Podcasts about enterprise data

Send us Fan MailIn this episode of the WTR Small-Cap Spotlight podcast, Gautam Garg, Vice President of Finance of eGain Corporation (NASDAQ: EGAN), joins host Tim Gerdeman, Vice Chair, Co-Founder, and Chief Marketing Officer of Water Tower Research, and WTR Analyst James Kisner.eGain is a leader in AI-powered knowledge management, helping Global 2000 enterprises unify siloed content into an AI Knowledge Hub that delivers accurate, compliant answers across customer service and adjacent functions.Garg explains why trusted knowledge has emerged as core AI infrastructure and why enterprise AI initiatives frequently underperform when built on stale or inconsistent data. He walks through recent product launches including the AI Knowledge Suite for Retail Banking, the IVA voice agent, Evaluator, Agentic Studio, and the developer-focused Composer platform, which supports integrations with Copilot, Claude, Gemini, and Cursor via MCP connectors.The conversation also covers a surge in RFP activity, a fast-growing partner ecosystem, expansion into HR and field service verticals, and eGain's profitable, debt-free financial profile heading into fiscal year 2027.

What happens when AI systems stop acting like assistants and start acting like autonomous decision-makers inside your business? And if those systems are pulling information from fragmented, inconsistent, and poorly governed data environments, how much trust can organizations really place in the outcomes? In today's episode, I'm joined by Terry Dorsey for a fascinating conversation about the growing gap between AI ambition and enterprise reality. Terry brings decades of experience spanning enterprise architecture, business intelligence, operations, healthcare, utilities, manufacturing, and defense. Long before AI became the headline topic dominating every boardroom conversation, he was already working deeply in semantic modeling, natural language systems, and the architectural foundations that modern AI now depends on. At the center of our discussion is the new AI Trust Gap report from Denodo, which reveals why so many organizations are struggling to move AI projects from experimentation into reliable production environments. We explore why live data matters so much in an agentic AI world, why "more data" often creates more confusion instead of clarity, and how inconsistent business meaning across systems quietly undermines AI trust inside large organizations. Terry explains why many enterprises are still operating on architectures originally designed for historical reporting and analytics, while now expecting those same environments to support autonomous AI systems making real-time operational decisions. From semantic sprawl and duplicated business logic to governance failures and fragmented security models, we unpack the hidden technical debt that AI is now exposing at scale. The conversation also takes a deeper philosophical turn as we discuss why enterprise meaning itself may become the future control plane for AI. Terry shares why provenance, explainability, and semantic consistency are no longer optional concerns reserved for compliance teams, they are becoming foundational requirements for trustworthy AI systems capable of operating autonomously. We also discuss why governance cannot be bolted on after deployment, how logical data management helps organizations reduce duplication and maintain operational trust, and why the companies that succeed with agentic AI will not necessarily be the fastest movers, but the ones building stable and reusable architectural foundations beneath the surface. If your organization is rushing toward AI adoption while wrestling with siloed systems, disconnected data, and growing governance concerns, this episode offers a much-needed reality check. Because, as Terry explains, the future competitive advantage may have less to do with the AI model itself and far more to do with the architecture, meaning, and trust frameworks supporting it. Useful Links Terry Dorsey LinkedIn Denodo LinkedIn Denodo Website The AI Trust Gap Report — global survey of 850 executives that explores why organizations are investing heavily in AI, but many still can't fully trust the data behind it. O'Reilly's The Rise of Logical Data Management, by Christopher Gardner — explains what's necessary to enable true self-service data access and 24/7 AI-ready data. The Enterprise AI and Data Management Glossary  — glossary that helps ensure both technical and non-technical professionals can make informed decisions, optimize strategies, and align on best practices for digital transformation. The ROI of Using the Denodo Platform alongside the Modern Data Lakehouse — Drawing on interviews with numerous global enterprises and applying a comprehensive ROI methodology, this study, conducted by independent analyst Veqtor8, found that by using Denodo alongside their data lakehouse, they realized considerable benefits. Agentic AI Manifesto — a blueprint for credible autonomy at enterprise scale. Denodo's standard for the next era of trusted, autonomous enterprise AI.

In this episode, Jure Leskovec, co-founder and chief scientist at Kumo and professor of computer science at Stanford, joins us to explore two fronts of his work: AI for science and relational deep learning. We begin with AI Virtual Cell, a multiscale effort to learn data-driven representations from proteins to cells to patients using single-cell RNA-seq data, protein language models like ESM, and structure models like AlphaFold—without hand-encoding biology. Jure then dives into relational deep learning, reframing enterprise databases as graphs and training neural networks directly on raw multi-table data. He explains Kumo's Relational Foundation Model (RFM2), which performs in-context learning over subgraphs to make accurate predictions on new databases and tasks with no training, and how this approach benchmarks against RelBench and other multi-table datasets. We also discuss real-world deployments at companies like Reddit, DoorDash, and Coinbase, explainability via attention over tables and columns, integration with agentic systems, deployment options, and practical limitations. The complete show notes for this episode can be found at https://twimlai.com/go/768.

05-20-2026 Elizabeth Thede Learn more about the interview and get additional links here: https://usabusinessradio.com/may-is-zombie-awareness-month-beware-enterprise-data-zombies/ Subscribe to the best of our content here: https://priceofbusiness.substack.com/ Subscribe to our YouTube channel here: https://www.youtube.com/channel/UCywgbHv7dpiBG2Qswr_ceEQ

Another solid conversation from Qlik Connect 2026. This time with Gregory Pierce, MBA from Amazon Web Services (AWS), and we went deep into what it actually looks like to build data and AI systems at scale.What I liked about this discussion was how practical it was. There is always a lot of talk about cloud and AI, but this was more about how teams are actually making it work.We talked about the partnership between AWS and Qlik, and how it is helping customers bring everything together. Data integration, analytics, governance, all running on a scalable foundation. Not as separate pieces, but as something that needs to work end to end.One point that really stood out was around growth. Data volumes are increasing fast, AI workloads are getting heavier, and most teams are still dealing with legacy systems. The question is not just how to move to the cloud, but how to do it in a way that sets you up for what comes next.We also got into AI and GenAI in modernization. Where does it actually help? Things like speeding up migrations, reducing manual effort, and making systems easier to understand. But at the same time, Greg was clear about being careful. If your data is not reliable, adding AI on top just increases risk.And that led to another important point. Accuracy and trust. As teams use AI to transform legacy systems, they need strong validation, governance, and a clear understanding of what is happening behind the scenes.The last part of the conversation was about flexibility. This space is changing fast. New tools, new architectures, new expectations. The teams that win are the ones that stay adaptable and do not lock themselves into one way of doing things too early.Overall, this was a very grounded conversation on how cloud, data, and AI actually come together.#data #ai #qlikconnect #qlik #daredevil #api #trust #dataquality #agentic #agents #theravitshow

