Podcasts about data strategy

This episode was brought to you by RayonRetail design teams use Rayon to create store layouts, documentation, standards, and presentations in one collaborative platform. By combining design tools and AI in a single workspace, Rayon helps teams move faster from concept to execution while maintaining consistency across locations. If you're looking to design better retail spaces and streamline your workflow, visit rayon.design and sign up for free todayWhen Ricardo Larroude first joined OFFBounds, Larroudé was producing just 300 pairs of shoes a day. Today, the company manufactures more than 2,000 pairs daily, employs 700 people, and has become one of the fastest-growing vertically integrated footwear brands in the market. In this conversation, Ricardo shares how tariffs, rapid growth, and operational complexity pushed him to rethink how he runs the business and why he decided to personally dive into AI instead of delegating it to his technology team.The result was more than automation. Ricardo built an AI-powered operating system that connects data, teams, and decision-making across the company. From improving website conversion rates to eliminating process bottlenecks and redefining how leaders should approach technology, this episode explores why the future belongs to executives who are willing to learn, experiment, and build. If you're a retail, commerce, or business leader trying to understand what AI actually means beyond the headlines, this conversation offers a practical look at what happens when a CEO gets hands-on.

Join Angel Horvat, Founder and CEO of AI Readi, for a candid evaluation of why the corporate rush into generative AI is grinding to an unexpected halt. Despite massive infrastructure investments, the enterprise journey is hitting a hard wall: while 88% of organizations have initiated AI pilots, a staggering 94% remain permanently trapped in pilot purgatory. Drawing on his years leading AI and data strategy at Nike (EMEA) and Gartner, Angel reveals that these failures are almost never a failure of the tech—they are structural failures of the organization itself. In this episode, we discover how to bridge the gap between initial demo and scaled business value.

You've invested in the dashboards. You've declared data a top priority. So why does transformation still feel out of reach? In this episode, Brandon Laws sits down with Dr. Sebastian Wernicke, author of Data Inspired: Building an Organizational Culture of Inquiry for Lasting Transformation, to unpack one of business's most frustrating paradoxes: companies that succeed at data... and still don't change. Sebastian challenges the conventional wisdom around data-driven organizations, reveals why human psychology is working against your data strategy, and introduces a more powerful mindset: becoming data inspired. From Goodhart's Law to Netflix's bold decision-making model, this conversation is loaded with ideas that will fundamentally change how you think about data, leadership, and organizational transformation. Don't miss it. Key Timestamps [00:00] — Welcome & Introduction to Data Inspired [00:39] — The bold opening argument: data initiatives don't fail; they succeed at keeping organizations the same. Sebastian unpacks the difference between getting modest value from data and achieving true transformation, and why only ~10% of companies ever get there. [07:38] — Are organizations paralyzed by too much data? Sebastian explains why collecting more data is often a way of avoiding the harder, more courageous work of challenging your own assumptions. [09:39] — What "data-driven" actually means in practice and why it's harder than it sounds. Sebastian introduces the "data deficit theory" and draws on 50 years of psychological research showing that data often hardens our existing beliefs rather than changing them. [13:45] — The Charles Barkley moment: what a legendary NBA star's skepticism about data analysts gets right and wrong about using data in sports and business. [16:03] — How data is collected and used in modern organizations, and why the real challenge isn't gathering data; it's organizing it. (Yes, there's a "data swamp" warning here.) [18:00] — Why the classic 8-step decision-making model is a myth. Sebastian explains what monkey brain research and animal herds reveal about how decisions are actually made and what that means for how you introduce data into the process. [22:36] — Goodhart's Law and the GE cautionary tale: the dangerous difference between steering metrics and success metrics, and what happens when leaders confuse the two. [25:38] — The decision-making spectrum: from fully automated machine-learning decisions to pure gut instinct and how Netflix found the sweet spot between data and human judgment. [30:17] — AI vs. machine learning: why we're wired to trust the type of automated decision-making that's actually less reliable and what that means for your organization right now. [33:34] — Data fatigue is real. Sebastian introduces two archetypes, the Dashboard Director and the Data Diver, and explains why you need both to build a truly innovative organization. [36:31] — A peek into Part 5: The Toolbox, with practical checklists, workshop formats, and tried-and-tested methods developed over 20 years of real-world data projects. [39:03] — Closing wisdom: why copying what successful companies did is a trap, and what it really takes to lead transformative change with data, including the courage to slow down before you speed up. A QUICK GLIMPSE INTO OUR PODCAST Podcast: Transform Your Workplace, sponsored by Xenium HR Host: Brandon Laws In Brandon's own words: "The Transform Your Workplace podcast is your go-to source for the latest workplace trends, big ideas, and time-tested methods straight from the mouths of industry experts and respected thought-leaders." About Xenium HR Xenium HR is on a mission to transform workplaces by providing expert outsourced HR and payroll services for small and medium-sized businesses. With a people-first approach, Xenium helps organizations create thriving work environments where employees feel valued and supported. From navigating compliance to enhancing workplace culture, Xenium offers tailored solutions that empower growth and simplify HR. Whether managing employee relations, payroll processing, or implementing impactful training programs, Xenium is the trusted partner businesses rely on to elevate their workplace experience. Discover how Xenium can transform your workplace: Learn more Connect with Brandon Laws: LinkedIn | Instagram | About Connect with Xenium HR: Website | LinkedIn | Facebook | Twitter | Instagram | YouTube

Wie verändert KI gerade die tägliche Arbeit von Unternehmen? In dieser Folge sprechen Jonas Rashedi und Tim Wiegels über Claude, ChatGPT, KI-Agenten und die nächste Evolutionsstufe von AI-Workflows. Ein zentrales Thema: Warum Content und Daten plötzlich zu einem echten Wettbewerbsvorteil werden. Wer eigene Inhalte, Prozesse und Strukturen sauber aufgebaut hat, kann KI heute deutlich effektiver nutzen als andere. Gleichzeitig diskutieren die beiden, warum viele Unternehmen KI immer noch nur als Chatbot verstehen – statt als Infrastrukturthema. Darüber hinaus geht es um Enterprise AI, neue Plattformansätze von Snowflake und Databricks sowie die Frage, wie Unternehmen mit der extremen Geschwindigkeit technologischer Veränderungen umgehen können. MY DATA IS BETTER THAN YOURS ist ein Projekt von BETTER THAN YOURS, der Marke für richtig gute Podcasts.

