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Become and Expert in Agentic Analytics and AI Evals at https://dataneighbor.com/Learn more about Index:https://index.app/In this episode of the Data Neighbor Podcast, we sit down with Xavier Pladevall, co-founder of Index, to break down what’s actually changing in business intelligence and what’s not as AI enters the analytics stack.Index is building an AI-powered data platform that blends dashboards, SQL, and an agentic chat experience. But as Xavier explains, the hard parts of data work still show up in the same places: messy data, unclear questions, and getting a room of humans to trust and act on what they see.In this episode, you’ll learn:- Why most “modern data” talk is disconnected from what teams actually use day to day- Why dashboards remain the default interface for stakeholders- How product design and UI quality change trust in data- What Index 1.0 is solving today, and what Index 2.0 unlocks next- How Index 2.0 makes analytics more proactive with recommended questions- Where AI breaks in analytics and why data quality is still the bottleneck- Why “data slop” compounds AI errors in surprising ways- Why data scientists are not going anywhere, even as AI takes on more tasks- Why SQL is more important than ever, just more abstractedWhat the real future looks like beyond “chat with your data” #datapodcast #analytics #businessintelligence #datatools #datascience #aiforanalytics #agenticanalytics #productanalytics #dataleadership #dataneighbor #index

Explore Data Neighbor free workshops on AI evaluation and agentic analytics (plus our live, hands on course): https://dataneighbor.com/Learn more about LiveDocs:https://livedocs.com/In this episode of the Data Neighbor Podcast, we sit down with Arsalan Bashir, founder and CEO of LiveDocs, to unpack one of the biggest shifts happening in data right now: tools that don’t just help you analyze data, but start doing parts of the analysis for you.If you care about the future of analytics workflows, this is a must watch.Connect with the team (tell us YouTube sent you!):Shane Butler: https://linkedin.openinapp.co/b02feSravya Madipalli: https://linkedin.openinapp.co/9be8cHai Guan: https://linkedin.openinapp.co/4qi1rConnect with Arsalan:https://www.linkedin.com/in/arslnb/In this episode, you’ll learn:- Why “chat with your data” is the wrong mental model for where analytics is headed- What an “AI data scientist” actually means in practice (and what it doesn’t)- Why context matters more than schema for getting correct results from AI-generated analysis- How LiveDocs blends notebooks, app-building, and agentic workflows in one workspace- What it looks like when AI automates the 0→80% of data work (aka “yak shaving”)- How scheduling, notifications, and lightweight automation change the day-to-day of analysis- Where AI can go wrong in data work, and how to design for trust and review- Why handoff between stakeholders and data teams is still the core bottleneck- What new skills will matter most for analysts and data teams as agentic tools become standard#datapodcast #analytics #businessintelligence #datatools #datascience #aiforanalytics #agents #agenticanalytics #productanalytics #dataleadership #dataneighbor #livedocs

Explore Data Neighbor free workshops on AI evaluation and agentic analytics (plus our live, hands on course): https://dataneighbor.com/Learn more about Count:https://count.co/In this episode of the Data Neighbor Podcast, we sit down with Ollie Hughes, co founder and CEO of Count, to unpack why traditional BI often fails at the real job teams hire data for: making better decisions, faster.If you care about the future of analytics workflows, this is a must watch.Connect with the team (tell us YouTube sent you!):Shane Butler: https://linkedin.openinapp.co/b02feSravya Madipalli: https://linkedin.openinapp.co/9be8cHai Guan: https://linkedin.openinapp.co/4qi1rConnect with Ollie:https://www.linkedin.com/in/hughesoliver/In this episode, you’ll learn:- Why dashboards are not designed for collaborative decision making- What the “service trap” is and how it keeps data teams in reactive work- How operational clarity helps companies simplify what matters- Why a canvas based interface changes how teams reason with data- What collaborative analysis looks like in practice between analysts and stakeholders- How agile decision making can reduce time to insight and time to decision- How Count thinks about AI agents for analytics without black boxes- What skills will matter most for data teams as AI takes on more of the mechanical work #datapodcast #analytics #businessintelligence #datatools #datascience #aiforanalytics #agents #agenticanalytics #aievals #productanalytics #dataleadership #dataneighbor #count