What does it mean to “think like an FM analyst”? In this episode of Connected FM, Dr. Matt Tucker, Director of Knowledge and Insights at IFMA, sits down with Drew DePriest, Director of Workplace Services, Vendor Management at Salesforce, to unpack the evolving role of data, analytics and AI in facility management. Drawing on insights from IFMA's The Rise of the FM Analyst report, they explore why the FM analyst isn't a job title, but a mindset grounded in curiosity, problem solving and cross-functional thinking. From fragmented data systems to the growing importance of storytelling in the C-suite, the conversation highlights what it takes to turn data into decisions that drive real business value. They also dive into: The six key traits shaping modern FM professionals Why data confidence is still a major gap across the industry How FM fits into the broader enterprise data ecosystem The role of AI as a “co-pilot” for analysis and decision-making What the next 5–10 years of FM could look like in a digital-first world This episode is sponsored by TMA Systems! Discover more at https://www.tmasystems.com/ifmapodcast Timestamps: 00:00 Introduction 02:50 Six Traits Breakdown 06:29 Are Traits Happening 08:40 FM In Data Ecosystem 10:57 Tool Stack Reality 13:14 From Reports To Decisions 14:49 AI As Copilot 17:17 AI Confidence Concerns 19:37 Skills Training Pathways 22:21 FM In Five Years 24:01 Wrap Up And Outro Connect with Us:LinkedIn: https://www.linkedin.com/company/ifmaFacebook: https://www.facebook.com/InternationalFacilityManagementAssociation/Twitter: https://twitter.com/IFMAInstagram: https://www.instagram.com/ifma_hq/YouTube: https://youtube.com/ifmaglobalVisit us at https://ifma.org

Why Knowledge, Not Technology, Is the Foundation of Successful AI-Driven Data MigrationGuest: Dominik Wittenbeck, Group CTO at SNP GroupHost: Seth Earley, CEO at Earley Information SciencePublished on: April 20, 2026In this episode, Seth Earley speaks with Dominik Wittenbeck, Group CTO at SNP Group, a 1,600-person global software and solutions firm with 30 years of SAP-centric data migration expertise. They explore why AI is only as good as the institutional knowledge behind it, how agentic AI is transforming high-stakes enterprise migrations, and why organizations must treat data migration as a strategic opportunity rather than a cost-reduction exercise. Dominik shares hard-won insights on semantic architecture, governance, and what executives consistently get wrong when applying AI to critical enterprise processes.Key Takeaways:AI is not a silver bullet for data migration - it requires deep, domain-specific knowledge to produce deterministic, auditable results.Enterprise data migration is a team sport requiring cross-functional specialists; AI accelerates the work but cannot replace that expertise.The real opportunity in migration is not just moving data - it is cleaning it up and optimizing processes while the organization is already changing.Agentic AI is transforming the full migration lifecycle, from pre-sales solutioning and blueprint generation to rule creation and automated testing.Governance established once without ongoing enforcement decays quickly - organizations must build continuous oversight into critical processes from the start.Value mapping, not just structural mapping, is the dominant challenge in SAP migrations, and AI can significantly accelerate semantic alignment work.Executives should focus AI investments on problems that truly matter, not easy wins - meaningful impact comes from finding where differentiation really counts.Insightful Quotes:"In order to run complicated systems which have a critical impact on your business, they need enough grounding. You actually need to feed the knowledge into the agentic system that you're building on top of, in order to make sure that you get deterministic results in the end." - Dominik Wittenbeck"Rather than re-architecting the whole thing, try to identify what the critical processes really are, that if they are not exercised correctly, really hurt your business. Find where the value lies - or if you can't find that, find where your risk lies." - Dominik Wittenbeck"Sometimes cheap is quite costly, and sometimes slowing down speeds things up. If you're moving stuff from one system to another and you say, we'll clean it up later - that's never going to happen. It's like moving from one house to another with an attic full of boxes and junk." - Seth EarleyTune in to discover why successful AI-driven enterprise migration depends less on technology and more on institutional knowledge, governance, and treating transformation as a strategic opportunity.LinksLinkedIn: https://www.linkedin.com/in/dominik-wittenbeck-61a64669/Website: https://www.snpgroup.comThanks to our sponsors:VKTREarley Information ScienceAI Powered Enterprise Book

In this episode, Ajay Kapare, President & CEO, ELLKAY, explains how strong data integrity, interoperability, and governance form the foundation for scalable AI success, and why organizations that prioritize enterprise data strategy are the ones seeing real results.This episode is sponsored by ELLKAY.

It's long been said that good data is necessary before you can have good AI. But to an increasing degree, AI is also helping businesses manage, analyze, and generate their data too.With AI code generation already well understood, businesses are also leaning on natural language processing and agentic AI to help their experts such as data engineers and data scientists automate their work more effectively.What does all this mean for businesses looking to adopt AI? And how is the UK AI market maturing?In this episode, recorded on the ground at Databricks AI Days London 2026, Rory speaks to Michael Green, UK&I managing director at Databricks and Richard Shaw, AVP Field Engineering at Databricks, to better understand how data and AI are converging.Read more:What is natural language processing?‘A true vote of confidence': Databricks announces $850m UK investment as firm looks to quadruple London office footprint"We want AI to work for Britain": The UK government wants to upskill 10 million Brits in AI by 2030 – and the courses are free to accessThe UK's AI ambitions depend on channel partnersMicrosoft says fear of falling behind is driving an AI arms race among UK businesses – and it's fueling record adoption ratesDatabricks wants to train 100,000 people in AI across the UK and Ireland – here's how to get involved

Today, we're joined by Andrew Stern, CEO of Quilt Software – not a fabric company, but a software company powering 20K+ specialty retailers across North America. We talk about:The advantages of serving thousands of smaller customers, such as predictabilityProviding users with actionable intelligenceServing a highly varied client baseBringing best-of-breed solutions to small market segmentsWhy the little guys are chronically underserved

Innovation is no longer a niche topic, it is a public priority with implications for economic mobility, workforce development, public trust, and regional identity. The latest in tech innovation is Artificial Intelligence (AI), which his now widely embedded in industries from workforce recruitment and healthcare to business operations and design.rnrnGreater Cleveland is also entering a defining moment in its economic evolution. Our region is home to some of the nation's (and world's) most influential enterprises-organizations that are modernizing at scale, adopting AI and data-driven strategies, and reshaping how people live, work, bank, manufacture, and receive care. At the center of this transformation are women leading technology and innovation across our largest institutions.rnrnPanelists: Elise Bockman, VP, Enterprise Data and Insights, Sherwin-Williams; Amy G. Brady, Chief Information Officer, KeyBank; Amy Merlino, MD, VP - Chief Health Information Officer, Cleveland Clinic; Katrina Redmond, Executive Vice President and Chief Information Officer; EatonrnModerated by Felicia Johnson, Executive Technology Leader, AI Adoption, Business Value & Digital Transformation

„Chat with your Enterprise Data“ eine Vision, die Self Service Analytics auf ein neues Level heben könnte. Statt lange nach passenden Datensätzen zu suchen, stellt man eine Frage und die Antwort mit den relevanten Daten wird mir einfach ausgespielt. In dieser Episode von TDWI Data Universe spricht Claudia Koschtial mit Dr. Simon Harrer darüber, wie solche Ansätze auch für Data Governance genutzt werden können. Denn in dezentralen Datenlandschaften mit vielen Datenprodukten, Contracts und Policies wird es zunehmend schwierig, alle Regeln und Nutzungskontexte im Blick zu behalten. Ein Prototyp zeigt, wie Large Language Models Governance-Regeln, Data Contracts und Nutzungszwecke analysieren können, um erste Entscheidungsempfehlungen zu geben – etwa bei der Frage, ob ein Datensatz für einen bestimmten Zweck verwendet werden darf. Eine Episode über „Chat with your Enterprise Data“, über den Wandel vom Datenkatalog zum Datenmarktplatz und darüber, wie KI und menschliche Urteilskraft gemeinsam Data Governance skalierbar machen.