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the critical definition and requirements for navigating Enterprise AI. You’ll learn how to distinguish between consumer-grade tools and the strict standards required in regulated industries. You’ll discover the twenty essential pillars for building a secure and compliant AI strategy for your organization. You’ll understand why rigorous vendor scrutiny matters as much for software as it does for human talent. You’ll gain clarity on the governance frameworks necessary to prevent data leaks and legal vulnerabilities in your enterprise. 00:00 – Introduction 03:15 – Defining Enterprise AI vs. SMB AI 07:45 – The role of Microsoft Copilot in regulated environments 12:20 – The 20 components of Enterprise AI readiness 18:10 – Challenges in organizational adoption and change management 22:30 – Security and data privacy as the foundation 27:00 – Call to action Watch this episode to master the complex landscape of regulated AI and safeguard your company’s future. Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/inearinsights/tipodcast-enterprise-ai-101.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! [podcastsponsor] Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher S. Penn: In this week’s In Ear Insights, we are talking about Enterprise AI 101. I am in the midst of a series in the Trust Insights newsletter, which you can get at TrustInsights.ai/newsletter. Part one was last week on seven different aspects of enterprise AI. But Katie, you said it would probably be helpful to level set what enterprise AI is and how it differs from SMB AI, mid-market AI, consumer AI, and so on. Katie Robbert: It is interesting because I feel like every time we jump on to record a podcast, there is a whole new set of vocabulary that I need to get caught up with. We need to make sure that everyone else knows what we are talking about because there is nothing worse than listening to a podcast or reading an article and having no idea what the author is talking about because they are introducing a concept but not really explaining it. I wanted to take this episode to talk about what enterprise AI is. Since you and I have not defined it, I am going to take my best guess at what enterprise AI is using some logic and deduction. I could be wrong, and that is why I think it is worth covering. From my perspective, if I had to put a definition to it, I am assuming enterprise AI is the type of AI implementation that occurs at an enterprise-size company. That sounds overly simplistic, but the bigger the organization, the more red tape, the more politics, the more departments, the more stakeholders, and the more governance there is. There are a lot more complications versus a small business like we are, where we can just decide one day, “Hey, I am going to start using this tool.” There are no real hurdles to go through. Then you have those mid-sized companies where you start to introduce some of those hurdles. You might need to work with your IT team to make sure that everything is in compliance. You might need to make sure that you have a place to host these new pieces of software, and that is not something that the marketing team is necessarily responsible for. Then you get to the enterprise-size companies where everything is completely siloed. Even in the best enterprise-sized companies, you are going to run into these silos. Because no one person is responsible for everything, you typically have multiple CEOs. Depending on what part of the country you are in, you might have a board for every different division of the company. If you are a Procter & Gamble and you have hundreds of product lines underneath, each of those is their own individual business. Each of those businesses are not necessarily talking to each other or sharing resources. That is my logical guess at what enterprise AI is. Christopher S. Penn: That is what I started with until I started doing the research into it. I realized that is not what it is. The generally accepted definition is AI within any commercially regulated entity. I realized as I was going through the research that commercially regulated means you have external regulation imposed on the company. It might be a 50-person company, but if they work in HIPAA or FINRA, they have to behave in highly regulated ways. Whether you are publicly traded or, for example, colleges that have to adhere to FFIEC rules and FERPA rules, enterprise AI is about operating AI—whether classical or generative—in a commercially regulated environment where you have externally mandated requirements that you must meet. Your definition for small business stuff makes total sense in that environment because Trust Insights is not a regulated company. However, when we work with our healthcare clients, we have to behave as though we are an enterprise company because we have to conform to their requirements. Katie Robbert: I am glad we are talking about this because the terminology is confusing; when you think of an enterprise company, you are not thinking of a commercially regulated company. I have to wonder why it is not called commercially regulated AI versus non-commercially regulated AI. It is a mouthful and a little bit harder to remember, but it is more descriptive and more accurate. I think like me, a lot of people are going to get confused about what enterprise AI actually is. Christopher S. Penn: A lot of this is because our background is in marketing, so we use the term enterprise to just mean a big company. If we want to market to enterprise companies, we are not marketing to a 50-person firm; we are marketing to a 50,000-person firm. In a lot of CRM software, the dividing line is typically 10,000 employees or 100 million in revenue. This is especially relevant because you see a lot of AI companies like Anthropic and OpenAI in a fight with Microsoft to try and gain a foothold into those enterprises. Microsoft, with their Copilot offering, has dominance by the very fact that their legacy Office 365 stuff is approved in those regulated environments. Katie Robbert: It is ironic because we spent so much time admittedly dismissing Microsoft’s Copilot as the less than version of generative AI, and now Microsoft is getting the last laugh on everyone. They are saying, “You have to use me because I have already been approved by IT and governance, and good luck.” You are stuck with whatever I decide to give you. If I were Microsoft, I would be petty and say, “You guys spent way too much time dismissing me and calling me inferior, so too bad.” Christopher S. Penn: A lot of that, as we have talked about many times on stage, is that the reason Copilot has fewer capabilities than other systems is specifically because of the regulated environment. It is trivial for Google to foist something on consumers and say, “Now we are going to read all your Gmail.” That does not fly in a regulated industry. Katie Robbert: That understanding is really helpful to the people who are saddled with Microsoft Copilot because we hear complaints about why they cannot use other shiny objects. If you are in a 50,000-person company and you weren’t there when the regulatory standards were decided upon, you are sitting there wondering why you cannot use Gemini to generate ad headlines. Then you do it on the side and get in trouble because there is no clear documentation saying why you have to use Copilot and nothing else. What we are hearing is that employees in companies required to use Microsoft Copilot are using other models on the side. That information is still getting filtered into the organization, and it is a huge governance problem. Christopher S. Penn: Completely. In enterprise AI, there are 20 different components to being ready. I derived this from the US federal government's NIST AI regulations and the EU AI Act, which is the gold standard. Katie Robbert: I want to see if you can get all 20. Christopher S. Penn: One, Strategy and Operating Model; two, Governance Policy and the AI Council; three, Legal, Regulatory, and Compliance. Katie Robbert: Are you reading this off a screen? Christopher S. Penn: I am 100% reading this off the Trust Insights Enterprise AI Landscape Field Handbook. Katie Robbert: Fine, continue. Christopher S. Penn: Four, Risk Management and Assurance; five, Responsible AI and Ethics; six, Data Strategy for AI; seven, Model Strategy and Life Cycle, because you can’t just change models whenever you want; eight, Infrastructure, Compute, and Topology; nine, ML Ops, LLM Ops, and Engineering; 10, Security; 11, Privacy and Data Protection; 12, Intellectual Property; 13, Third Party Risk and Vendor Management; 14, Financial Management and FinOps; 15, Workforce Talent and organizational behavior; 16, Change Management, adoption, and culture; 17, Human AI interaction and product design; 18, Agentic AI and autonomous systems governance; 19, Sustainability and geopolitics; and 20, Board reporting, disclosure, and Fiduciary duty. Katie Robbert: I just heard a whole lot of new job opportunities listed. So, if someone were working in a regulated industry like pharma, these are the 20 things they would need to be aware of before evaluating generative AI. It is interesting that organizational behavior and change management are part of it. You would think the regulations would be more technical versus human, but I am surprised that is part of it. Christopher S. Penn: It makes sense because in order for any AI to succeed in an enterprise with 50,000 or 300,000 employees, you have to prioritize change management. Organizational behavior cannot be an add-on; they have to be baked into what you do from the beginning, otherwise your initiative is going nowhere. Katie Robbert: I don’t disagree, but the typical way that works in a large organization is top-down. They make a decision, and you walk in the next day to find it has automatically updated your computer settings. Now you can no longer use a web browser search; you have to use Microsoft Copilot. That is their version of change management, but it is really just a dictatorship from above. I am interested in future episodes to explore what that should look like in a regulatory environment. Christopher S. Penn: We have known for two years that adoption is the hardest part. Deployment is easy compared to adoption. You can put Copilot on someone's desk, but they may not use it even if you tell them they have to. It comes back to how you get them to see the benefits. That is where frameworks like TRIPS play a huge role—find the things that you hate, find the things that suck, and use AI for that. Get that one thing off your plate. Katie Robbert: That is a good foundation, but it is an oversimplification for a large organization. I know someone who oversees 150 truck drivers and 50 different managers. The layers are so deep. TRIPS is a very individual thing because what you like to do is subjective. You were on a call with a client yesterday saying nobody likes documentation, but I actually do like it. My scoring would look different than yours. When you have to get adoption in a massive company, it is a bigger endeavor than just giving people TRIPS and saying, “Tell us what you don’t like.” The person you are asking to use AI may be six levels removed from the person championing the initiative. Christopher S. Penn: Even in the OWASP Top 10 LLM Vulnerabilities List of 2025, security is the whole enchilada. Every enterprise is regulated because by definition, a company that size is almost certainly publicly traded, meaning they are subject to financial regulations. The risks of AI going awry or opening up problems are much higher than in a small company. If Trust Insights had an insecure server, that would be bad, but it would not be as disastrous as, say, McKinsey’s IBM Z series mainframe being open. Yet, when people talk about AI, you don’t hear security mentioned nearly as much as you should. Katie Robbert: It is true. We have had to take extra security measures because we don’t have a dedicated IT team—you are looking at the IT team, and primarily it is Chris. We don’t have any wiggle room to set things up haphazardly. We have to do it right from the start. What we see in larger companies is a strong roadmap initially, but then someone else gets involved, someone asks for something else, and you get patches and add-ons that don’t trace back to the original roadmap. By the end, you are wondering what the original goal was. The bigger the organization gets, the harder it is to maintain control. It becomes a snowball effect. Christopher S. Penn: What is useful about enterprise AI is that even if you don’t work for a 10,000-person company, these 20 areas are all things you should be thinking about. Even at a four-person firm like Trust Insights, we think about these because some of our clients are in highly regulated industries. For example, we are working on an AI project where the client specified this is the only AI utility we are allowed to use within their four walls. Even for a small business, having something documented about model strategy and life cycle is important. As of the day we are recording this, Google Gemini 3.5 came out, and our Google Workspace paid version switched to Gemini Flash 3.5. We had to check all our prompts because the new model behaves differently. Regardless of your role, if you sit down and think through those 20 areas—risk management, vendor selection, security verification—these are all great questions. Katie Robbert: There is a good starting place for this. You can find our downloads at TrustInsights.ai/StrategicToolkit. There is also a free version at TrustInsights.ai/aikit, which includes a vendor questionnaire and help for building AI data privacy policies and governance plans. We have already templated these things out. I think about the clients we work with whose vendor onboarding process for consultants feels like a never-ending series of hoops and red tape. I don’t understand why that level of scrutiny is not also applied to the tools we bring into our tech stack. We are renting space in those tools and freely giving them our data. Those companies now have our data and will use it for their own benefit. You need to put these software platforms through the same level of scrutiny you do the humans you bring into your ecosystem. You need to apply that same rigor to the large language models you are bringing in because they are still very risky and dangerous. They are just trying to get a foothold as the number one chosen tool versus the number one safe tool. Christopher S. Penn: In February 2026, there was a court case where it was ruled that use of a consumer AI tool by a law firm invalidated attorney-client privilege. The judge ruled that this is no longer privileged information. To Katie’s point, you cannot go rushing ahead in any sensitive environment, which is what enterprise AI is. You have to be doing your homework. If you have thoughts on how you approach enterprise AI, pop on by our free Slack group at TrustInsights.ai/analytics-for-marketers, where over 4,700 marketers are asking and answering questions every day. Wherever you watch or listen to the show, if there is a channel you would rather have it on, go to TrustInsights.ai/tipodcast. Thanks for tuning in; we will talk to you on the next one. Katie Robbert: Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach. Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning to drive measurable marketing ROI. Our services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies. Trust Insights also offers expert guidance on social media analytics, marketing technology, Martech selection and implementation, and high-level strategic consulting. Encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, DALL-E, Midjourney, Stable Diffusion, and Meta Llama, Trust Insights provides fractional team members such as a CMO or data scientists to augment existing teams. Beyond client work, Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In-Ear Insights podcast, the Inbox Insights newsletter, the So What? livestream webinars, and keynote speaking. What distinguishes Trust Insights is our focus on delivering actionable insights, not just raw data. We are adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet we excel at explaining complex concepts clearly through compelling narratives and data storytelling. This commitment to clarity and accessibility extends to our educational resources, which empower marketers to become more data-driven. Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you are a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information. Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.

On our second installment of How to Raise Your Agent, we welcome Michelle Finneran Dennedy back into the SmarterMarkets™ studio. Michelle is Chief Data Strategy Officer at Abaxx Technologies. David Greely sits down with Michelle to discuss what business leaders and managers will need to do to adapt to a world where their teams include both people and agents – and how they should be thinking about integrating agents into their teams, including how to raise and govern them.

Learn what happens when the executive accountable for data strategy is also the executive accountable for the business results that depend on it. Saugata Saha, President of S&P Global Market Intelligence and Chief Enterprise Data Officer at S&P Global, shares how he manages one of the world's largest financial data estates while driving business outcomes across public and private markets. He breaks down the four pillars of S&P Global's data strategy, the federated organizational model that connects data teams to business value, and why capturing ROI from AI requires deliberate workflow transformation. Key Moments Why Data Strategy Must Follow Business Strategy (04:57): Saugata challenges the idea that data and business strategy can run in parallel. Market trends, customer pain points, and existing capabilities must come first. Building an AI-Ready Financial Data Estate (15:10): Scale alone does not create intelligence. Saugata explains why semantic layers and graph databases are the hard work behind connected financial data. How AI Compresses Post-Acquisition Data Integration (18:29): Manual reconciliation of millions of records is no longer the only path. Discover how AI entity matching accelerated post-acquisition integration. The Federated Model That Connects Data to Value (22:49): Most large organizations either over-centralize data teams or leave them too embedded to scale. Saugata outlines the federated model that actually bridges both. Rethinking AI Productivity: From Marginal to Transformative (28:29): Most AI programs stop at training and tooling. Saugata explains why deliberately redesigning workflows is the missing step between AI investment and real ROI. Key Quotes “Data strategy and business strategy have to be very tightly connected. And if they're not, that's when value capture does not happen. In fact, I would go so far as to say data strategy actually follows from business strategy.” - Saugata Saha “Stop treating data as an afterthought or byproduct, but start thinking about data as a key ingredient for value creation and competitive advantage.” - Saugata Saha “We don't want everybody to become 10% more productive, because that's a little squishy. We want 10% of the people to become a hundred percent more productive so they can do other things.” - Saugata Saha “If a company can really use data at scale for better decision making, better client service, [and] better outcomes, that creates a lasting edge over the competition.” - Saugata Saha Mentions S&P Global Agrees to Acquire With Intelligence from Motive Partners for $1.8 Billion, Establishing Its Leadership in Private Markets Intelligence The Data & AI Chief: Why a Federated Data Team is Crucial for Business Value, with Dow Private Companies Wait Too Long to Go Public The Lex Fridman Podcast Guest Bios  Saugata serves as President of S&P Global Market Intelligence, leading the division's efforts to deliver essential insights and intelligence to clients worldwide. He is also S&P Global's Chief Enterprise Data Officer, responsible for driving innovation and excellence in the company's enterprise data strategy. Saugata is a member of S&P Global's Executive Leadership Team, contributing to the strategic direction and growth of the organization. Before joining S&P Global, Saugata was a consultant at McKinsey & Company's New York office, where he advised clients on strategy, mergers and acquisitions, corporate finance, and operational improvements across various industries, with a strong focus on financial services. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Snowflake is the AI Data Cloud behind some of the world's largest enterprises — $4.68 billion in annual revenue, 29% year-over-year growth, and over 760 Forbes Global 2000 companies as customers. Baris Gultekin, VP of AI at Snowflake, leads the product efforts that sit at the center of how those enterprises actually operationalize AI. Before Snowflake, he co-founded Google Assistant and scaled it from 10 million to 500 million monthly users.What you'll learn:Why our data isn't clean enough is a delay tactic — and the scoped approach to move past itWhat the semantic layer is and how it lets AI answer business questions accurately, not just fluentlyWhy running AI next to data (instead of sending data to models) makes governance dramatically easierHow Snowflake deployed AI internally: a CEO-level non-optional mandate combined with bottom-up access to their own Cortex coding agentWhy context — not just data — is what agents need to operate reliably at enterprise scaleKey takeaways:Start with one scoped use case, build the semantic model around it, layer governance — don't wait for perfect dataContext is a shared reality for agents: unified data + business semantics + codified workflowsAI adoption compounds when leadership sets a hard mandate and simultaneously gives everyone a tool to experiment withCredits:Host: Carlos Gonzalez de VillaumbrosiaGuest: Baris GultekinSocial Links:Find out more about Product School hereFollow our Podcast on TikTok hereFollow Product School on LinkedIn here