Join Our Upcoming AI Evals Cohort! https://maven.com/dataneighbor/ai-evalsSubscribe to our newsletter: https://dataneighbor.substack.com/DNP Swag Store: https://shop.dataneighbor.com/In this episode of the Data Neighbor Podcast, we do something different. No guest. Just the three of us reflecting on what we learned after a year of running the show.We talk honestly about what surprised us most, what turned out to be harder than expected, and why the “real work” of podcasting is everything around the recording: editing, publishing, distribution, and yes, thumbnails.We also dig into what hosting a podcast has done for our careers: better communication reps, more surface area in the industry, and a faster growing network than we expected.Finally, we share a preview of what we are building in 2026, including two big themes we keep seeing across the industry: AI product evaluation and AI powered analytics.If you are thinking about starting a podcast, already building one, or just want a candid behind the scenes look at what it takes to stay consistent for a full year, this episode is for you.Connect with the team (tell us which platform sent you!):Shane Butler: https://linkedin.openinapp.co/b02feSravya Madipalli: https://linkedin.openinapp.co/9be8cHai Guan: https://linkedin.openinapp.co/4qi1rIn this episode, you’ll learn:- What nobody tells you about the time investment behind a podcast- Why consistency is the hardest part, and how to design for it- How having multiple co hosts reduces burnout and improves the show- How podcasting can expand your network and professional opportunities- What we learned from a year of interviewing builders and operators- What we are focusing on in 2026: AI evaluation and AI powered analytics #podcasting #podcast #creator #contentcreation #careergrowth #networking #aiproduct #aievaluation #analytics #datascience #dataneighbor

In this episode of the Data Neighbor Podcast, we sit down with Shelby Heinecke, PhD, Senior AI Research Manager at Salesforce, to break down what modern AI teams actually look like and how enterprise AI gets built in practice.Shelby shares how her team moves research into production, why small crisp problem definitions outperform ambitious abstractions, and how evaluation before development has become a non negotiable part of the workflow.We also talk about the shifting shape of AI teams, the rising importance of domain experts, and why interdisciplinary collaboration is quickly becoming the core of the field.If you want an inside look at how leading AI orgs actually operate, this is the episode.Connect with the team (tell us which platform sent you!): - Sravya Madipalli: https://linkedin.openinapp.co/9be8c- Shane Butler: https://linkedin.openinapp.co/b02fe - Hai Guan: https://linkedin.openinapp.co/4qi1r Connect with Shelby:https://www.linkedin.com/in/shelby-heinecke/In this episode, you’ll learn:- How enterprise AI research teams actually set direction- Why crisp scope and early evaluation decide which projects reach production- What makes interdisciplinary collaboration essential for AI success- How small models and agents are being deployed across Salesforce- What skills matter most for the next generation of AI roles- Why embodied agents may represent the next major leap in AI#aipodcast #airesearch #salesforce #aiteams #aiproducts #llm #datascience #mlengineering #aidevelopment #agents #embodiedai #dataneighbor #aifuture

Join us for an inspiring conversation with Sadie St Lawrence, founder and CEO of Women in Data, and the Human Machine Collaboration Institute! Sadie shares her incredible journey from piano and neuroscience to pioneering a global movement empowering tens of thousands of women in data and AI. Discover her unique insights on building impactful communities, navigating career changes, and the evolving role of humans in the age of AI.In this episode, we cover:- Sadie's fascinating career trajectory, from a neuroscience lab to a data science pioneer and community builder.- The origin story of Women in Data, starting from a personal need for community to a global movement of 70,000 members across 120+ countries.- The critical role of consistency and trust in building a thriving community and achieving professional growth.- The current landscape of diversity in data careers, with eye-opening statistics and the significant impact of female leadership.- Sadie's visionary perspective on the future of work, where humans become "orchestra conductors" in a world augmented by AI.- The mission of the Human Machine Collaboration Institute (HMCI) in tackling fundamental questions about humanity, emotion, and consciousness in the AI era.- Practical advice on cultivating curiosity, breaking patterns, and leveraging your innate desire to learn for career advancement and personal fulfillment.Whether you're looking to start a community, advance your career in data, or curious about the philosophical implications of AI, Sadie's story and insights offer invaluable lessons. Tune in to understand why consistency, community, and curiosity are your greatest assets in the rapidly changing world of technology.Connect with Hai, Sravya, and Shane (let us know which platform sent you!):- Hai: https://www.linkedin.com/in/hai-guan-6b58a7a/- Sravya: https://www.linkedin.com/in/sravyamadipalli/- Shane: https://www.linkedin.com/in/shaneausleybutler/#DataScience #AI #WomenInData #CareerAdvice #TechLeadership #CommunityBuilding #HumanMachineCollaboration #Curiosity #DiversityInTech #Neuroscience #Consciousness #FutureOfWork #DataCareers #STEM #ProfessionalDevelopment #Podcast #DataNeighbor