Today, we're joined by Evan Powell, Founding CEO of DeepTempo, a pioneer in behavioral threat detection powered by deep learning. We talk about:The cybersecurity problems – and solutions– resulting from AIPossible continued existence of huge software companies generating high revenuesThe source of SaaS value: The AI model or the software/code?How to price SaaS apps when most users are AI agentsThe proliferation of custom, homegrown software in enterprises

Cohesity CPO Vasu Murthy breaks down how enterprise data protection has evolved into a 24/7 cyber recovery operation — and why the battle against ransomware is now being fought with AI on both sides.Topics Include:Cohesity protects, secures, and provides insights into enterprise data globally.70% of Fortune 500 companies trust Cohesity with their critical data.Cyber attacks are now the dominant threat to enterprise data resilience.Cohesity recently merged with Veritas, dramatically expanding its customer base.Real-world rescue: a cyber-hit company ran payroll on time anyway.Recovery operations run 24/7, with dozens of active rescues at any time.The threat landscape is now AI versus AI — and escalating fast.Cohesity enables customers to practice cyber recovery drills through automation.AI is accelerating product cycles — planning that took months now takes weeks.More than a third of Cohesity's latest release was AI-assisted code.Flatter teams and less hierarchy are defining the new CPO playbook.AWS partnership innovations like Glacier Instant Retrieval delivered 30% cost savings.Participants:Vasu Murthy – Chief Product Officer, CohesitySee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Cyera is accelerating its mission to secure enterprise data in the age of AI. CEO Yotam Segev joins Inside the ICE House to discuss the company's $400 million Series F funding round and how Cyera plans to invest in building a unified platform that connects AI, identity, and data security. Segev explains why enterprises are still in the early innings of AI adoption and why securing data will be essential as AI systems become more embedded in daily operations. 

In this episode of Future Fuzz, Vince Quinn is joined by Jaime Gastelle, Senior Director of Marketing at Landgate, for a deep dive into the rapidly shifting AI landscape. Together, they unpack the rise of AI browsers, the battle between Google, OpenAI, Anthropic, and Perplexity, and what these changes mean for digital marketers heading into 2026 and beyond.Jaime breaks down real usage data across consumer and enterprise AI tools, explains why traditional SEO is giving way to GEO (Generative Engine Optimization), and outlines how brands can stay visible as traffic increasingly stays inside AI interfaces. The conversation also explores the massive infrastructure demands behind AI—data centers, energy grids, and land siting—and why AI's growth is as much a physical challenge as a digital one.A must-listen for marketers, tech leaders, and anyone trying to understand where AI, search, and digital strategy are headed next.Guest BioJaime Gastelle is the Senior Director of Marketing at Landgate, a data intelligence platform that helps energy developers, real estate professionals, and investors make smarter land siting decisions. Landgate provides deep analytics for data centers, renewable energy, oil and gas, and other large-scale infrastructure projects, bridging the worlds of real estate and energy development.With experience marketing to both enterprise and consumer audiences, Jaime brings a unique perspective on AI adoption, digital strategy, and how emerging technologies are reshaping marketing, search, and infrastructure planning.TakeawaysAI browser adoption is accelerating, with Google, OpenAI, and Anthropic competing for long-term user attentionChatGPT dominates consumer usage, while Claude leads in enterprise adoptionAI interfaces will increasingly keep users from leaving platforms, reducing traditional website trafficDigital marketers must shift from SEO to GEO (Generative Engine Optimization)Being cited, recommended, and referenced by AI models will matter more than rankings aloneAI chips consume significantly more energy, putting major strain on the U.S. power gridData center growth is driving massive land, energy, and infrastructure demand—especially in Virginia and TexasPlatform consolidation will favor mega brands unless marketers adapt quicklyChapters00:00 Welcome and Introduction 00:30 Meet Jaime Gastelle and Landgate 01:40 How AI Is Driving Data Center Growth 02:09 The AI Browser Battle Explained 03:26 Google, Gemini, and the Future of Search 04:29 Experimenting With AI Browsers Like Comet 05:14 Black Hat Marketing and Misspelled Domains 06:42 Authenticity vs. AI-Perfect Content 08:28 LLM Usage: Consumer vs. Enterprise Data 10:26 Why Claude Leads Enterprise Adoption 11:09 Daily Searches Across Major Platforms 12:19 The Coming Traffic Shift to AI Interfaces 14:26 Cultural Impact of ChatGPT as a Brand 15:08 SEO vs. GEO: What Marketers Must Change 18:48 Energy Demands of AI and Data Centers 20:11 Land Siting, Power Grids, and Infrastructure 21:12 How to Connect With Landgate 21:34 Final Predictions for AI and Marketing in 2026LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow Jaime Gastelle on LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow Vince Quinn on LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

02-18-2026 Elizabeth Thede Learn more about the interview and get additional links here: https://usadailytimes.com/2026/02/18/the-ides-of-march-is-fast-approaching-take-heed-of-any-warnings-in-your-enterprise-data/ Subscribe to the best of our content here: https://priceofbusiness.substack.com/ Subscribe to our YouTube channel here: https://www.youtube.com/channel/UCywgbHv7dpiBG2Qswr_ceEQ

Today, I am speaking with Angana Jacob, Head of the Research Data group within the Enterprise Data business at Bloomberg.We talk about Angana's career path through quantitative research and data platforms, and how the industry has evolved from a world dominated by bespoke models and backtests to one where many models have become increasingly commoditized. A central theme of our conversation is the idea that while models are easier than ever to replicate, data — how it's sourced, cleaned, standardized, linked, and delivered — has become the true competitive moat.We discuss what it means to “do data correctly,” how Bloomberg decides which datasets to build or sunset, how modern quants think about their data pipelines and tech stacks, and why aligning research data with production and back-office systems matters more than most people realize. Throughout the episode, we focus on Bloomberg's goal of shortening a client's time to alpha, and what that looks like in practice.At its core, this episode is about a simple but powerful idea: when everyone has access to similar models, durable edge increasingly comes from the data beneath them.Please enjoy my episode with Angana Jacob.