Join KJ Burke, as he dives into the key highlights from NVIDIA's GTC 2026 conference. Discover the latest in AI advancements, robotics, space computing and enterprise strategies shaping the future of technology. To learn more, visit cdw.ca Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Gustavo Cardona is VP of Technology at Levain Bakery, where he leads IT infrastructure and data strategy to bridge innovative technology with the operational reality of a craft-focused, omnichannel retail business.  He's responsible for building Levain's enterprise systems, including integrating Toast, NetSuite, and CrunchTime into a unified data platform, migrating the organization to real-time reporting with Tableau, and developing a multi-year technology roadmap. Gustavo's approach focuses on what he calls the "adoption challenge"—ensuring technology actually gets used rather than just implemented.  He's currently leading Levain's Emerging Tech Strategy with a focus on operational efficiency, product innovation, and decision intelligence through responsible AI governance.  In This Conversation We Discuss: [00:00] Intro [02:35] Networking with peers at conferences  [06:10] Sponsor: Klaviyo [08:17] Leveraging past experience in a new role  [11:36] Sponsor: Intelligems [13:36] Cultivating adaptability and resourcefulness [15:54] Identifying bottlenecks in daily operations  [19:29] Sponsor: Electric Eye [20:41] Identifying team pain points on the ground [23:17] Callouts  [23:27] Keeping a human in the loop for AI tools  [27:51] Making your product the hero instead of tech  Resources: Subscribe to Honest Ecommerce on Youtube New York City's Most Famous Cookies levainbakery.com/    Follow Gustavo Cardona linkedin.com/in/cardonagustavo Migrate and grow more klaviyo.com/honest Book a demo today at intelligems.io/ Schedule an intro call with one of our expertselectriceye.io/connect If you're enjoying the show, we'd love it if you left Honest Ecommerce a review on Apple Podcasts. It makes a huge impact on the success of the podcast, and we love reading every one of your reviews!

Markus Unger ist Inhaber von vit:bikes – dem Münchner Fahrradgeschäfts-Verbund mit über 30 Shops und 180.000 YouTube-Abonnenten. Wir sprechen darüber, wie er den Sprung vom Aushilfe-Job zu einer AI-nativen Organisation geschafft hat – und warum der Mittelstand schneller AI-nativ werden kann als jeder Konzern. Eine Folge über Speichengregale, Säge schärfen, Cost per Lead, das vit:bikes-Cockpit und die These, dass der CEO Data und AI selbst treiben muss. Key Takeaways: → Warum 50 Prozent der Fahrradhändler in 3 Jahren nicht mehr existieren werden → Wie ein Speichengregal der Wendepunkt für 30 Shops wurde → Warum vit:bikes mit YouTube 10-12 Millionen Nutzer pro Jahr erreicht → Wieso eine selbstgebaute App 100.000-Euro-Angebote von Software-Firmen ersetzt → Warum der CEO Data und AI selbst treiben muss – und nicht die IT Über den Gast: Markus Unger ist Inhaber und Geschäftsführer von vit:bikes – einem der größten Fahrradgeschäfts-Verbünde im deutschsprachigen Raum mit über 30 Shops und 180.000 YouTube-Abonnenten. Bis 2030 will er auf 200 Shops skalieren – befeuert durch eine selbstentwickelte Software, das vit:bikes-Cockpit.

Matt Ober, Managing Partner at Social Leverage, joins Jake & Gino to discuss venture capital, fintech investing, data-driven investing strategies, AI, entrepreneurship, and the future of finance. Previously Chief Data Scientist at Third Point and Head of Data Strategy at WorldQuant, Matt shares valuable insights into startup investing, identifying market opportunities, and how technology is transforming the financial world. In this episode: Venture capital & fintech trends Data science in investing Startup growth strategies AI in finance Entrepreneurship & scaling businesses Long-term investing insights Looking to grow your real estate investing business with proven systems and education?  Visit Wheelbarrowprofits.com and start building long-term wealth today. timestamps 0:05 - Introduction by Jake Stenziano 0:13 - Gino responds to Jake 0:18 - Jake's comment on gratitude 0:21 - Gino talks about yesterday's conversation 0:49 - Gino acknowledges Jake's support 1:07 - Discussion about the weather 1:23 - Introduction of guest Matt Ober 1:52 - Matt Ober's introduction 2:01 - Matt shares his career journey 2:30 - Matt talks about his hedge fund experience 2:58 - Discussion on venture firm building 3:28 - Matt talks about his partners 3:37 - Matt discusses the hedge fund space 4:05 - Jake comments on the hedge fund space 4:31 - Matt talks about his current company 5:11 - Discussion on investment thesis 5:30 - Matt explains investment focus 6:29 - Matt talks about investing in people 7:06 - Discussion on adversity and entrepreneurship 7:39 - Jake asks about investing in trust funds 8:28 - Matt discusses work atmosphere 9:05 - Discussion on investment backgrounds 9:35 - Matt talks about global team experience 10:24 - Discussion on competition and relationships 11:00 - Discussion on wealth management 12:16 - Discussion on gambling and prediction markets 13:28 - Discussion on prediction markets as media 14:05 - Discussion on tax loss harvesting 15:17 - Discussion on investment strategies 16:02 - Discussion on borrowing against stock portfolios 17:10 - Discussion on interest rates and loans 18:04 - Discussion on democratizing financial tools 19:24 - Discussion on data and AI 20:55 - Discussion on company adaptation to AI 22:06 - Discussion on layoffs and efficiency 23:26 - Discussion on AI and job skills 24:09 - Discussion on investment lifecycle 25:13 - Discussion on venture scale 26:24 - Discussion on raising capital 27:45 - Discussion on investment success rates 29:10 - Discussion on investment distribution 30:18 - Discussion on timing and product success 31:14 - Discussion on founding teams 32:09 - Discussion on founder challenges 33:25 - Discussion on business similarities 34:25 - Discussion on AI and creativity 35:24 - Discussion on creativity and skills 36:27 - Discussion on AI usage 37:49 - Discussion on sales and networking 38:26 - Discussion on commercial real estate 39:16 - Discussion on loan processes 40:38 - Discussion on real estate debt space 41:06 - Discussion on mortgage processes 42:32 - Discussion on financial planning 43:00 - Discussion on 401k transfers 43:59 - Matt's bold prediction 44:42 - Closing remarks We're here to help create real estate entrepreneurs... About Jake & Gino: Jake & Gino are multifamily investors, operators, and owners who have created a vertically integrated real estate company. They control over $350M in assets under management. Connect with Jake & Gino here --> https://jakeandgino.com. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

"Architects might start feeling useless."That's Dimitrie Stefanescu, founder of Speckle, on this week's Bricks & Bytes — on why GCs moving upstream into design is triggering an existential crisis across AEC.Joined by Jit Kee Chin, CTO of Suffolk Construction and co-founder of Suffolk Technologies, who just led Suffolk's investment into Speckle.Tune in to find out about:✅ Why 3D data is the missing infrastructure layer for AI in construction✅ The real reason design-build is eating traditional architecture✅ The most insane thing a customer has built on top of Speckle (yes, you'll want to hear this one)✅ What actually triggered a $B GC to bet on an open-source startupWatch now on YouTube and Spotify!#aec #construction #constructiontech #bricksandbytes #bricksbytes #ai #vc

Tom Hinkle, senior leader in data and analytics at TIAA, has  built his career at the intersection of finance, technology, and strategy. He also has a budding YouTube channel (Tom's Data World, which includes instructional videos on Excel, SQL, and AI). He's worked extensively with Excel (is a Microsoft MVP) and SQL, as well as Tableau and Power BI, and has worked with major financial institutions, including Bank of America, Wells Fargo, and TIAA.  "Nobody really gets excited about spending money on data governance. They get excited about machine learning, about AI, about data storytelling — everybody wants seminars on that. But data governance? At the end of the day, you're coming up with something you thought was right anyway, and then you're asking someone to spend more money just to make sure it's right." Why data governance is the foundation of AI  Trust but verify challenges The two sides of data governance: master data management vs. data quality and controls How FP&A teams should think about their role as downstream consumers of data Why domain expertise is still non-negotiable in the age of vibe coding Tom's Minesweeper-in-Excel story, and what it taught him about prompting

Tune in to hear everything from why data projects are chronically underestimated at the exec level, to what a proper data strategy actually looks like when you strip away the buzzwords. Mike gets practical on data migrations, data cleanup, and the hidden gotchas that trip teams up when they start handing control to agentic AI. Before finding his way into Salesforce consulting, Mike Dickson cycled solo across Canada, studied mechanical engineering, and cut his teeth on some of the most data-rich implementations in the nonprofit space. That analytical, problem-solving mindset shapes everything he has to say about why so many organisations are sleepwalking into their AI ambitions without the data foundation to back them up.Mike and Jack close things out with a thread about what commercial organisations can learn from the nonprofit world when it comes to relationships, relevance, and not being noisy.00:01 Intro & Meet Mike Dickson00:13 Why Data Is So Important in Salesforce01:47 Mike's Background: Mechanical Engineering & Cycling Across Canada05:12 Lessons from the Road That Still Apply Today07:53 10 Years in the Ecosystem: The Rise of Data08:17 Starting in the Nonprofit Space & Why Nonprofits Were Data-Rich Early Adopters12:25 Data Migrations: Why They're Harder Than Anyone Thinks15:58 Is the Complexity of Data Projects Misunderstood at the Exec Level?19:01 What a Solid Data Strategy Actually Looks Like22:08 Start With the Business Objective, Not the Data23:13 How to Approach a Data Cleanup Project26:49 The Hidden Gotcha: Records That Can't Be Saved Anymore27:04 Don't Boil the Ocean — Chunk It Up30:14 Data Migration Tips & How to Avoid Disaster33:22 The Right Tooling for the Job35:36 Data Doesn't Sit in a Vacuum36:05 What Nonprofits & Commercial Orgs Can Learn From Each Other40:15 Where to Find Mike & Speediful