In this episode of the Data Neighbor Podcast, we sit down with Lei Tang, co-founder and CTO of Fabi AI, to explore the messy reality of data quality, the limits of self-serve BI, and why Vibe Analytics might be the shift organizations need. With experience leading data science at Lyft, Walmart Labs, and Clari, Lei brings grounded, first-hand insights into how modern data teams can thrive even when their data is anything but clean. You’ll learn: - Why “perfect data” is a myth and what to do instead - How AI-native BI changes the self-serve equation - The challenges and promise of Vibe Analytics - Why critical thinking, not SQL, is your most valuable skill - The case for AI-driven semantic layers over manual curation - How AI agents might evolve into collaborative teammates - Real risks of AI hallucinations and how to build guardrails If you’ve ever dealt with stakeholder overload, a graveyard of unused dashboards, or felt stuck waiting on a “single source of truth” project to finish, this one’s for you. We get real about trade-offs, show how AI can amplify impact (not replace you), and dive into what the future of analytics workflows might actually look like. Connect with Lei: https://www.linkedin.com/in/lei-tang-ai/ Connect with Hai, Sravya, and Shane (let us know which platform sent you!): - Hai: https://www.linkedin.com/in/hai-guan-6b58a7a/ - Sravya: https://www.linkedin.com/in/sravyamadipalli/ - Shane: https://www.linkedin.com/in/shaneausleybutler/ #datascience #vibeanalytics #fabi #selfservebi #aiinanalytics #dataquality #dataengineering #dataops #aibi #dataneighborpodcast #aiproducts #dataworkflows #analyticsleadership #futureofanalytics

AI is evolving faster than ever, and the people keeping up with it are the AI Research Engineers. In this episode of the Data Neighbor Podcast, we sit down with Sandi Besen, AI Research Engineer at IBM Research, to unpack what it actually means to live and work on the bleeding edge of AI.Sandi shares what it takes to move from model demos to real systems, why research engineering is becoming one of the most critical jobs in tech, and how she prototypes, evaluates, and ships new agent frameworks at record speed.Connect with the team (tell us which platform sent you!):- Shane Butler: https://linkedin.openinapp.co/b02fe- Sravya Madipalli: https://linkedin.openinapp.co/9be8c- Hai Guan: https://linkedin.openinapp.co/4qi1rConnect with Sandi: https://www.linkedin.com/in/sandibesen/In this episode, you’ll learn about:-What an AI Research Engineer actually does day-to-day-How research engineering bridges AI research and production-Why requirements frameworks help agents stay reliable-The trade-offs between low-code and pro-code approaches-How evals and observability are evolving for agent systems-The human side of working at the frontier of AI #aipodcast #airesearch #ibmresearch #aiagents #agentframeworks #llm #datascience #mlengineering #automation #aidevelopment #beeai #aiproducts #researchengineering #dataneighbor #aifuture #ibm