In this episode of the Crazy Wisdom podcast, host Stewart Alsop welcomes Roni Burd, a data and AI executive with extensive experience at Amazon and Microsoft, for a deep dive into the evolving landscape of data management and artificial intelligence in enterprise environments. Their conversation explores the longstanding challenges organizations face with knowledge management and data architecture, from the traditional bronze-silver-gold data processing pipeline to how AI agents are revolutionizing how people interact with organizational data without needing SQL or Python expertise. Burd shares insights on the economics of AI implementation at scale, the debate between one-size-fits-all models versus specialized fine-tuned solutions, and the technical constraints that prevent companies like Apple from upgrading services like Siri to modern LLM capabilities, while discussing the future of inference optimization and the hundreds-of-millions-of-dollars cost barrier that makes architectural experimentation in AI uniquely expensive compared to other industries.Timestamps00:00 Introduction to Data and AI Challenges03:08 The Evolution of Data Management05:54 Understanding Data Quality and Metadata08:57 The Role of AI in Data Cleaning11:50 Knowledge Management in Large Organizations14:55 The Future of AI and LLMs17:59 Economics of AI Implementation29:14 The Importance of LLMs for Major Tech Companies32:00 Open Source: Opportunities and Challenges35:19 The Future of AI Inference and Hardware43:24 Optimizing Inference: The Next Frontier49:23 The Commercial Viability of AI ModelsKey Insights1. Data Architecture Evolution: The industry has evolved through bronze-silver-gold data layers, where bronze is raw data, silver is cleaned/processed data, and gold is business-ready datasets. However, this creates bottlenecks as stakeholders lose access to original data during the cleaning process, making metadata and data cataloging increasingly critical for organizations.2. AI Democratizing Data Access: LLMs are breaking down technical barriers by allowing business users to query data in plain English without needing SQL, Python, or dashboarding skills. This represents a fundamental shift from requiring intermediaries to direct stakeholder access, though the full implications remain speculative.3. Economics Drive AI Architecture Decisions: Token costs and latency requirements are major factors determining AI implementation. Companies like Meta likely need their own models because paying per-token for billions of social media interactions would be economically unfeasible, driving the need for self-hosted solutions.4. One Model Won't Rule Them All: Despite initial hopes for universal models, the reality points toward specialized models for different use cases. This is driven by economics (smaller models for simple tasks), performance requirements (millisecond response times), and industry-specific needs (medical, military terminology).5. Inference is the Commercial Battleground: The majority of commercial AI value lies in inference rather than training. Current GPUs, while specialized for graphics and matrix operations, may still be too general for optimal inference performance, creating opportunities for even more specialized hardware.6. Open Source vs Open Weights Distinction: True open source in AI means access to architecture for debugging and modification, while "open weights" enables fine-tuning and customization. This distinction is crucial for enterprise adoption, as open weights provide the flexibility companies need without starting from scratch.7. Architecture Innovation Faces Expensive Testing Loops: Unlike database optimization where query plans can be easily modified, testing new AI architectures requires expensive retraining cycles costing hundreds of millions of dollars. This creates a potential innovation bottleneck, similar to aerospace industries where testing new designs is prohibitively expensive.

Bill Saltmarsh joins me to discuss where a modern CDO gets the inspiration to “operate in the producty way” in his domain, which is healthcare. Now Vice President of Enterprise Data and Transformation and the Chief Data Officer at Children's Mercy Kansas City, his early days as an analyst revealed a gap between what stakeholders asked for vs. the outcomes they sought. This convinced him that data teams need to pause, ask better questions, and prioritize meaningful outcomes over quickly churning out dashboards and reports. Bill and I discuss how a producty mindset can be embedded across an organization. He also talks about why data leaders must set firm expectations. We explore the personal and cultural shifts needed for analysts and data scientists to embrace design, facilitation, and deeper discovery, even when it initially seems to slow things down. We also examine how to define value and ROI in healthcare, where a data team's impact is often indirect.  By tying data efforts to organizational OKRs and investing in governance, strong data foundations, and data literacy, he argues that analytics, data, and AI can drive better decisions, enhance patient care, and create durable organizational value. Highlights/ Skip to: What led Bill Saltmarsh to run his team at Children's Mercy “the producty way” (1:42)  The kinds of environments Bill worked in prior that influenced his current management philosophy (4:36) Why data teams shouldn't be report factories (6:37)  Setting the standard at the leadership level vs the everyday work (10:53) How Bill is skilling and hiring for non-technical skills (i.e. product, design, etc) (13:51)  Patterns that data professionals go through to know if they're guiding stakeholders correctly (20:54)  The point when Bill has to think about the financial side of the hospital (26:30) How Bill thinks about measuring the data team's  contributions to the hospital's success (30:28) Bill's philosophy on generative AI (36:00) Links Bill Saltmarsh on LinkedIn

What happens when a former NHL player who once faced Wayne Gretzky ends up running a global data company that sits at the center of the AI boom? That question kept coming back to me as I reconnected with Mike McKee, the CEO of Ataccama, seven years after our last conversation. So much has shifted in the world since then, yet the theme that shaped this discussion felt surprisingly grounded. None of the big promises of AI can take hold unless leaders can rely on the data sitting underneath every system they run. Mike brings a rare mix of stories and experience to this theme. His journey from the ice to the C suite feels like its own lesson in discipline, teamwork, and patience, and he openly reflects on the way those early years influence how he leads today. But the heart of this conversation sits in the reality he sees inside global enterprises. Everyone is racing to build AI powered services, yet the biggest blockers are messy records, inconsistent metadata, long forgotten databases, and years of quality issues that were never addressed. It is a blunt problem, and Mike explains why the companies winning with AI right now are the ones treating data trust as a foundation rather than an afterthought. Across the discussion, he shares stories from organisations like T Mobile and Prudential, where millions of records, thousands of systems, and vast volumes of structured and unstructured data must be monitored, understood, and governed in real time. Mike walks through how teams build confidence in their data again, why quality scores matter, and how automation now shapes everything from compliance to customer retention. What stood out most is how quickly the expectations have shifted. Boards and CEOs now treat data as a strategic asset rather than an operational chore, and entire roles have emerged above the chief data officer to steer these programmes. This episode is also a reminder that AI progress is never only about models or GPUs. Mike pulls back the curtain on why organisations struggle to measure AI readiness, how they can avoid bottlenecks, and what it takes to prioritise the work that actually moves the needle. His point is simple. Without trustworthy data, AI remains a promise rather than a practical tool. With it, businesses can act with confidence, respond faster, and make decisions that genuinely improve outcomes for customers and employees. So as AI reaches deeper into systems everywhere, how should leaders rethink their approach to data trust, governance, and quality? And if you have been on your own journey with data challenges, where have you seen progress and where are you still stuck? I would love to hear your thoughts. Tech Talks Daily is Sponsored by NordLayer: Get the exclusive Black Friday offer: 28% off NordLayer yearly plans with the coupon code: techdaily-28. Valid until December 10th, 2025. Try it risk-free with a 14-day money-back guarantee.

ChatGPT has gained the power to process corporate data like never before. OpenAI says the feature improves business efficiency and insight generation. Privacy experts are watching closely as companies explore its potential and risks.Get the top 40+ AI Models for $20 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustle

Join Actualize Consulting's Senior Consultant, Dom Boyle, and Senior Manager Will Robertson as they dive into crafting a powerful data strategy within treasury organizations. They break down the full transformation journey from understanding why data strategy matters to implementing enterprise data lakes and ensuring robust data governance. Whether you're looking to centralize your financial data, improve decision-making, or explore AI applications in treasury, this Actualizing Success episode offers actionable insights to help you get started.Listen to learn more about:The fundamentals of data strategy and its importance for treasurySteps to validate existing data, build a minimum viable product (MVP), and design a comprehensive data schemaHow to consolidate systems and build a centralized enterprise data lakeKey roles and responsibilities in maintaining data governance, including data stewards and governance leadsInnovative AI and machine learning use cases, from predictive forecasting to payment anomaly detection and automated core data enrichmentThanks for listening to this episode of the Actualizing Success Podcast! We hope you enjoyed the discussion and will come back for more. In the meantime, don't forget to rate this episode and leave a review!   Get in touch with Actualize at www.actualizeconsulting.com     We'd love to hear from you! If you have any questions, comments, or would like to collaborate on a future episode, please contact us at podcast@actualizeconsulting.com.     