KI-Hype trifft Realität: 750.000 Unternehmen in NRW – aber nur die wenigsten nutzen KI strategisch. Dr. Christian Temath, Geschäftsführer von KI.NRW, erklärt, warum die Gewinner heute schon aufgestellt sind, welche Rolle Kultur und Commitment spielen und warum „be bold" wichtiger ist als jeder ROI-Case. Key Takeaways: → Warum die meisten Unternehmen KI falsch herum angehen – und wie du es besser machst → Placebo-KI vs. echte Transformation: Der Unterschied zwischen Copilot-Lizenz und strategischem Mehrwert → Der Miele-Fall: Wie ein Backofen mit Kamera gegen Deckungsbeitrag gestartet und zum Datenschatz wurde → KI Act und Regulierung: Warum selbst Juristen uneins sind – und wie du trotzdem ins Tun kommst → Was der deutsche Mittelstand dem Silicon Valley wirklich entgegensetzen kann Über den Gast: Dr. Christian Temath ist Geschäftsführer der Kompetenzplattform KI.NRW am Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS in Sankt Augustin. Vor seiner Zeit bei KI.NRW war er acht Jahre in der Strategieberatung bei McKinsey und hat anschließend bei HRS tief in den Datenthemen eines global agierenden Tech-Unternehmens gearbeitet. Heute begleitet er das KI-Ökosystem in NRW – von Startups bis zum Mittelstand. MY DATA IS BETTER THAN YOURS ist ein Projekt von BETTER THAN YOURS, der Marke für richtig gute Podcasts. Du möchtest gezielt Werbung im Podcast MY DATA IS BETTER THAN YOURS schalten? Zum Kontaktformular: https://2frg6t.share-eu1.hsforms.com/2ugV0DR-wTX-mVZrX6BWtxg Zum Linkedin Profil von Christian: https://www.linkedin.com/in/christiantemath/ Zur Homepage von KI.NRW: https://www.ki.nrw/ Zum Linkedin Profil von Jonas: https://www.linkedin.com/in/jonasrashedi/ Jonas' Bücher: https://www.amazon.de/stores/Jonas-Rashedi/author/B09FKCFYBM Alle Folgen: https://my-data-is-better-than-yours.podigee.io/

Government contracting consultants who struggle to sign new clients are often missing one thing: proof. In this episode of the Federal Help Center Podcast, Eric Coffey breaks down the exact presentation strategy he uses to walk small businesses through the federal market data they never knew existed — and turn skeptics into signed clients. In this episode you'll learn: How to build a market data pitch using NAICS codes and SAM.gov spending reports — Eric walks through a real Arizona-based precast concrete company and reveals how $222 million in missed federal contracts closed the deal before terms were even discussed. The "missed opportunity" slide that sells for you — Learn how to show prospects three years of federal spending in their industry and geography so they feel the urgency without a hard sell. How to structure your consultant onboarding roadmap — From the initial Q&A to SAM.gov registration, capability statements, and JV teaming opportunities, Eric lays out the exact sequence he uses with new clients. What to ask in your discovery questionnaire — The three things Eric always needs to know: current revenue, geographic footprint, and scaling ability — and why the answer to "can you handle a $500K contract next week?" tells you everything. The a-la-carte consulting model — Why Eric moved away from broad "BD services" and toward a targeted, pick-and-choose service menu that matches what clients actually need.   EPISODE CHAPTERS:  0:00 - Introduction to the Federal Help Center Podcast 0:27 - Why Eric consults instead of contracting directly 1:27 - Case study: Arizona precast concrete company with zero federal experience 2:25 - Pulling NAICS code spending data to build your pitch 3:18 - The $222 million missed opportunity slide that closes clients 4:44 - Showing small business set-asides and example federal projects 5:44 - Building an a-la-carte consulting service menu for clients 6:39 - Consultant onboarding roadmap: SAM.gov, teaming, and capability statements 7:31 - Discovery questionnaire: revenue, geography, and scaling ability   Join our community of entrepreneurs helping entrepreneurs win federal contracts. If you want to learn more about the community and to join the webinars go to: https://federalhelpcenter.com/ Website: https://govcongiants.org/ Connect with Encore Funding: http://govcongiants.org/funding

Richard McGirr talks about how most entrepreneurs and investors overlook the simplest yet most powerful way to leverage AI organization. Revealing how meticulous systems and data management are the secret weapons that turn AI agents from a futuristic fantasy into your daily operational powerhouse. Discover how top firms are transforming their productivity with just a few tweaks: from recording every internal meeting and keeping your CRM hyper-updated, to organizing your data in Markdown instead of Word. Richard shares compelling insights from his own experiments like how understanding risk discussions boosts deal closure rates by revealing what truly moves investors and how automation can turn chaos into a strategic advantage. Learn more about your ad choices. Visit megaphone.fm/adchoices

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.

In dieser Folge begrüßt Host Dimitrios Haratsis Sarah Ostkamp, Digital Commerce Business Lead DACH bei The Magnum Ice Cream Company. Sarah blickt auf über 15 Jahre Erfahrung in der Digitalbranche zurück, unter anderem bei Stationen wie SevenOne, AdAudience und Unilever. Als Expertin für FMCG-Marketing, Programmatic Advertising und Datenstrategien verantwortet sie heute den Ausbau des digitalen E-Commerce für eine Kategorie, die traditionell fast ausschließlich vom stationären Impulskauf lebt.Wir sprechen darüber, wie man Eiscreme erfolgreich in den digitalen Raum transformiert und dort kaufbar macht. Sarah erklärt uns, warum das Konzept der „Mental Availability“ wichtiger ist als jede detaillierte Einkaufsliste und wie der klassische Marketing-Funnel zu einem konversionsstarken „Infinity-Loop“ verschmilzt. Wir erhalten spannende Einblicke in eine Datenstrategie, die bewusst gegen den Strom schwimmt, und erfahren, wie enge Partnerschaften mit Retailern das Wachstum von morgen sichern.

On this episode of Shoe In Show, Brent Barkin, Executive Vice President of ShipSigma, shares how brands can rethink shipping as more than just a cost. Drawing from his experience launching and scaling ShoeStation.com, along with leadership roles across retail, e-commerce, and logistics, Brent explains how data, carrier strategy, and operational discipline can unlock efficiency, improve margins, and support scalable growth. For footwear brands navigating today's complex supply chain, this conversation offers a practical look at turning logistics into a competitive advantage. With special guest: Brent Barkin, Executive Vice President, ShipSigma Hosted by: Matt Priest, FDRA and Thomas Crockett, FDRA

Rob Fried, digital forensics consultant, author, and assistant professor at Hofstra University, joins the Forensic Focus Podcast to talk about the evolving challenges of forensic practice, AI in investigations, and his latest book. Rob discusses his annual publication cadence — now in its fourth edition — and how each year's content is shaped by real cases, collaborations, and emerging topics in the DFIR community. This year's edition tackles AI policy, data culling strategy, and the documentation practices that keep forensic work defensible in court. The conversation also covers the growing complexity of remote collections and the soft skills required to manage them effectively, the tension between targeted and full file system approaches, and what happens when those decisions come under scrutiny in litigation. Rob shares his experience building a forensics practice from the ground up within an established e-discovery firm, his work teaching the next generation of examiners at Hofstra, and his upcoming symposium — now in its fifth year — which this year will focus on open source intelligence and corporate investigations. #mobileforensics #digitalforensics #dfir ⏱️ Timestamps 00:00 Introducing Rob Fried 03:48 Why Rob Keeps Writing 07:33 AI Policies and Teaching 09:39 AI Tools and NotebookLM 11:42 AI Spam Call Story 14:33 Forensic Lab and Data Strategy 22:30 Targeted Collection Risks 26:10 Remote Collections and Soft Skills 33:41 Building a Forensics Practice 35:14 Academia and Closing Skill Gaps 39:17 Forensics Network 41:26 Learning and Collaboration 45:42 Planning the Annual Symposium 50:36 Tech Roots and Career Values 54:49 Swag, Challenge Coins and Future Plans

In this episode, we are joined by Maria Clara Sayeg from the World Economic Forum to discuss the transformative impact of AI and data on modern supply chains and how its role creates a responsive and transparent supply chain. Download the ⁠⁠⁠⁠⁠⁠⁠⁠⁠episode transcript⁠⁠===== In this episode, we are joined by Maria Clara Sayeg from the World Economic Forum to discuss the transformative impact of AI and data on modern supply chains and how its role creates a responsive and transparent supply chain. We will discuss the importance of real-time data and the need for cross-industry collaboration. The episode also addresses future trends in manufacturing, focusing on autonomous systems, circular resource flows, and augmented workforce capabilities. ===== Guest: Maria Clara Sayeg Ribeiro Maria Clara Sayeg Ribeiro is an industrial AI strategist and product builder who's spent the last decade at the intersection of management consulting and technology, first at DuPont Sustainable Solutions, where she led engagements across industrial clients and built a global SaaS portfolio, and now at the World Economic Forum, where she leads AI and Data Strategy for Advanced Manufacturing and Supply Chains. With a background spanning four languages, an Executive MBA from EPFL, and deployments across 190+ industrial sites globally, Maria brings a rare mix of strategic perspective and hands-on product instinct to every conversation. She's passionate about making industrial AI real, not just a slide deck, but a working system that drives measurable outcomes in sustainability, resilience, and operational performance.Host 1: Richard Howells⁠⁠Richard Howells⁠⁠ has been working in the Supply Chain Management and Manufacturing space for over 30 years. He is responsible for driving the thought leadership and awareness of SAP's ERP, Finance, and Supply Chain solutions and is an active writer, podcaster, and thought leader on the topics of supply chain, Industry 4.0, digitization, and sustainability.Host 2: Sin ToSin brings over 15 years of experience in the digital media and technology industry – primarily in marketing, business development, thought leadership, and editorial. At SAP, they ensure that SAP's supply chain solutions are properly visible with a focus on future trends and sustainable innovations as part of the Thought Leadership & Awareness Supply Chain Team.===== Show Links:SAP Digital Supply Chain: www.sap.com/scmWorld Economic Forum (WEF):https://www.weforum.org/meetings/world-economic-forum-annual-meeting-2026/sessions/factories-that-think/https://centres.weforum.org/centre-for-advanced-manufacturing-and-supply-chains/luminahttps://reports.weforum.org/docs/WEF_Global_Value_Chains_Outlook_2026.pdfhttps://reports.weforum.org/docs/WEF_Global_Lighthouse_Network_2026.pdfFollow Us on Social Media : Maria Clara Sayeg Ribeiro: LinkedIn: https://www.linkedin.com/in/mariaclarasayeg/ Richard Howells:LinkedIn: www.linkedin.com/in/richardjhowells Sin To: LinkedIn: www.linkedin.com/in/sin-to-5334208 SAP Digital Supply Chain:LinkedIn: www.linkedin.com/showcase/sapdsc/ Please give us a like, share, and subscribe to stay up-to-date on future episodes!  ===== Chapters: 00:00:00 Introduction to Future Supply Chains00:00:43 Meet the Guest Maria Clara Sayeg00:02:18 AI and Data in Modern Supply Chains00:04:08 Real-World AI Implementations00:10:03 Transparency and Sustainability00:12:41 Data Integration Challenges00:16:04 Cross-Industry Collaboration00:20:20 Future Trends in Manufacturing00:24:12 Conclusion and Key Takeaways