AI is moving fast, but reliable agents are still rare. In this Data Neighbor Podcast, we sit down with Jigyasa Grover, ML Engineer at Uber, author of Sculpting Data for ML: The first act of Machine Learning, and member of Google’s ML Advisory Board, to unpack why most AI agents fail and what it really takes to build ones you can count on.Jigyasa shares how to design, evaluate, and secure reliable agent systems - from memory management and adversarial testing to using human judgment without slowing down innovation. Connect with the team (tell us YouTube sent you!):- Shane Butler: https://linkedin.openinapp.co/b02fe- Sravya Madipalli: https://linkedin.openinapp.co/9be8c- Hai Guan: https://linkedin.openinapp.co/4qi1rConnect with Jigyasa: https://www.linkedin.com/in/jigyasa-grover/In this episode, Jigyasa explains how agents evolve beyond simple workflows into autonomous systems, why evals are at the heart of reliable AI, and how developers can prevent silent failures through better design, testing, and observability.You'll learn about:-Why most AI agents fail and how to engineer reliability from day one-Workflow agents vs LLM-based agents-How evals, memory hygiene, and adversarial testing improve reliability-When to use traditional ML instead of LLMs-Designing for human judgment, security, and recovery in agent systems #aipodcast #aiagents #aidevelopment #aiengineering #llm #mlops #datascience #agentdesign #workflowagents #memory #evaluation #productstrategy #aiproductmanagement #autonomousagents #aiethics #aideployments #reliableai #dataneighbor #jigyasagrover #agenticai

AI is reshaping data and analytics, moving from brittle dashboards to agentic, conversational workflows. In this Data Neighbor Podcast, we sit down with Barry McCardel, CEO & Co-founder of Hex, to unpack how agentic analytics, natural-language querying, and semantic modeling are changing how data teams (and the whole business) make decisions. Connect with Shane, Sravya, and Hai (tell us which platform sent you!):- Shane Butler: https://linkedin.openinapp.co/b02fe- Sravya Madipalli: https://linkedin.openinapp.co/9be8c- Hai Guan: https://linkedin.openinapp.co/4qi1rConnect with Barry: https://www.linkedin.com/in/barrymccardel/In this episode, Barry shares how Hex evolved beyond notebooks into a self-serve BI + AI agent platform, why PMF is a moving target in AI, and how great data teams are shifting from ticket queues to curation, governance, and partnership.You'll learn about:- Agentic analytics in practice: from “chat with my data” to explainable, reproducible workflows (thinking traces, SQL visibility, versioned projects).- How semantic models (Hex, Snowflake, dbt, Cube) unlock trusted self-serve BI.- How to find PMF in AI: sustaining product-market fit when model capabilities shift weekly.- What is Data team 2.0: moving repetitive “pull a number” requests to agents so humans focus on curation, modeling, experimentation, and strategy.- How to ship rigor at speed: why transparency, lineage, and observability matter for trust—not just accuracy. #aiproductmanagement #agenticanalytics #conversationalbi #datateams #selfserveBI #semanticlayer #dbt #snowflake #dataapps #llm #aiagents #mlops #productstrategy #dataneighbor #hextech #hex #datascience #ai

Is the future of Machine Learning Engineer (MLE) jobs secure in the age of AI? Umang Chaudhary, an ML Engineer at TikTok (formerly Amazon), dives deep into this pressing question and shares his invaluable insights on navigating the rapidly evolving ML landscape. In this episode, Umang recounts his unique journey from web development to a thriving MLE career, the challenges of ML interview prep, and why he's now dedicated to guiding aspiring ML professionals.Discover how Umang leverages cutting-edge AI tools like Gemini and Grok in his daily workflow and for interview preparation, offering a fresh perspective on productivity and learning. Learn about the common fears and questions his mentees face regarding AI's impact on job security and how to differentiate between "real-world" ML skills and those needed to ace interviews. This episode is a must-watch for anyone looking to break into or advance in the ML field, offering a blend of career guidance, practical tips, and a compelling look into the future of AI.In this episode, you will learn:* The evolving role of AI and LLMs in daily ML workflows, from solution building to enhanced productivity.* How Umang leverages AI tools like Gemini Pro and Grok for efficient coding, document analysis, and comprehensive ML system design interview preparation.* Umang's unique journey, transitioning from web development to a Machine Learning Engineer role at Amazon and then TikTok.* Current concerns from aspiring ML professionals about AI's impact on the future of MLE jobs and Umang's perspective on career longevity.* Inspiring stories of individuals making unconventional transitions into ML engineering roles, including web developers, data analysts, and product managers.* A four-step plan to effectively break down and master Machine Learning interview preparation (ML fundamentals, ML design, ML system design, ML coding).* The critical importance of patience and a strategic "numbers game" approach to landing an ML job in today's competitive market.Connect with Umang:https://www.linkedin.com/in/mlwithumang/https://www.instagram.com/umangabroad/https://www.instagram.com/ml.with.umang/Connect with Hai, Sravya, and Shane (let us know which platform sent you!):- Hai: https://www.linkedin.com/in/hai-guan-6b58a7a/- Sravya: https://www.linkedin.com/in/sravyamadipalli/- Shane: https://www.linkedin.com/in/shaneausleybutler/#MLEngineer #MachineLearning #AIJobs #LLM #AICareers #MLCareerGuidance #MLInterviewPrep #TikTok #Amazon #DataScience #TechCareers #CareerTransition #Grok #ChatGPT #Gemini #Entrepreneurship #MachineLearningEngineer #AIInnovation #DataNeighborPodcast