Join Pure Storage Technical Evangelists Don Poorman and Mike Nelson as we dive into Pure Fusion and how Pure Storage is enabling users to focus less on managing storage and more on managing their data. We start by examining the complexities of managing storage and application workloads in today's rapidly evolving IT landscape. We expose the challenges posed by legacy vendor "portfolios" which often consist of disparate products lacking unified GUIs and APIs. Learn why a fundamental shift is necessary to eliminate silos in enterprise storage, moving beyond mere federation to true integration – a unified management plane with common APIs that seamlessly operate across the entire storage ecosystem. Poorman and Nelson underscore how this integration and automation are not just valuable for traditional workloads but will be absolutely critical for the future of AI implementation, especially for inference. Our discussion pivots to Pure Storage's groundbreaking solution: Fusion. Learn what Fusion is – a powerful capability included in the latest versions of the Purity operating environment that provides an intelligent control plane for a centralized, unified management experience across an entire fleet of arrays. Our experts explain how Fusion inherently adopts Pure's API-First strategy, offering robust automation capabilities through PowerShell SDK, Ansible, and Python. They highlight how Fusion drives management, compliance, and workload configuration consistency from a single pane of glass, and how it's a vital foundation of Pure's Enterprise Data Cloud (EDC) vision. Listeners and viewers will gain invaluable insights into the tangible benefits of Fusion, including the ability to provision storage on any array from any array within the same UI, search and manage storage resources globally, and reconfigure resources without needing to access a specific array. Poorman and Nelson also explore how Fusion simplifies and standardizes workload deployments with pre-configured definitions, enabling end-to-end workload orchestration. They touch upon future enhancements like seamless interoperability across file, object, and block storage in on-site, hybrid, and cloud environments, and the exciting prospect of workload mobility. 
 Check out the new Pure Storage digital customer community to join the conversation with peers and Pure experts: 
https://purecommunity.purestorage.com/

Dell Technologies has announced Dell AI Data Platform advancements designed to help enterprises turn distributed, siloed data into faster, more reliable AI outcomes. Why it matters As enterprise AI adoption surges and data grows, organisations need a platform that can securely transform distributed, siloed data into actionable insights. The Dell AI Data Platform, a critical component of the Dell AI Factory, delivers an open, modular foundation to create value from scattered data silos. By decoupling data storage from processing, it eliminates bottlenecks and provides the flexibility needed for AI workloads like training, fine-tuning, retrieval-augmented generation (RAG) or inferencing. The platform, integrated with the NVIDIA AI Data Platform reference design, is powered by four core building blocks: Storage engines for smart data placement and seamless data movement Data engines to turn data into actionable insights Built-in cyber resiliency Data management services Together, they create a scalable, flexible foundation for customers to realise AI's full potential. Dell AI Data Platform storage engines deliver peak AI performance Dell PowerScale and Dell ObjectScale, the Dell AI Data Platform's storage engines, offer the performance, security and multi-protocol access essential for AI data. Dell PowerScale delivers NAS (network-attached storage) simplicity and parallel performance for AI workloads like training, fine-tuning, inferencing and retrieval-augmented generation (RAG) pipelines. With new integration of NVIDIA GB200 and GB300 NVL72 and ongoing software updates, Dell PowerScale delivers reliable performance, simplified management at scale and seamless compatibility with applications and solution stacks. PowerScale F710, which has achieved NVIDIA Cloud Partner (NCP) certification for high-performance storage, delivers 16k+ GPU-scale with up to 5X less rack space, 88% fewer network switches and up to 72% lower power consumption compared to competitors. Dell ObjectScale, the industry's highest-performing object platform, provides extremely performant, scalable S3-native object storage for massive AI workloads. ObjectScale is available as an appliance or through a new software-defined option on Dell PowerEdge servers that is up to 8 times faster than previous-generation all-flash object storage. New advancements improve ObjectScale's speed, scalability and efficiency. S3 over RDMA support will soon enter tech preview. It will offer up to 230% higher throughput, 80% lower latency and 98% lower CPU usage compared to traditional S3. Small object performance and efficiency improvements for large deployments deliver up to 19% higher throughput and up to 18% lower latency for 10KB objects. Deeper AWS S3 integration and bucket-level compression give developers and data scientists better tools to store, move and use large amounts of data. Dell AI Data Platform data engines power real-time AI Dell is also expanding its data engines, the specialised tools in the Dell AI Data Platform that organise, query and activate AI data. Dell's data engines are built in collaboration with trusted AI leaders like NVIDIA, Elastic and Starburst. The new Data Search Engine, developed in collaboration with Elastic, speeds decision-making by allowing customers to interact with data as naturally as asking a question. Designed for tasks like RAG, semantic search and generative AI pipelines, it integrates with MetadataIQ data discovery software to search billions of files on PowerScale and ObjectScale using granular metadata. Developers can build smarter RAG applications in tools like LangChain with the engine, ingesting only updated files to save compute time and keep vector databases current. The Data Analytics Engine, developed in collaboration with Starburst, enables seamless data querying across spreadsheets, databases, cloud warehouses and lakehouses. The new Data Analytics Engine Agentic Layer transforms raw data into business-ready products in...

Among the most significant impacts of the latest generation of artificial intelligence tools is the ability to quickly make better decisions with the data industrial enterprises already have on hand. They's also helping to fill the human resource gap, effectively replacing years of on-the-job experience. One company leading the way in this charge is Honeywell Process Solutions, and Control's Keith Larson caught up with Sharan Ragarajan, offering manager for the company's Honeywell Forge Production Intelligence capabilities to learn more about the unprecedented responsiveness the tool is making possible.

This week, we're covering the new H-1B visa fee, the Equal Employment Opportunity Commission's (EEOC's) closure of disparate impact cases, and recent key labor appointments. New Fee for H-1B Visas Employers must now pay $100,000 for each first-time H-1B petition filed on or after September 21, 2025. Current visa holders are not affected. Exceptions may apply, but details are limited. EEOC Shuts Down Disparate Impact Cases The EEOC has closed nearly all disparate impact cases following a recent executive order. These claims can still be pursued in court. The agency will also dissolve its Office of Enterprise Data and Analytics, although EEO-1 reporting requirements appear unchanged. Key Labor Roles Confirmed The Senate has confirmed Daniel Aronowitz as Assistant Secretary of Labor for the Employee Benefits Security Administration. Additionally, the Senate has confirmed over 100 other labor-related appointments—including 11 top labor positions—restoring a quorum at both the EEOC and the Merit Systems Protection Board. Visit our site for this week's Other Highlights and links: https://www.ebglaw.com/eltw407 Download our Wage & Hour Guide for Employers app: https://www.ebglaw.com/wage-hour-guide-for-employers-app. Subscribe to #WorkforceWednesday: https://www.ebglaw.com/eltw-subscribe Visit http://www.EmploymentLawThisWeek.com This podcast is presented by Epstein Becker & Green, P.C. All rights are reserved. This audio recording includes information about legal issues and legal developments. Such materials are for informational purposes only and may not reflect the most current legal developments. These informational materials are not intended, and should not be taken, as legal advice on any particular set of facts or circumstances, and these materials are not a substitute for the advice of competent counsel. The content reflects the personal views and opinions of the participants. No attorney-client relationship has been created by this audio recording. This audio recording may be considered attorney advertising in some jurisdictions under the applicable law and ethical rules. The determination of the need for legal services and the choice of a lawyer are extremely important decisions and should not be based solely upon advertisements or self-proclaimed expertise. No representation is made that the quality of the legal services to be performed is greater than the quality of legal services performed by other lawyers.    