Discover how enterprise AI and data strategy are operationalized at scale in one of the most highly regulated industries in the world. Louis DiModugno, Global Chief Data Officer at Verisk, shares how he builds AI-ready data foundations across 40+ petabytes of insurance and risk data, and the best practices behind embedding AI into enterprise products. He discusses unstructured data, deepfakes, and the shift from governance to observability, offering practical insights for data leaders scaling AI responsibly. Key Moments: From Military Leadership to Chief Data Officer: Data Integrity as a Competitive Advantage (03:02): Louis shares how his experience as a U.S. Air Force Colonel has shaped his approach to data governance, data quality, and enterprise AI leadership. He explains why integrity, service, and operational excellence are essential foundations for modern CDOs building trusted, decision-ready data environments. Building AI-Ready Data Foundations at a 40+ Petabyte Scale (17:13): Managing more than 40 petabytes of insurance and risk data, Louis breaks down how Verisk transforms complex, multi-source data into AI-ready infrastructure. From entity resolution and master data management to benchmarking and predictive analytics, he outlines what it takes to prepare enterprise data for AI and advanced analytics at scale. Designing an AI-First Data Strategy for Enterprise Decision Intelligence (20:00): Louis breaks down how Verisk evolved toward an AI-first data strategy across more than 150 insurance and analytics products. Rather than treating AI as an add-on, he explains how embedding AI into core workflows enables smarter underwriting, pricing, regulatory reporting, and risk management. He also discusses the strategic role ThoughtSpot plays in delivering natural language search, embedded analytics, and scalable AI-driven decision making. AI Fraud, Deepfakes, and Risk Management in Financial Services (26:11): As AI-generated images and synthetic claims become more sophisticated, Louis discusses how the insurance industry is combating deepfake fraud and AI-driven manipulation. He shares best practices around AI risk management, vendor partnerships, and regulatory collaboration to protect policyholders and maintain trust. Unstructured Data and AI: Why Governance Still Matters (29:28): Louis explores how expanding beyond structured data is reshaping enterprise AI. He explains why incorporating unstructured data into vector databases, graph models, and knowledge systems can significantly improve model accuracy and decision confidence. At the same time, he emphasizes that stronger governance (or observability as he reframes it) is essential as organizations scale AI across regulated industries. Key Quotes: “The more data that you bring to the equation, the more elements that you have in the algorithm, the higher level of accuracy you should be able to reach with your outcomes.” - Louis DiModugno “I've tried to move away from using the word governance as much as I like to use the word observability, because I really think observability shows more aspects of what it is that we are doing with the data.” - Louis DiModugno “The underlying aspect of what ThoughtSpot's delivering to them is our insights that not only give them their answer, but also give them insights that maybe they weren't looking specifically for. One of the big benefits of ThoughtSpot is that it's trying to anticipate what you're asking for.” - Louis DiModugno “We've partnered with ThoughtSpot, which brings AI embedded within its product. By having our data available through the data sets that we populate through the ThoughtSpot products, we've got the opportunity to utilize Spotter and the natural language processing capabilities to interact with the data, so that you can ‘talk with your data'.” - Louis DiModugno Mentions From Months to Weeks: How Verisk Scaled Embedded Analytics Breaking Down Digital Media Fraud for Claims in the AI Era Randy Bean's 2026 AI & Data Leadership Executive Benchmark Survey Guest Bio  Louis DiModugno brings more than 20 years of career experience in data and analytics to his new role. He has held several leadership positions in insurance and (re)insurance at firms including The Hartford and AXA US, where he served as the company's inaugural Chief Data & Analytics Officer. Most recently, DiModugno pioneered the role of Chief Data and Technology Officer for Hartford Steam Boiler. Before entering the private sector, DiModugno served with distinction as a Colonel in the U.S. Air Force and Air Force Reserves. He has held teaching positions at Rensselaer Polytechnic Institute, and he currently serves on the Chief Data Officer Advisory Council for the George Mason University School of Business. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Data is only useful if you know what to do with it. In this episode, we talk with Andrew Langlois, Data Strategist for DeKalb CUSD 428, and Susie Manikas, Senior Account Executive for SchoolData.net, about how one Illinois district is turning raw data into real decisions. Seven months into their implementation, Andrew shares what's changed — and what other superintendents should know before they start.

Zainulabedin Shah is a visionary leader with over 18 years of expertise in data strategy, analytics, and AI, renowned for transforming businesses and driving significant growth. As the CEO and Co-Founder of Zeed, he empowers companies to unlock their potential through cutting-edge data solutions. His accomplishments include modernizing data and AI platforms for a $5B global company, resulting in $2M in annual savings, and leading strategy at First Republic Bank, driving a 42% year-over-year sales increase. He also scaled a $2M auto startup into a $70M business with a 143% CAGR, despite having no prior industry experience. As CEO of Zeed, Zainulabedin continues to leverage his vast experience, helping businesses harness the power of data to drive strategic priorities and achieve scalable growth. His expertise extends to executing complex M&A integrations, developing innovative business models, and fostering data-driven cultures. As a podcast guest, Zainulabedin brings valuable insights into data strategy, digital transformation, and leadership, offering a compelling perspective for audiences seeking to understand the intersection of technology and business growth. Connect with Jon Dwoskin: Twitter: @jdwoskin Facebook: https://www.facebook.com/jonathan.dwoskin Instagram: https://www.instagram.com/thejondwoskinexperience/ Website: https://jondwoskin.com/LinkedIn: https://www.linkedin.com/in/jondwoskin/ Email: jon@jondwoskin.com Get Jon's Book: The Think Big Movement: Grow your business big. Very Big!   Connect with Zainulabedin Shah:Linkedin: https://www.linkedin.com/in/zainulabedinshah/                                                                                       *E – explicit language may be used in this podcast.

Episode OverviewIn this episode of CDO Matters, host Malcolm Hawker speaks with Samir Sharma about the idea behind the Data Strategy Canvas and how organizations can bring more structure to their data initiatives. They discuss how frameworks can help leaders connect data strategy to real business priorities and improve alignment across teams. The conversation explores practical ways data leaders can simplify complex strategy discussions and move from vision to execution.Episode Links and ResourcesFollow Malcolm Hawker on LinkedInFollow Samir Sharma on LinkedIn

Compliance and regulatory reporting used to mean endless spreadsheets, fragmented data sources, and teams drowning in manual work. Today, AI is transforming how the world's largest companies manage financial reporting, sustainability disclosures, and audit workflows—not by replacing humans, but by giving them time back to do strategic work. In this episode of IT Visionaries, host Chris Brandt sits down with Kim Huffman, CIO of Workiva, the platform used by 85% of the Fortune 100 for critical financial and compliance reporting. Kim shares her unique perspective as both a former Workiva customer and now the CIO steering the company into an AI-powered future. They explore how the office of the CFO is evolving under pressure from new sustainability regulations, how AI governance actually works in practice, and why collaboration between IT, finance, sustainability, and risk teams has become essential. Kim also discusses the changing role of the CIO, the coming wave of autonomous agents in the workplace, and why having more data doesn't always mean making better decisions.   Key Moments: 00:58 – The State of Compliance Today 02:18 – Why Standards and Regulations Matter 05:48 – The Complexity of Global Compliance 07:36 – Data Collection Across Teams 08:36 – Single Source of Truth 10:20 – The Sustainability Data Challenge 13:36 – The Endless Spreadsheet Problem 16:12 – What's Driving the CFO Office 19:46 – AI's Strategic Role at Workiva 23:02 – Beyond Repetitive Tasks 25:20 – Transforming How Teams Work 27:03 – Will AI Replace Jobs or Create Capacity? 30:00 – Measuring AI's Business Impact 33:06 – Speed vs. Data Overload 36:25 – The Evolving Role of the CIO 40:00 – Technology Leadership in Transition 43:09 – The Next Five Years for CIOs 46:14 – Managing the Coming Wave of AI Agents 50:02 – AI Will Create Its Own Security Industry 52:26 – The Sustainability Reporting Reality 55:31 – Resource Constraints and AI Consumption 57:34 – Why ESG Data Is Now Critical Business Intelligence 59:23 – Keeping NPS High While Innovating   -- This episode of IT Visionaries is brought to you by Meter - the company building better networks. Businesses today are frustrated with outdated providers, rigid pricing, and fragmented tools. Meter changes that with a single integrated solution that covers everything wired, wireless, and even cellular networking. They design the hardware, write the firmware, build the software, and manage it all so your team doesn't have to.That means you get fast, secure, and scalable connectivity without the complexity of juggling multiple providers. Thanks to meter for sponsoring. Go to meter.com/itv to book a demo.---IT Visionaries is made by the team at Mission.org. Learn more about our media studio and network of podcasts at mission.org. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Rare diseases are often thought about in terms of the financial burden they create, but a new paper from the World Economic Forum urges policymakers, payers, and business leaders to see them as one of the greatest underappreciated opportunities in global health. In “Making Rare Diseases Count: How Better Data Can Unlock a Multi-Trillion Dollar Opportunity,” the organization argues that high-quality, patient-centered data must sit at the core of any rare disease strategy. We spoke to Will Greene, lead author of the paper and a board member of the Foundation for Prader-Willi Research, about the need for smarter collection, sharing, and analysis of data; how doing so can unlock outsized returns in human health, and what it will take to mobilize a broad set of stakeholders around that vision.

Guests: Tim Kreytak, CEO and Co-Founder of Ironside, one of North America's leading analytics and data consulting firms and a multiple Inc. 5000 honoree. Jason Breazeale, Vice President of Technology at Burn Boot Camp, a rapidly growing fitness franchise with nearly 400 locations. Overview: Yes, your company needs to be using AI. But before you start buying subscriptions and upgrading your tech stack, you need to understand what actually drives successful AI integrations: a bedrock of solid data. Without clean, accessible, and trusted data, even the most advanced AI tools will fail to deliver results. On today's show, Tim Kreytak and Jason Breazeale discuss why you need BIG data mastery before AI can help your company Make BIG Happen.