Unlock the secrets to building a future-proof data organization that thrives on impact, not just effort. Join us as we sit down with Manoj Mohan, former Engineering Leader of Data and AI Platforms at Intuit, and a seasoned leader from Meta, Cloudera, and Apple. Manoj shares his deep insights from two decades in the data, ML, and AI space, offering pragmatic strategies for long-term success.In this episode, you’ll discover:- Hard-won lessons from early data warehouse failures and the critical role of humility and scalability in data projects.- Why embracing a "platform as a product" mindset for data engineering is essential for long-term efficiency and avoiding KPI chaos.- Manoj Mohan's powerful "3 Gs" framework (Grounded, Guarded, Governed) for deploying Large Language Models (LLMs) responsibly and effectively within the enterprise, comparing them to high-speed Formula One cars that need robust guardrails.- A visionary outlook on what a future-proof data organization might look like by 2030, where AI-driven insights are seamlessly accessible to every employee.- Practical advice for startups on balancing speed with sustainable data infrastructure, ensuring foundational blocks are built alongside product innovation.- Key principles for data leaders, including the importance of continuous learning, unlearning, and focusing on problem-solving over tools.Whether you're a data engineer, an AI enthusiast, a data leader, or navigating data challenges in a startup, this episode is packed with invaluable wisdom to help you build resilient, scalable, and impactful data systems. Connect with Hai, Sravya, and Shane (let us know which platform sent you!):- Hai: https://www.linkedin.com/in/hai-guan-6b58a7a/- Sravya: https://www.linkedin.com/in/sravyamadipalli/- Shane: https://www.linkedin.com/in/shaneausleybutler/#DataEngineering #AIPlatforms #LLMs #DataStrategy #Scalability #DataGovernance #ResponsibleAI #PlatformAsAProduct #FutureOfData #DataOrganization #StartupData #EnterpriseAI #DataLeadership #MLEngineering #DataManagement #ManojMohan #DataNeighborPodcast #TechLeadership

AI is changing product management, from how teams prototype to how they measure success. In this episode of the Data Neighbor Podcast, we’re joined by Aman Khan, Head of Product at Arize AI (LLM evaluation & observability). Aman breaks down the three emerging AI PM archetypes (AI-native PM, AI platform PM, and AI-powered PM), how to move from “vibe coding” to eval-driven development (EDD), and why aligning evals to business outcomes matters more than any single accuracy score. He also shares hard-won tactics for handling subjectivity in LLM outputs, setting user expectations in UX, and deciding when rigor can (and can’t) slow down speed. In this episode, you’ll learn:-The three ways AI shows up in PM work—and how those roles are converging.-A practical ladder from “vibe checks” to EDD (evals in dev & production), including LLM-as-a-judge and when to trust it.-How to tie evals to business metrics (trust, value, speed) and resolve “good eval, bad outcome” conflicts.-UX patterns for long-running agent tasks (progress, ETAs, checkpoints) that preserve trust.-Where AI coding tools help most (and least) across engineers, PMs, and data teams.Connect with Aman Khan:LinkedIn: https://www.linkedin.com/in/amanberkeley/🌐 Website: https://amank.ai🏢 Arize AI: https://arize.com/ Arize AIConnect with Shane, Sravya, and Hai (let us know which platform sent you!):👉 Shane Butler: https://linkedin.openinapp.co/b02fe👉 Sravya Madipalli: https://linkedin.openinapp.co/9be8c👉 Hai Guan: https://linkedin.openinapp.co/4qi1r #aiproductmanagement #aievals #llmobservability #productmanagement #datascience #mlops #aiagents #evaluation #productstrategy #dataneighbor #arizeai #llms

