Mutual and CDO at Farmers Insurance, that is not an AI problem. It is a data problem, and it is the most common reason enterprise AI programs fail to scale. Yorck shares how he led Farmers Insurance through a migration from on-prem to Snowflake on AWS, using that transition as a forcing function to settle long-standing disputes between actuaries, underwriters, and product teams over how the same data should be defined. He also unpacks his framework for decision intelligence, which he applies at every level of an organization: determine only what information you truly need to make a decision, and treat everything else as noise. Topics discussed: •        Why insurance is inherently a data business •        Building physical and technological innovation lab environments •        Using VR to scale claims adjuster training at Farmers •        Aligning AI strategy directly to business strategy outcomes •        Data governance as the primary barrier to AI at scale •        Migrating to Snowflake to enforce data quality upstream •        AI and multimodal data in claims, including AI-generated fraud detection •        Shifting from backward-looking claims history to predictive catastrophe modeling •        Why lateral career moves accelerate long-term advancement •        The evolving CDO role in an AI-first enterprise

    45 min
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DatAInnovators & Builders features Chief Data Officers and data leaders sharing real strategies for conquering data complexity and building AI solutions that work. Host Saket Saurabh, CEO of Nexla, delivers practical insights on tackling data variety, moving AI from pilot to production, and making transformation actually happen.

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