Matt Ober is a Managing Partner at Social Leverage, an early stage venture capital firm focused on investing in fintech and enterprise SaaS. The firm is best well know for being early investors in Robinhood, Etoro, Alpaca, & Kustomer which was acquired by Meta. Mat was most recently the Chief Data Scientist at Third Point. Prior to joining Third Point, Matt was the Head of Data Strategy at WorldQuant and part of the WorldQuant Ventures founding team focused on private investments in fintech, data, and technology companies. Matt is the founder of InitialDataOffering.com which is the largest community of data buyers and data vendors. Matt holds a Chartered Alternative Investment Analyst (CAIA) designation and sits on the board of governors for his alma mater, California State University Chico. In this conversation, we discuss:- Identifying trends - Prediction Markets allowing people to monetize their niche - The degenerate economy & financial nihilism - Never been a better time to build a business - Tokenization of all assets - Stablecoins - Consumption-based data models - Early-stage investing - How wealth management technology is changing - How AI is influencing all startups Social LeverageX: @SocialLeverageWebsite: www.socialleverage.comLinkedIn: Social LeverageMatt OberX: @obermattjLinkedIn: Matt Ober---------------------------------------------------------------------------------This episode is brought to you by PrimeXBT.PrimeXBT offers a robust trading system for both beginners and professional traders that demand highly reliable market data and performance. Traders of all experience levels can easily design and customize layouts and widgets to best fit their trading style. PrimeXBT is always offering innovative products and professional trading conditions to all customers.  PrimeXBT is running an exclusive promotion for listeners of the podcast. After making your first deposit, 50% of that first deposit will be credited to your account as a bonus that can be used as additional collateral to open positions. Code: CRYPTONEWS50 This promotion is available for a month after activation. Click the link below: PrimeXBT x CRYPTONEWS50FollowApple PodcastsSpotifyAmazon MusicRSS Feed

This conversation features a thoughtful discussion between Nathan and Kelley Hecht on how nonprofits can use AI in practical, responsible ways.  Nathan introduces Kelley and highlights her work at AWS, where she helps nonprofit organizations adopt cloud technology and AI with a strong focus on data, security, and ethics. Kelley shares her journey into AI, explaining how early confusion helped her understand the gap between hype and reality. Together, they explore common misperceptions about AI, stressing that it is not about tools alone, but about clear goals, good data, and strong decision-making.   The conversation emphasizes data as a strategic asset and introduces Amazon's "working backwards" approach to solving problems. Nathan and Kelley also discuss challenges faced by both small and large nonprofits, the importance of leadership support, and the role of culture in AI adoption. They conclude by reinforcing that AI should strengthen human connection, not replace it.    HIGHLIGHTS  [02:22] Kelley's Role and Contributions to Nonprofit AI  [07:04] Challenges and Misperceptions in AI Adoption   [12:11] AI's Role in Nonprofit Ecosystems   [15:00] Data Strategy and AI Integration   [20:52] Working Backwards: Starting With the Why  [24:16] Moving Beyond "AI as the Answer"  [32:59] Data as the Lifeblood of the Organization  [41:45] Future of AI in Nonprofit Sector   Connect with Kelley:  LinkedIn: linkedin.com/in/kelleyhecht  Connect with Nathan and Scott:  LinkedIn (Nathan): ⁠⁠linkedin.com/in/nathanchappell/⁠⁠  LinkedIn (Scott): ⁠⁠linkedin.com/in/scott-rosenkrans⁠⁠  Website: ⁠⁠fundraising.ai/⁠⁠ 

Collecting and analyzing student data and then acting on any findings to support student success is a struggle for many institutions. Often data is in the wrong format, inaccessible to the right teams or there are so many analytics colleges don't know where to start. Many administrators also lack the data literacy needed to make accurate, data-informed decisions. In this episode, we're sharing a discussion Inside Higher Ed Editor in Chief Sara Custer had with higher ed leaders at IHE's Student Success 2025 event. Courtney Brown, vice-president of strategic impact and planning at the Lumina Foundation, Elliot Felix, the higher education advisory practice lead at Buro Happold and Mark Milliron the president of National University bring unique experiences and perspectives to the question of how institutions can be data-driven and student centered. This episode is sponsored by the Gates Foundation.

For Steve Dennis and Michael LeBlanc's final episode recorded live at the CommerceNext Growth show, they welcome two visionary leaders from Tapestry, the global house of brands that includes Coach and Kate Spade: Pooja Chandiramani, VP Global Media Strategy & Planning, Marketing Analytics, Operations and Transformation, and Avinash Kaushik, Brand Strategy & Marketing Transformation.Pooja and Avinash unpack Tapestry's ongoing transformation, which embeds analytics as a core pillar of brand growth. For Coach in particular, analytics isn't just incremental—it's a complete transformation journey. By using data to generate insights that directly drive business impact, Tapestry ensures marketing investments are accountable, measurable, and tied to outcomes.Avinash, a globally recognized thought leader and author, explains how Tapestry embraces intent-centric marketing to connect authentically with consumers. Moving beyond the outdated “accessible luxury” positioning, the company has shifted toward "expressive luxury"—a modern framework that reflects values-driven, authentic consumer engagement, particularly resonant with younger audiences.The conversation dives into the cultural foundations necessary for analytics to thrive. Avinash emphasizes that “culture is more important than data,” crediting Tapestry's CEO Joanne Crevoiserat and senior leadership for creating an environment where data can challenge assumptions and guide decisions. This culture enables bold experiments, including measuring the incrementality of brand marketing—one of the toughest questions in retail.Pooja highlights how creative pre-testing has become a critical unlock. By partnering with Human Made Machine, Tapestry tests campaigns with real audiences before investing media spend. This approach ensures that creative—responsible for up to 70% of marketing impact—delivers measurable results in driving brand awareness and incremental sales. It's a cultural shift, moving from subjective opinions about creative to decisions grounded in data.The episode also explores the role of AI and machine learning in accelerating agility, simplifying decision-making frameworks, and enabling global scalability. Both leaders stress that outcomes-based planning—rather than activity-based planning—keeps Tapestry aligned with its ambitious growth goals.  About UsJennifer MarloHead of Content, CommerceNextJennifer Marlo drives industry-leading programming at CommerceNext, drawing on experience from Ascendant Network and iMedia Connection, where she spearheaded content strategies to inspire retail, brand and agency marketing leaders. Guided by the belief that “a rising tide lifts all boats,” Jennifer uses in-person and digital platforms to educate and foster industry collaboration. Steve Dennis is a strategic advisor and keynote speaker focused on growth and innovation, who has also been named one of the world's top retail influencers. He is the bestselling authro of two books: Leaders Leap: Transforming Your Company at the Speed of Disruption and Remarkable Retail: How To Win & Keep Customers in the Age of Disruption. Steve regularly shares his insights in his role as a Forbes senior retail contributor and on social media.Michael LeBlanc is the president and founder of M.E. LeBlanc & Company Inc, a senior retail advisor, top retail influencer, keynote speaker and media entrepreneur. Michael produces and hosts a network of leading retail trade podcasts, including the award-winning No.1 independent retail industry podcast in America, Remarkable Retail with his partner, Dallas-based best-selling author Steve Dennis; Canada's top retail industry podcast The Voice of Retail and Canada's top food industry and one of the top Canadian-produced management independent podcasts in the country, The Food Professor with Dr. Sylvain Charlebois from Dalhousie University in Halifax.

In this special Cloud Wars report, Bob Evans sits down with Michael Ameling, President and Chief Product Officer of SAP Business Technology Platform, for a deep dive into how SAP is helping customers navigate the fast-moving AI Era. Ameling and Evans discuss how SAP's Business Data Cloud, partnerships with Snowflake and Databricks, HANA Cloud innovations, and new AI-powered tools and agents are helping SAP evolve from an applications powerhouse into a data-and-AI-driven business platform for the next generation.SAP's AI Data FutureThe Big Themes:SAP HANA Cloud Becomes an AI-Optimized Database: SAP HANA Cloud is evolving into “the database AI was looking for." As a multi-model system supporting spatial, graph, vector, and document storage, HANA Cloud enables AI workloads to run more efficiently and contextually. Recent additions, like vector engines and Knowledge Graph capabilities, give customers powerful tools for retrieval-augmented generation (RAG), contextual reasoning, and advanced analytics.Developers Are 'The AI Revolution': Developers aren't observing the AI Revolution, they are the revolution. With modern AI tools, developers can innovate faster, solve bigger problems, and directly influence business outcomes. SAP is investing heavily in meeting developers where they are by enhancing IDEs, building business-aware development tools, and providing context-rich assets such as APIs, business objects, and process insights. AI acts as a teammate, not a replacement.SAP: An Applications and a Data Company: SAP must be both an applications and a data company. Customer value emerges when applications, data, and AI converge seamlessly. SAP's decades of industry expertise give it unparalleled business context, which becomes even more powerful when embedded into AI agents and data platforms. With more than 34,000 SAP HANA Cloud customers and rapidly expanding AI adoption, SAP is positioning itself as the platform where business process knowledge meets modern AI capability.The Big Quote: " . . what we need to understand that AI is our teammate. It's like asking your best friend who has a lot of knowledge, but you can ask multiple friends at the same time. Not everything is always right, but you can ask questions, you can continuously improve. If we understand that pattern, we understand that AI helps us to solve much bigger problems as a developer, and then, of course, having much more impact on real business."More from Michael Ameling and SAP:Connect with Michael Ameling on LinkedIn, or get more insights from SAP TechEd.  Visit Cloud Wars for more.