Today we get an inside look at a major data center migration that Nokia is undertaking. Nokia is our sponsor for today’s episode. The company is moving legacy sets of data center networking equipment to its own Event Driven Automation (EDA) solution. We go behind the scenes of Nokia’s own IT department, which is supporting... Read more »

Today we get an inside look at a major data center migration that Nokia is undertaking. Nokia is our sponsor for today’s episode. The company is moving legacy sets of data center networking equipment to its own Event Driven Automation (EDA) solution. We go behind the scenes of Nokia’s own IT department, which is supporting... Read more »

In this episode of Startup Project, Nataraj interviews Ben Kus, CTO of Box, about the critical role of unstructured data in the AI revolution. They discuss the cost structures of adopting and building with AI, and how AI is transforming enterprise businesses. Ben shares Box's unique perspective, managing over an exabyte of data for 120,000 enterprise customers. Learn how AI can understand, automate, and enhance the value of your unstructured data, turning untapped potential into practical benefits. What you'll learn: Understand what unstructured data is and why it's so critical for AI applications in business.Discover Box's strategic approach to integrating with other platforms and providing AI solutions on top of your data.Learn about the pivotal moment when Box realized the potential of generative AI and how they retrofitted their platform to be AI-first.Explore early AI use cases launched at Box, including chatting with documents and data extraction, and how enterprises are adopting these features.Understand the cost implications of leveraging AI and how Box balances offering AI for free with managing expenses.Dive into Box's perspective on pricing based on usage versus outcomes, and their current subscription model.Learn about Box's approach to AI agents, their definition, and how they are being implemented to solve complex problems.Discover the concept of "context engineering" and its importance in building AI agents that understand user needs.Understand how AI is impacting productivity within Box and the broader enterprise landscape.Find out about the AI models Box is working with and how they ensure security and trustworthiness for enterprise customers.About the Guest and Host:Ben Kus: Chief Technology Officer at Box, previously VP of Product at Box and co-founder of Subspace.Connect with Guest:→ LinkedIn:   https://www.linkedin.com/in/benkus .→ Website: https://box.com/Nataraj: Host of the Startup Project podcast, Senior PM at Azure & Investor.→ LinkedIn: https://www.linkedin.com/in/natarajsindam/→ Twitter: https://x.com/natarajsindam→ Substack: ⁠https://startupproject.substack.com/⁠In this episode, we cover:(00:01) Introduction(00:35) What is unstructured data and why is Box in the center of AI?(04:05) Box's strategy on building new AI tools and features.(06:55) The moment Box realized AI was a big shift.(11:08) Earliest AI use cases launched at Box.(15:17) The cost of leveraging AI and its impact on profitability.(19:24) Pricing based on usage vs. outcomes.(22:47) Abuse prevention and handling unlimited storage.(24:16) AI products targeted for specific knowledge worker persona.(28:18) Being an AI-first company.(30:55) Defining and implementing AI agents within Box.(36:38) Form factors for agents in an enterprise product sense.(39:59) Productivity improvements with AI.(44:07) Progression in junior developers.(46:17) Document parsing and extraction.(49:16) AI models Box is working with.(52:10) Startup ideas in the AI era.Don't forget to subscribe and leave us a review/comment on YouTube, Apple, Spotify or wherever you listen to podcasts.#unstructureddata #ai #artificialintelligence #enterprisetech #cto #box #datamanagement #machinelearning #generativeai #businesstransformation #ainnovation #techleadership #cloudcomputing #datascience #podcast #startupproject #natarajsindam #digitaltransformation #enterprisesolutions #aifirst

What does the “long tail” of AI really look like in a highly regulated industry? In this episode, Dave Wollenberg, VP of Enterprise Data & Analytics at Scan, breaks it down. From cautious experimentation to enabling non-technical users to build AI-driven POCs, Dave shares a grounded, practical perspective on AI adoption inside a Medicare Advantage organization.You'll hear why the real transformation isn't just technical—it's cultural. We talk about how to shift employee mindsets, educate business teams, and unlock self-service analytics while staying compliant. If you're a tech or data leader trying to separate hype from real value, this one's for you.Key Takeaways:The long tail of AI means rethinking roles—not just automating tasksReal AI enablement starts with data quality, governance, and semantic clarityNon-technical employees can (and should) build AI proof-of-conceptsChange management will make or break your AI strategyIn regulated industries, open source and secure deployment models matterTimestamped Highlights:00:55 – What Scan Health Plan does and why AI matters in healthcare03:10 – From machine learning to generative AI: how use cases have evolved08:15 – Three types of business users and how to upskill them for AI12:40 – Shifting expectations: stakeholders want AI-powered insights, now15:20 – Why self-service BI still falls short without a solid data foundation18:35 – AI adoption isn't just IT's job—business users need to lead too22:15 – Navigating AI in regulated industries: risks, rules, and realitiesQuote of the Episode:“It's not as if there's a certain amount of work in the world, and if AI takes some, there's nothing left to do. When you make people more powerful, they add more value—and you want more of them, not fewer.”Pro Tips:Host internal hackathons to build excitement and break down resistanceUse sandbox environments to safely encourage experimentationDon't wait for technical users—give your business teams the tools to tryCall to Action:Like what you heard? Share this episode with someone exploring AI adoption in their org. Subscribe to The Tech Trek for more candid conversations with tech leaders on building, scaling, and leading through change.

In this episode, we catch up with Mai-Lan Tomsen Bukovec, VP of Technology at AWS, as she reveals three transformative approaches to enterprise data management: aggregate, curate, and extend. Drawing from her extensive experience leading AWS data services, Mai-Lan shares how organizations can build flexible, scalable data foundations that enable both innovation and governance. Join Mai-Lan as she discusses the intricacies of data infrastructure modernization with AWS Enterprise Strategist Tom Soderstrom. Together they explore how modern data infrastructure can accommodate rapid technology changes while maintaining security and compliance. This essential discussion provides leaders with practical insights for data-driven business transformation, from federating data ownership to implementing strategic data platform modernization that adapts to evolving business needs.

In this episode of TAG Data Talk, Dr. Beverly Wright discusses with Aparajit Agarwal, Kao Corporation:What is analytics maturity as it relates to GenAI?Discussing relationship between a solid machine learning strategy and developing GenAI capabilities.Finding the applicable use cases for GenAI given maturity levels.Advice for getting GenAI implemented in your organization.Aparajit Agarwal, Enterprise Data and AI Architect at Kao CorporationFollow Aparajit Agarwal

Welcome back to season 5 of the Data Democratization Podcast - and our first-ever live studio recording.In this episode, Alexandra Ebert sits down with Faris Haddad, AWS' Global AI Technical Strategy Lead. Together, they delve into the transformative role of synthetic data in modern enterprises.  Faris shares his journey into the world of synthetic data, highlighting its evolution from a niche solution to a cornerstone of enterprise data strategy. He discusses the challenges organizations face with data silos, legacy systems, and privacy concerns, and how synthetic data offers a pathway to overcome these hurdles.  Whether you're grappling with data accessibility issues or exploring innovative ways to leverage your organization's data assets, this episode offers valuable perspectives on integrating synthetic data into your enterprise data strategy. 