Are you overwhelmed by ad-hoc data questions? Ever wondered how to automate business intelligence with AI? Join us as we sit down with Lohitaksh Yogi, a seasoned AI product leader from companies like ServiceNow and Adobe, to explore the exciting world of AI data agents and Natural Language Business Intelligence (NLBI). Lohit shares his journey from early machine learning chatbots to cutting-edge LLM-powered conversational AI, offering invaluable insights into building and deploying these transformative systems.In this episode, you will learn:- The evolution of chatbots: Understanding the limitations of early rule-based systems vs. the powerful context-awareness of LLMs.- The vision for Natural Language Business Intelligence (NLBI): How close we are to asking a chatbot natural language questions and getting instant insights from our data.- Key challenges in AI deployment: Navigating schema ambiguities, data inconsistencies, and the critical issue of AI hallucinations.- Strategies for building an effective AI data agent: From designing intuitive user experiences (UX) to implementing robust error handling and feedback loops.- The paramount importance of data governance: Protecting sensitive information and ensuring data privacy when leveraging AI for internal data analysis.- Why internal beta testing is crucial: Breaking your system internally before exposing it to external stakeholders to build trust and ensure accuracy.- The right mindset for AI adoption: Viewing AI as an investment for long-term productivity gains, not a quick fix, and understanding its rapid evolution.Whether you're a data professional looking to boost productivity, a business leader seeking to automate data requests, or just curious about the future of AI in the enterprise, this episode provides actionable strategies and a realistic outlook on deploying AI data agents. Connect with Lohitaksh: https://www.linkedin.com/in/lohitakshyogi/Connect with Hai, Sravya, and Shane (let us know YouTube sent you!):- Hai: https://www.linkedin.com/in/hai-guan-6b58a7a/- Sravya: https://www.linkedin.com/in/sravyamadipalli/- Shane: https://www.linkedin.com/in/shaneausleybutler/#Chatbots #ConversationalAI #AIDataAgents #NaturalLanguageBI #LLM #LargeLanguageModels #DataGovernance #AIHallucination #DataAnalytics #DataScience #MachineLearning #ProductLeadership #AIStrategy #UserExperience #DataWorkflow #AIPodcast #TechInsights #DataProduct #EnterpriseAI

What truly defines a good data scientist, and how can you excel in this rapidly evolving field? Join us as we sit down with Siddharth Ranganathan, Director of Data Science at Microsoft, to uncover practical insights on navigating data science careers, balancing rigor with business needs, and the transformative impact of AI. Siddharth shares invaluable lessons from his extensive experience, emphasizing impact over complexity and strategy over execution.In this episode, we cover:- What constitutes good data science: Focusing on decisions, impact, scientific rigor, and practicality.- Balancing speed and rigor in analysis: Strategies for delivering timely insights without compromising integrity.- Common misunderstandings about product data science: It's more than just building ML models; it's a strategic, cross-functional role.- How to become a strategic data scientist: Shifting focus from outputs to outcomes and asking better questions.- The evolving landscape of data science with AI and Gen AI: Anticipating the rise of role-based agents and the convergence of tech and business.- Identifying and avoiding common career traps for data scientists, such as staying in execution mode or over-indexing on technical depth.- Key factors directors look for in promotions: Driving impact beyond your current level, securing patrons, and clearly communicating your contributions.- The most underrated skill for a data scientist: The ability to break down complex problems and deal with ambiguity.Whether you're an aspiring data scientist, a mid-level professional looking to grow, or a leader shaping data teams, this episode offers a wealth of actionable advice to elevate your data science career and impact. Connect with Hai, Sravya, and Shane (let us know which platform sent you!):- Hai: https://www.linkedin.com/in/hai-guan-6b58a7a/- Sravya: https://www.linkedin.com/in/sravyamadipalli/- Shane: https://www.linkedin.com/in/shaneausleybutler/#DataScience #ProductDataScience #AI #GenAI #LLMs #CareerGrowth #StrategicDataScientist #Microsoft #DataScienceCareer #DataSciencePromotions #DataScienceAdvice #DataScienceLeadership #ImpactOverComplexity #TradeOffs #DataNeighborPodcast