The promise of agentic AI has been massive, autonomous systems that act, reason, and make business decisions, but most enterprises are still struggling to see results.In this episode, host Chris Brandt sits down with Sumeet Arora, Chief Product Officer at Teradata, to unpack why the gap exists between AI hype and actual impact, and what it takes to make AI scale, explainable, and ROI-driven.From the shift toward “AI with ROI” to the new era of human + AI systems and data quality challenges, Sumeet shares how leading enterprises are moving from flashy demos to measurable value and trust in the next phase of AI. CHAPTER MARKERS00:00 The AI Hackathon Era03:10 Hype vs Reality in Agentic AI06:05 Redesigning the Human AI Interface09:15 From Demos to Real Economic Outcomes12:20 Why Scaling AI Still Fails15:05 The Importance of AI Ready Knowledge18:10 Data Quality and the Biggest Bottleneck20:46 Building the Customer 360 Knowledge Layer23:35 Push vs Pull Systems in Modern AI26:15 Rethinking Enterprise Workflows29:20 AI Agents and Outcome Driven Design32:45 Where Agentic AI Works Today36:10 What Enterprises Still Get Wrong39:30 How AI Changes Engineering Priorities55:49 The Future of GPUs and Efficiency Challenges -- This episode of IT Visionaries is brought to you by Meter - the company building better networks. Businesses today are frustrated with outdated providers, rigid pricing, and fragmented tools. Meter changes that with a single integrated solution that covers everything wired, wireless, and even cellular networking. They design the hardware, write the firmware, build the software, and manage it all so your team doesn't have to.That means you get fast, secure, and scalable connectivity without the complexity of juggling multiple providers. Thanks to meter for sponsoring. Go to meter.com/itv to book a demo.---IT Visionaries is made by the team at Mission.org. Learn more about our media studio and network of podcasts at mission.org. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Decoding Shopify's Strategic Shift: What It Means for MerchantsIn Gartner's recent Magic Quadrant, Shopify has gained significantly in Execution and Vision. Whilst analyst reports shouldn't be seen as definitive, what's interesting is charting Shopify's fast ascent from challenger to leader.In this podcast, James & Paul look at the key strategic developments in 2025 that have fuelled this shift, and explore what they reveal about Shopify's direction of travel, as well as the implications for ecommerce teams.As Shopify continues to solidify its position as a market leader, understanding its strategic direction is crucial for businesses looking to leverage the platform effectively, as well as deciding if it's the right platform to bet on.What you'll get from the pod:Insightful analysis: get a deeper understanding of Shopify's strategic moves and how they impact the ecommerce landscape. Expert opinion: hear from industry experts as they dissect Shopify's latest initiatives. Actionable takeaways: discover practical insights that can help you leverage Shopify's capabilities.Key discussion points:AI and automation: Shopify's focus on AI capabilities aims to enhance automation and self-service, offering merchants new ways to streamline operations and improve customer experiences. Atlas AI store builder: empowers merchants to build stores with minimal developer support, expanding accessibility and innovation in store creation. Data strategy: Shopify's first-party data strategy is set to benefit advertising and customer insights, providing merchants with valuable data to drive decision-making. Payment focus: The integration with Lovable highlights Shopify's commitment to securing payment processes and enhancing brand awareness. Get James & Paul's informed perspective on how Shopify's strategic direction impacts your business.Key chapters:[00:30]- Introduction to Shopify's Strategy[03:30]- AI and Automation in Shopify[10:00]- Atlas AI Store Builder[16:00]- Shopify's Data Strategy[22:00]- Why Invest In Lovable?[28:00]- Omnichannel Experience

Leaders are under pressure to "do something with AI," but many end up making the same preventable mistakes that derail their data strategy before it starts. In this episode, Ashok sits down with David Cohen, founder of Superposition Strategy, to run a live workshop identifying the top five strategic missteps leaders make—and more importantly, how to avoid them. If you're leading or supporting a data or AI initiative, this episode is your tactical blueprint for getting it right. You'll hear David walk through an interactive exercise designed to prioritize problems and focus on high-impact solutions. Together, he and Ashok unpack real-world examples from highly regulated industries, debate the role of data as a profit center, and share facilitation tactics that help teams speak up and make better decisions. Whether you're drowning in stakeholder expectations or trying to build buy-in, you'll walk away with actionable strategies to make your AI and data efforts more resilient—and more effective. Unlock the full potential of your product team with Integral's player coaches, experts in lean, human-centered design. Visit integral.io/convergence for a free Product Success Lab workshop to gain clarity and confidence in tackling any product design or engineering challenge. Inside the episode... The 5 most common mistakes leaders make in AI and data strategy Why "no leadership buy-in" is the #1 killer of data initiatives The power of divergent thinking in early-stage problem framing What leaders get wrong about experimentation and timelines Real examples from healthcare, finance, and automotive sectors Why facilitation tools like silent brainstorming and dot voting matter How governance can unlock innovation—if you start early The hidden advantage small consultancies have with AI Using structured workshops to create shared understanding and focus Mentioned in this episode LEGO Build Together sets Ford Labs, 99P Labs, Capital One Labs (innovation labs examples) Facilitation techniques (dot voting, silent ideation, Roman voting) David's Linkedin: https://www.linkedin.com/in/davcohen06/ David's company: https://www.superpositionstrat.com/  Superposition's youtube channel: https://www.youtube.com/@Superpositionstrat Subscribe to the Convergence podcast wherever you get podcasts, including video episodes on YouTube at youtube.com/@convergencefmpodcast Learn something? Give us a 5 star review and like the podcast on YouTube. It's how we grow.  

Industrial Talk is onsite at SMRP 2025 and talking to Colin Morris, Sr. Director of Solution Consulting at MaintainX about "Mobile first asset management platform". Scott MacKenzie interviews Colin from MaintainX at the SMRP conference in Fort Worth, Texas. Colin discusses Maintain X's mobile-first platform, which integrates with various systems like SCADA, ERPs, and wireless sensors to enhance maintenance decisions and asset availability. The platform, which has a three-week implementation period, supports voice summaries to capture technician knowledge and offers a full web client. MaintainX recently raised funding to enhance AI and mobility features. Colin emphasizes the importance of data strategy and extensibility in modern maintenance practices. Listeners can learn more at gomaintainx.com. Action Items [ ] Reach out to Colin on LinkedIn or the maintain X website to learn more about the platform. Outline Introduction and Welcome to Industrial Talk Podcast Scott MacKenzie introduces the Industrial Talk podcast, sponsored by CAP Logistics, emphasizing the importance of 24/7 insights into supply chains.Scott MacKenzie welcomes listeners to the podcast, highlighting the SMRP conference in Fort Worth, Texas, and introduces Colin from MaintainX.Colin shares his experience of working with Maintain X for over two years and his background in the software industry, including his time at Rockwell Automation. Colin's Background and MaintainX's Funding Colin discusses his career journey, including his 10 years in the software business before joining MaintainX.Scott MacKenzie inquires about Colin's first SMRP experience, and Colin expresses his excitement and impressions of the conference.Colin explains MaintainX's recent funding round, which will enhance AI and mobility features, and improve the product's strength.Scott MacKenzie asks about MaintainX's strategic vision and priorities, focusing on AI and mobility. MaintainX's Features and Differentiation Colin elaborates on MaintainX's mobile-first application, which is designed for technicians and other business users.Scott MacKenzie asks how MaintainX differentiates itself from other solutions, and Colin emphasizes the platform's extensibility and connectivity.Colin explains MaintainX's ability to ingest information from various sources, including wireless sensors, SCADA, and ERPs, to improve maintenance decisions.Scott MacKenzie and Colin discuss the importance of bidirectional connectivity and API bridges to integrate with existing systems. Data Strategy and Implementation Colin highlights the importance of understanding data strategy and building integrations with other systems.MaintainX offers in-house integration teams and self-serve tools for customers to connect their systems.Colin discusses the challenges of OEE and the need for real-time analytics to improve asset availability.Scott MacKenzie inquires about the point of diminishing returns with data, and Colin explains the need to balance data collection with business impact. Implementation Period and Third-Party Solutions Colin explains MaintainX's average implementation period of three weeks, emphasizing outcome-based goals.MaintainX has a team of over 40 implementation consultants, most of whom are engineers, to ensure successful setups.Colin confirms that Maintain X supports third-party solutions, including vibration vendors, Ignition, Kepware, and industrial protocols like MQTT.MaintainX is an SAP partner and connects with Oracle and other...

1026: What if your company's data could become a public good and a competitive moat? Beaumont Vance, SVP of Data and AI at Paychex, shares how his team transforms payroll and HR data into real-time economic insight, powering everything from AI copilots to U.S. government response efforts. With 1 in 11 U.S. workers on the platform and 39 million client interactions annually, Paychex treats data as infrastructure.

Black Friday is around the corner, and if your product-based clients aren't ready with their e-commerce marketing, it's not too late. Progress over perfection is the name of the game.In this episode, I'm joined by Business Strategist Carlyn Bushman to talk about e-commerce marketing, Shopify strategy, and how freelancers like you can confidently support product-based clients heading into Q4. We're breaking down what's actually working in today's noisy e-com world, like using Shopify data, SMS campaigns, and smart AI tools, so you can bring real strategy to your clients.Carlyn Bushman is the founder of Carlyn Bushman Consulting and the powerhouse behind POP Academy—a no-fluff, all-strategy learning hub for product-based business owners who want to scale smart and stay profitable. With a background in corporate leadership and deep expertise in business strategy, she's obsessed with helping founders ditch the guesswork, master their numbers, and finally build the business they've been dreaming about with practical tools, real talk, and data that actually drives growth.Listen to learn more about:How to use Shopify analytics and Sidekick AI to plan smarter Q4 promotionsWhy SMS marketing is outperforming email (and how to start small)Real talk on omni-channel marketing and what “being everywhere” actually meansTips for creating multiple day-of promos for Black Friday, Small Business Saturday, and Cyber MondayHow freelance marketers can support e-commerce clients with data-driven strategy and executionThis episode gives you the exact tools, talking points, and tactics to confidently show up for your product-based clients and turn their data into measurable sales growth.Sponsored by The Digital Marketer's Workgroup Are you already doing marketing work but need more clients and a stronger referral network? Join our tight-knit community of freelancers and get access to behind-the-scenes conversations, support, and troubleshooting that every solo marketer needs. Plus, you'll benefit from advanced trainings, networking opportunities, and exclusive job leads. Apply here!Links Mentioned in the Show: Grab Carlyn's FREE Grant Resource List here. Carlyn took care of the research, so you can focus on applying. Get access to grant opportunities up to $100,000 covering startup support, industry-specific capital, and opportunities for creatives, women-owned brands, and beyond. Grants are added weekly, so bookmark it and keep it on hand to reference all year long!Watch our video in Carlyn's Founder Series: Marketing That's Actually Working in 2025 with Emily ReaganThe Mom Test by Rob FitzpatrickConnect with Carlyn:Instagram: @carlynbushmanconsulting Facebook: CMB BrandsWebsite:

In this episode of the Thread Podcast, host Justin Vandehey sits down with Max Mozes, Director of Revenue Operations at Walmart for Business, to unpack what it takes to build a world-class B2B motion inside one of the world's largest retail organizations.Before joining Walmart, Max carried a quota as a seller, scaled local business teams at LevelUp (acquired by Grubhub for $400M), and held key operational roles at Amazon Business — giving him a unique perspective on how to connect frontline sales experience with scalable systems and data strategy.Together, Justin and Max dive into:The evolution of RevOps from sales enablement to strategic business architectureHow Walmart is redefining B2B commerce for 30+ million potential business customersWhat RevOps looks like when you're serving both local bakeries and Fortune 500sThe art of structuring and prioritizing massive datasets across millions of prospectsWhy data enrichment and hierarchy matter more than just “more data”What's hype vs. what's real when it comes to AI and GTM automationAnd the soft skills that separate good operators from great ones: agency, resourcefulness, and ownership Key TakeawaysB2B at scale is an architecture problem. Data flows, enrichment, and prioritization define how sales teams actually execute.RevOps must translate complexity into clarity. The role is as much about storytelling as it is about systems.AI and data are only as powerful as the processes behind them. Automation should amplify human productivity, not replace it.Resourcefulness + Agency = Operator Superpowers. The best RevOps leaders identify problems, fix them, and bring solutions already in motion.Memorable Quotes“Every business in the U.S. is a potential Walmart customer — from a one-person bakery to a university with a $300M endowment.” “There's no sales team big enough to manually reach every account — so data prioritization becomes your growth strategy.” “RevOps isn't about the system you use. It's about how you organize information so sellers can take action.” “Agency and resourcefulness — that's the difference between operators who just report problems and those who drive change.”Chapters00:00 — Introduction: From Seller to RevOps Leader 02:00 — Lessons from LevelUp and Amazon Business 03:30 — Bridging Sales and Operations 05:00 — Inside Walmart for Business: Scaling B2B at Retail Scale 08:00 — Data Architecture, Enrichment, and Prioritization 11:00 — AI Hype vs. Reality in Enterprise RevOps 14:00 — Building Resourceful Teams and Taking Ownership 17:00 — Walmart for Business: Hiring and Next StepsResources & MentionsWalmart for Business — https://business.walmart.comGrubhub acquires LevelUp — $400M acquisition (TechCrunch, 2021)Max Mozes on LinkedIn — https://www.linkedin.com/in/maxwellmozes About the GuestMax Mozes is the Director of Revenue Operations at Walmart for Business, where he leads strategy, data, and process optimization for one of the largest B2B commerce initiatives in the U.S. His career spans sales and RevOps roles at Amazon, Grubhub/LevelUp, and high-growth startups, giving him a rare perspective on building go-to-market infrastructure from both startup scrappiness and enterprise scale.