Box CEO and co-founder Aaron Levie joins the GeekWire Podcast to talk about the company’s latest AI initiatives, including a new integration with Microsoft 365 Copilot and a suite of Box AI agents designed to help businesses analyze content, extract data, and generate insights. Levie shares how AI is changing the way he and his team work, explains why he believes 2025 is still “day one” for enterprise AI agents, and offers a realistic take on what AI can — and can’t — do today. He also reflects on Box’s evolution, the shifting role of content in the enterprise, and why he thinks AI is creating more work, not less. RELATED LINKS GeekWire: Box tightens Microsoft ties with new Copilot integration, builds out its own suite of AI agents Aaron Levie: How AI is changing my work as CEO at Box With GeekWire co-founder Todd BishopSee omnystudio.com/listener for privacy information.

The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

✨ Heads up! This episode features a demonstration of the SnapLogic UI and its AI Agent Creator towards the end. For the full visual experience, check out the video version on the Spotify app! ✨(Episode Summary)Tired of tangled data spread across multiple clouds, on-premise systems, and the edge? In this episode, MongoDB's Shane McAllister sits down with Peter Ngai, Principal Architect at SnapLogic, to explore the future of data integration and management in today's complex tech landscape.Dive into the challenges and solutions surrounding modern data architecture, including:Navigating the complexities of multi-cloud and hybrid cloud environments.The secrets to building flexible, resilient data ecosystems that avoid vendor lock-in.Strategies for seamless data integration and connecting disparate applications using low-code/no-code platforms like SnapLogic.Meeting critical data compliance, security, and sovereignty demands (think GDPR, HIPAA, etc.).How AI is revolutionizing data automation and providing faster access to insights (featuring SnapLogic's Agent Creator).The powerful synergy between SnapLogic and MongoDB, leveraging MongoDB both internally and for customer integrations.Real-world applications, from IoT data processing to simplifying enterprise workflows.Whether you're an IT leader, data engineer, business analyst, or simply curious about cloud strategy, iPaaS solutions, AI in business, or simplifying your data stack, Peter offers invaluable insights into making data connectivity a driver, not a barrier, for innovation.-Keywords: Data Integration, Multi-Cloud, Hybrid Cloud, Edge Computing, SnapLogic, MongoDB, AI, Artificial Intelligence, Data Automation, iPaaS, Low-Code, No-Code, Data Architecture, Data Management, Cloud Data, Enterprise Data, API Integration, Data Compliance, Data Sovereignty, Data Security, Business Automation, ETL, ELT, Tech Stack Simplification, Peter Ngai, Shane McAllister.

Episode OverviewEnterprise data isn't just about governance anymore — it's about growth, agility, and survival.Malcolm Hawker and Apurva Wadodkar of Autodesk dive into what it takes to build a data function that keeps pace with change, sharing hard-won lessons and forward-looking strategies along the way.Episode Links and ResourcesFollow Malcolm Hawker on LinkedInFollow Apurva Wadodkar on LinkedIn

What should you really be asking during your interview as a tech leader? And once you land the role, how do you manage expectations, reduce technical debt, and make meaningful impact fast?In this episode, Justin Nguyen, Technology Director of Enterprise Data & Analytics at Home Depot, shares hard-won insights from his recent leadership transitions. From assessing team maturity to setting realistic AI expectations, we unpack the tactical and strategic moves leaders need to thrive in the first 180 days of a new role.

Former acting Navy Secretary Tom Harker and retired Rear Adm. Christian “Boris” Becker who led the Naval Information Warfare Systems Command, now senior advisers with Pantheon Data, join Defense & Aerospace Report Editor Vago Muradian to discuss the commentary they co-wrote with Pantheon Data CEO Kris Kenefic, — “The Need for Decision-Making Speed: Leveraging Enterprise Data for National Security” — on how to speed acquisition of data and AI capabilities that can help commanders with everything from faster and better decision-making to accountability; impediments to change; how to accelerate the acquisition process; and keys to ensuring cybersecurity.

Capital One has long been recognized as a digital-first financial services company. Now, it's productizing its tech DNA into standalone B2B software. In this episode, Peter High speaks with Ravi Raghu, President of Capital One Software, about how internal cloud and data security capabilities led to the launch of an enterprise SaaS business—starting with offerings like Slingshot and Data Bolt.

What is Google Agentspace? Find out on this Cloud and Clear episode with SADA's Kelly Wright. Director of Workspace, and Veronica Raulin, Senior Director, Advisory! We're discussing how Google Agentspace uses AI and Google Search to simplify complex tasks and connect your data. Plus, get a Google Cloud Next 2025 preview and learn about SADA's Proof of Value. Join us for more content by liking, sharing, and subscribing!

What does it take to invent a foundational AI paradigm — and then build a company to bring it to the enterprise? In this episode of Founded & Funded, Madrona Partner Jon Turow sits down with Douwe Kiela, co-founder and CEO of Contextual AI and the co-inventor of RAG (Retrieval Augmented Generation). They dive into the origins of RAG, its misunderstood role in the enterprise, and how Contextual is redefining what production-grade AI systems can do. Douwe shares what most companies get wrong about RAG, why chunking shouldn't matter, how to think about hallucinations, and what founders need to know in the era of RAG agents. Transcript: https://madrona.com/rag-inventor-talks-agents-grounded-ai-and-enterprise-impact Chapters: (00:00) Introduction  (01:27) The Origin of RAG (04:00) Challenges and Innovations in RAG (09:49) Enterprise Adoption and Use Cases (20:46) Scaling and Innovations at Contextual AI (23:39) The Future of RAG Agents (24:43) Challenges in Enterprise Data  (26:34) Building a Research-Driven Company (27:55) The Intersection of Research and Product (32:10) Advice for Founders and AI Companies (38:14) Understanding and Addressing Hallucinations (40:50) Company Building is Harder Than You'd Think (42:00) The Importance of Evaluation in AI (44:14) Concluding Thoughts

This guide provides a step-by-step framework to assess vendors, align priorities, and make informed decisions about enterprise data and analytics tools. Published at: https://www.eckerson.com/articles/the-buyer-s-guide-to-selecting-the-right-enterprise-data-analytics-tool

