DatAInnovators & Builders

Nexla

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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.

8 de set.

Why the real AI moat lives in your data when models are commodities

Terry Miller, Vice President AI & Machine Learning at Omada Health, calls frontier models "very expensive commodities." With 14 years of longitudinal data for over a million members, the real competitive advantage lives in proprietary data and the workflows built on top of it. Terry walks Saket through how reusable templates and tightly bounded applications make agent deployments succeed, why combining traditional machine learning with LLMs unlocks new capabilities, and what "edge healthcare" means for decentralized care from the home. Topics discussed: Frontier models as commodities, data as the real moat Building reusable agent templates for production systems Tightly bounded applications as the path to agent success Edge healthcare and decentralizing care to the home Combining traditional ML with LLMs for new capabilities Preventing the next wave of high-cost patients Deep personalization as table stakes for enterprise AI Policy gaps around autonomous AI in regulated industries
25 de ago.

Why grand unified data architecture kills AI projects before they start

For Anusha Dandapani, Chief Data & AI at UNICC, what keeps her up isn't model accuracy. It's the asymmetry of consequences. A wrong commercial AI call costs money, but in a humanitarian context, it can mean aid never reaches the people who need it. She tells Saket why she'd rather run a less sophisticated model with rigorous decision architecture than drop in a state-of-the-art system, and why nobody wants to talk about data lineage until it's too late. Topics discussed: Weighing asymmetry of consequences in humanitarian AI decisionsAvoiding grand unified architecture in favor of minimum interoperability layersCost-sharing a shared AI platform across ten UN organizationsBaking responsible AI into design instead of retrofitting laterTreating data lineage as the real foundation for AI trustDrawing the line between reversible and irreversible AI decisionsPrioritizing decision architecture over model sophisticationReframing data quality gaps as risk decisions for leadership
11 de ago.

Why AI programs stall after the pilot: the accountability gap no one has solved

Most enterprises know AI has value. Getting it into the financials is a different problem entirely. Asif Mujahid has spent over a decade in healthcare data and AI, and his read is direct: the technology problem is largely solved. What remains is trust, governance, and accountability, and most organizations are still working out how to get there. Asif breaks down why centralizing data and AI ownership is non-negotiable, how he frames the CDO role across three distinct archetypes, and what it actually takes to move AI from value identification to value realization in a regulated, high-stakes environment. Topics discussed: Why AI is now a risk governance problem, not a technology problem The three pillars required to build stakeholder trust in AI models Healthcare AI use cases across administrative, supply chain, and care management How generative AI expands volume, velocity, and variety of patient data The privacy and bias risks that derail AI programs before launch Why CDO success depends on centralizing AI ownership end to end The three CDO archetypes and how their importance is shifting Moving from "actionable insight" to joint ownership of business outcomes Bridging academic models and real-world operational constraints
28 de jul.

Why most enterprise data isn't actually big data

Most data leaders chase distributed infrastructure they don't need, paying what Mahesh Mishra calls a complexity tax. As VP of Artificial Intelligence at Cloudera, he argues most enterprise data isn't big data at all, and platform readiness, more than model quality, is the real barrier to production AI. Mahesh breaks down knowledge engineering as the missing layer beyond prompt and context engineering, explains why traditional data catalogs are becoming obsolete, and maps out how Iceberg and Lance are reshaping the lake house for agentic workloads. Topics discussed: - Distinguishing knowledge engineering from prompt and context engineering - Building decision flow and decision tracing languages for agents - Why data catalogs must evolve into semantic knowledge graphs - Combining Iceberg and Lance for structured and unstructured data - Three reasons AI projects stall before reaching production - Treating AI agents as identity-bound team members for governance - Asking whether your data actually qualifies as big data - Forecasting agent-driven decision making and falling engineering costs
14 de jul.

Why most organizations are solving the wrong data problem for AI

Justin Heller spent nearly 11 years building an enterprise data management program from the ground up, most recently as former SVP and Chief Data Officer at Synchrony Financial. He joins Saket to unpack the discipline shift that most organizations miss: structured and unstructured data have to be managed differently. The rise of generative AI has exposed a relevance gap, the piece most data quality programs were never built to measure, tied to the absence of a records management discipline capable of categorizing, labeling, and disposing of files at scale. Topics discussed: Building a data management operating model from the ground upApplying industry frameworks to establish a common data management languageDetecting versus preventing bad data through quality controlsManaging metadata across structured and unstructured repositoriesRedefining ROI as relevance of information in the age of generative AIWhy records management is having a resurgence because of AIAvoiding a police-state approach to data managementMVP-first approach to scaling AI foundations enterprise-wide
30 de jun.

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
16 de jun.

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
2 de jun.

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

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.

Criado por

Nexla
Anos de atividade

2025 - 2026
Episódios

21
Classificação

Livre
Copyright

© Nexla
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DatAInnovators & Builders