In this episode, Emma sits down with Chris Rueckert, SVP of Commerce at Flywheel, to explore how shopper media is evolving and why it matters more than ever. Chris shares details on Flywheel's new shopper media offering, highlighting the importance of connecting data, strategy, and the in-store experience.Listen in to learn how we're helping brands bridge the gap between retail media and shopper marketing, and why a holistic, consumer-centric approach is key for driving sales. Interested in learning more about our Retail Insights AI assistant? Let's connect: https://www.flywheeldigital.com/contact-us

Company background: "HSO is the second largest Microsoft partner in the globe," Holwagner reports. It focuses on industries including professional services, manufacturing, finance, and the public sector. HSO continues to grow not only with its traditional ERP services but also around cloud and AI services. "The mission here is really to improve our clients' business performance with the results of Microsoft solutions."AI's market impact: "It's definitely a transformation happening faster than anything I've seen before," Holwagner says. While there's already been significant advancements with AI, it's still only the beginning of what has yet to be built out and understood. He breaks down AI across four different roles:At the top level, boards and owners are pushing for areas of efficiency to stay competitive, reimagining the business model using AI.The next level is the CTO or an IT manager; they have efficiency demands, but they're also primarily thinking about how to contain information and data in a security model.The business leaders or department heads are being tasked to think about efficiency using AI but they're mostly busy keeping their engine going. They need tools that show them where to get ROI.The last level is HR, which might be considering where AI is filling in for various jobs.Perspectives for applying AI: HSO looks from a responsibility perspective in three different areas. First, it aims to educate customers on what's possible while also focusing on what's doable. Second is protection, which involves having control over your domain information. The third area is thinking about use cases for specific AI components.Organizational transformation: With the introduction of AI, there's a transformation happening across organizations in a variety of industries. AI has been thought of as a technical element when it needs to be included in functional conversation, especially for consulting businesses, Holwagner notes. Leaders and managers must understand the concepts of weaving in AI to give it value. AI transformation will likely lead to a "healthy reduction in certain areas" in the workforce, but "the transformation of what people are going to do in the organization is going to change." It will be more business logic transformation consulting and fewer hands-on the keyboard-related tasks, Holwagner shares.Summit NA: HSO will be attending Community Summit North America. You can connect with HSO at booth #209. The HSO team will be presenting several sessions throughout the event as well, including:The Latest D365 AI Agents and Features to Automate Your Supply Chain on Monday, October 20thDelivering a Scalable, Secure Data & AI Platform on Monday, October 20th3 Hidden Risks of AI in the Enterprise—and How to Manage Them Responsibly on Tuesday, October 21stSolving Customer Master Data Challenges for a 360° View in Dynamics 365 CE (CRM) and F/SCM (FO) on Wednesday, October 22nd Visit Cloud Wars for more.

What does it really mean to future-proof financial data? That's the question at the heart of my conversation with George Rosenberger, General Manager of NYFIX at Broadridge. George has spent his career moving through every corner of the capital markets, from trading desks to broker-dealers, and now into the software side where he oversees order routing, post-trade matching, and the adoption of new AI tools. His perspective is uniquely positioned between the history of financial markets and their rapidly accelerating future. This discussion takes inspiration from Broadridge's fifth annual Digital Transformation and Next Gen Technology study, which collected insights from more than five hundred technology and operations leaders across financial services. The survey highlights both the progress and the pressure points facing the industry. Forty-one percent of leaders still cite data security as a major hurdle, and while cloud, AI, and cybersecurity dominate the technology stack, a third of firms still lack security built into their core systems. George explains why this gap persists, how legacy platforms complicate modernization, and what steps firms can take to extract value from old infrastructure while preparing for what's next. We also explore the irony that many organizations overestimate their digital maturity. Generative AI adoption has surged from forty to seventy-two percent in a year, but governance, compliance, and data quality concerns remain. George stresses the importance of measuring outcomes, not just intentions, and shares how Broadridge is approaching AI responsibly through initiatives like its Algo Copilot, which helps traders make sharper decisions. If you're curious about how financial services can strengthen cybersecurity, reduce technical debt, and rethink data strategy as a true engine of innovation, this episode offers both a candid reality check and a roadmap. The speed of change is staggering, but with the right strategy, leaders can build resilience and stay ahead in a digital-first world.

In banking, every second matters. Fraud happens in milliseconds. Customers demand instant answers. And AI can only deliver value if it's powered by live, real-time data. Yet many banks are still relying on batch reports and outdated systems, making decisions based on yesterday's insights. The shift can't wait. Forrester predicts that by 2025, half of all businesses will use AI-powered self-service as their primary customer touchpoint. That future won't be possible without real-time data at the core. Banks that leverage streaming data will transform customer experiences, manage risks more efficiently, and unlock the full potential of AI. Those who don't risk being left behind. Today, I'm joined by Guillaume Aymé, CEO of Lenses.io and a leading voice on data innovation. Together, we'll explore why real-time data is becoming the lifeblood of modern banking, the hurdles institutions must overcome, and how to build the foundation for AI-driven success. This episode of Banking Transformed is sponsored by Lenses Lenses 6.0 is a Developer Experience designed to empower organizations to modernize applications and systems with real-time data autonomy. This is particularly crucial as AI adoption accelerates, and enterprises operate hundreds of Kafka clusters across multi-cloud environments. As the industry's first multi-Kafka developer experience, Lenses 6.0 allows teams to access, govern and process streaming data across any combination of Apache Kafka-based streaming platforms, from a single interface. https://lenses.io/

In this episode of the NASP Podcast, Sheila Arquette, President & CEO of the National Association of Specialty Pharmacy, sits down with Hillary Gretton, Director of Data Strategy at Claritas Rx. Together, they explore today's challenges in market access and patient journey analytics, and how Access Analytics is helping specialty pharmacies, payers, and partners tackle these barriers. The conversation highlights real-world use cases from Claritas Rx's recent whitepaper, From Prescription to Abandonment: How 31% of Specialty Medications Go Unfilled Across Therapeutic Areas, showing how better data can drive better outcomes.

This podcast is brought to you by Outcomes Rocket, your exclusive healthcare marketing agency. Learn how to accelerate your growth by going to outcomesrocket.com Strong science isn't just a checkbox; it's a growth strategy that can set digital health startups apart in crowded markets. In this episode, Dr. Jennifer Huberty, CEO & founder of Fit Minded, explores how behavioral health companies at any stage can build scientific credibility without depleting their resources. She stresses that many startups fail to recognize the value of the data they already possess: data that can drive business decisions, fuel marketing narratives, and even lead to peer-reviewed publications. Jennifer walks through cost-effective research strategies, such as retrospective analyses and national surveys, while cautioning against fragmented, one-off studies that lack strategic alignment. She also introduces the idea of “traveling science,” a framework for making research actionable across departments, and emphasizes that trust, transparency, and clear alignment with business goals are essential for impactful, lasting research partnerships. Tune in and learn how to leverage research not just for credibility, but for growth, clarity, and competitive advantage in the behavioral health tech landscape! Resources: Connect with and follow Dr. Jennifer Huberty on LinkedIn. Follow Fit Minded on LinkedIn and explore their website. 

You think using AI is your moat? Nope. Just using LLMs isn't enough to power your company's AI success. But do you know the real fuel? Having your data right is the ACTUAL key. So how do you do it? And how does your company's data strategy change with agentic AI? Find out from Deloitte's US Chief Data Analytics Officer, Ashish Verma.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Have a question? Join the convo here.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Transformative Data Strategy for AI SuccessImportance of Data Strategy in AIDeloitte's Data Marketplace ApproachMulti-Agent Orchestration ChallengesStructured vs. Unstructured Data in AISynthetic Data and AI TransformationAgentic AI and Data Labeling EssentialsAI's Impact on Business Value ChainTimestamps:00:00 "AI Success Requires Data Strategy"05:27 Data Integration and Utilization Insights10:31 Contextual Data Marketplace Evolution13:06 Structuring Unstructured AI Insights17:02 Agent Reasoning and Orchestration Insights20:37 Data Annotation Challenges23:39 AI's Impact on Industry Evolution26:09 "Data Strategy: Begin with the End"Keywords:transformative data strategy, AI success, generative AI, non-technical people, data teams, data strategy, business leaders, companies, careers, unedited podcast, livestream, Deloitte, US chief data and analytics officer, data analytics, GenAI, data experiments, third-party data, synthetic data, data marketplace, data concierge, chief data officer, compute environment, deterministic, probabilistic, AI transformation, digital transformation, data minder, CFO, CMO, public domain data, business partner data, metadata, business glossary, technical catalog, agentic AI, multi-agent orchestration, agent registry, agent orchestration, open standard protocols, economic AI, digital transformation strategy, data advantages, structured data, unstructured data, hybrid data, PowerPoint, staffing optimization, resource management, query engine, relevance-ranked search, annotation, data regulation, governance, data procurement, data curation, data feeds, data platforms, information indexing, future predictions.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

On today's episode, Andy and former Green Beret and CIA operative Scott Mann break down reports that the Trump administration is working with Palantir to build a centralized citizen database. With critics from all sides warning of authoritarian overreach, they explore what this means for privacy, national security, and the future of civil liberties in America. Focal Point is an IRONCLAD Original Sponsors:  Marek Health Take control of your health today! Head to https://www.MarekHealth.com and use code FOCALPOINT for 10% off at checkout. Learn more about your ad choices. Visit megaphone.fm/adchoices