I keep hearing data product, data strategy, and UX teams often struggle to quantify the value of their work. Whether it's as a team as a whole or on a specific data product initiative, the underlying problem is the same – your contribution is indirect, so it's harder to measure. Even worse, your stakeholders want to know if your work is creating an impact and value, but because you can't easily put numbers on it, valuation spirals into a messy problem.   The messy part of this valuation problem is what today's episode is all about—not math! Value is largely subjective, not objective, and I think this is partly why analytical teams may struggle with this. To improve at how you estimate the value of your data products, you need to leverage other skills—and stop approaching this as a math problem.   As a consulting product designer, estimating value when it's indirect is something that I've dealt with my entire career. It's not a skill learned overnight, and it's one you will need to keep developing over time—but the basic concepts are simple. I hope you'll find some value in applying these along with your other frameworks and tools.    Highlights/ Skip to   Value is subjective, not objective (5:01) Measurability does not necessarily mean valuable (6:36) Businesses are made up of humans. Most b2b stakeholders aren't spending their own money when making business decisions—what does that mean for your work? (9:30) Quantifying a data product's value starts with understanding what is worth measuring in the eye of the beholder(s)—not math calculations (13:44) The more difficult it is to show the value of your product (or team) in numbers, the lower that value is to the stakeholder—initially (16:46) By simply helping a stakeholder to think through how value should be calculated on a data product, you're likely already providing additional value (18:02) Focus on expressing estimated value via a range versus a single number (19:36) Measurement of anything requires that we can observe the phenomenon first—but many stakeholders won't be able to cite these phenomena without [your!] help (22:16) When you are measuring quantitative aspects of value, remember that measurement is not the same as accuracy (precision)—and the precision game can become a trap (25:37) How to measure anything—and why estimates often trump accuracy (31:19) Why you may need to steer the conversation away from ROI calculations in the short term (35:00)   Quotes from Today's Episode Even when you can easily assign a dollar value to the data product you're building, that does not necessarily reflect what your stakeholder actually feels about it—or your team's contribution. So, why do they keep asking you to quantify the value of your work? By actually understanding what a shareholder needs to observe for them to know progress has been made on their initiative or data product, you will be positioned to deliver results they actually care about. While most of the time, you should be able to show some obvious economic value in the work you're doing, you may be getting hounded about this because you're not meeting the often unstated qualitative goals. If you can surface the qualitative goals of your stakeholder, then the perception of the value of your team and its work goes up, and you'll spend less time trying to measure an indirect contribution in quant terms that only has a subjectively right answer. (6:50) The more difficult it is for you to show the monetary value of your data product (or team), the lower that value likely is to the stakeholder. This does not mean the value of your work is “low.” It means it's perceived as low because it cannot be easily quantified in a way that is observable to the person whose judgment matters. By understanding the personal motivations and interests of your stakeholders, you can begin to collaboratively figure out what the correct success metrics should be—and how they'd be measured. By just simply beginning to ask and uncover what they're trying to measure, you can start to increase your contributions' perceived value. (17:01) Think about expressing “indirect value” as a range, not a precise single value. It's much easier to refine your estimate (if necessary) once a range has been defined, and you only need to get precise enough for your stakeholder to make a decision with the information. How much time should you spend refining your measurement of the value? Potentially little to none—if the “better math” isn't going to change anyone's mind or decision.  Spending more time to measure a data product's value more accurately takes you away from doing actual product work—and if there isn't much obvious value to the work, maybe the work—not the measurement of the work—needs to change. (19:49) Smart leaders know that deriving a simple calculation of indirect contributions is complex—otherwise, the topic wouldn't keep coming up. There is a “why” behind why they're asking, and when you understand the “why,” you'll be better positioned to deliver the value they actually seek, using valuation measurements that are “just enough” in their precision. What do you think it says to a stakeholder if you're spending an inordinate amount of time simply trying to calculate and explain the value of your data product? (23:22) Many organizations for years have invested in things that don't always have a short term ROI.  They know that ROI takes time, and they can't really measure what it's worth along the way. Examples include investments in company culture, innovation, brand reputation, and many others. If you're constantly playing defense and having to justify your existence or methods by quantifying the financial value of your data products (or data product management team, or UX team, or any other indirect contributor/contribution), then either your work truly does lack value, or you haven't surfaced what the actual success metrics and outcomes are— in the eyes of the stakeholder. As such, the perceived value is “low” or opaque. They might be looking for a hard number to assign to it because they're not seeing any of the other forms of value that they care about that would indicate positive progress. It's easier to write [you] a large check  for a big, innovative, unproven initiative if your stakeholders know what you and your team can accomplish with a small check. (35:16)   Links Experiencing Data: Episode 80 with Doug Hubbard

In this episode we are looking at an area which impacts every business in the world. Unstructured data - that is, how we can start to squeeze insight from the piles of text, audio, video, and every other type of data that doesn't fit into a neat table.Carefully analysed, it can contain valuable insight, to be compared against other more traditional metrics such as sales figures, or economic results.Joining us to discuss is Gokul Sathiacama, VP of data storage for AI at Hewlett Packard Enterprise.This is Technology Now, a weekly show from Hewlett Packard Enterprise. Every week we look at a story that's been making headlines, take a look at the technology behind it, and explain why it matters to organizations and what we can learn from it. About this week's guest, Gokul Sathiacama: https://www.linkedin.com/in/gokuls/Sources cited in this week's episode:Statistics on global data generation: https://www.statista.com/statistics/871513/worldwide-data-created/Statistics on global IOT devices: https://paxtechnica.org/?page_id=738#:~:text=%E2%80%9COur%20IoT%20world%20is%20growing,billion%20by%202020.%E2%80%9D%20Intel.&text=Gartner.&text=Cisco.,-2011&text=%E2%80%9CGlobal%20M2M%20connections%20will%20increase,at%20the%20end%20of%202022.Global Web Index stats on smart devices: https://www.globalwebindex.net/

In this episode we are looking at an area which impacts every business in the world. Unstructured data - that is, how we can start to squeeze insight from the piles of text, audio, video, and every other type of data that doesn't fit into a neat table.Carefully analysed, it can contain valuable insight, to be compared against other more traditional metrics such as sales figures, or economic results.Joining us to discuss is Gokul Sathiacama, VP of data storage for AI at Hewlett Packard Enterprise.This is Technology Now, a weekly show from Hewlett Packard Enterprise. Every week we look at a story that's been making headlines, take a look at the technology behind it, and explain why it matters to organizations and what we can learn from it. About this week's guest, Gokul Sathiacama: https://www.linkedin.com/in/gokuls/Sources cited in this week's episode:Statistics on global data generation: https://www.statista.com/statistics/871513/worldwide-data-created/Statistics on global IOT devices: https://paxtechnica.org/?page_id=738#:~:text=%E2%80%9COur%20IoT%20world%20is%20growing,billion%20by%202020.%E2%80%9D%20Intel.&text=Gartner.&text=Cisco.,-2011&text=%E2%80%9CGlobal%20M2M%20connections%20will%20increase,at%20the%20end%20of%202022.Global Web Index stats on smart devices: https://www.globalwebindex.net/

Take a Network Break! This week we discuss Microsoft’s proposed deal to buy power from the Three Mile Island nuclear plant, new APs and switches from Juniper Networks, and T-Mobile landing a customer driven by the network slicing capabilities of 5G. China disbands a botnet, Nokia takes on data center switch giants with a new... Read more »

Take a Network Break! This week we discuss Microsoft’s proposed deal to buy power from the Three Mile Island nuclear plant, new APs and switches from Juniper Networks, and T-Mobile landing a customer driven by the network slicing capabilities of 5G. China disbands a botnet, Nokia takes on data center switch giants with a new... Read more »

Rohit Choudhary's journey from a curious teenager in India to a seasoned entrepreneur and innovator demonstrates the transformative power of technology and entrepreneurial spirit. His latest venture, Acceldata, has attracted funding from top-tier investors like Insight Partners, March Capital, Industry Ventures, Lightspeed, and Sorenson Ventures.