Podcasts about cdos

City leaders are on the front lines of data use, but most lack visibility into the federal data landscape, what's available, what's changing, and how federal policy decisions affect local outcomes. This gap delays emergency response, misdirects resources away from high-need neighborhoods, and undermines AI systems that depend on accurate data and community trust. Host Stephen Goldsmith speaks with Denice Ross,  Director of Federal Data Policy at the Federation of American Scientists, about the relationship between local and federal data, what city CDOs should prioritize, and why cities have untapped power to shape federal data policy. In this episode, you'll learn: The often-hidden relationship between local data needs and federal data infrastructure How to identify and access the federal data your city should be using Why now is the time to prepare for Census 2030 and protect funding How community participation in data decisions prevents disparities and builds legitimacy for AI systems How local data leaders can advocate effectively during federal policy windows Guest: Denice Ross –  Director of Federal Data Policy at the Federation of American Scientists; former United States Chief Data Scientist Listener Survey: bit.ly/datasmartpod Music credit: Summer-Man by Ketsa About Data-Smart City Solutions Data-Smart City Solutions, housed at the Bloomberg Center for Cities at Harvard University, is working to catalyze the adoption of data projects on the local government level by serving as a central resource for cities interested in this emerging field. We highlight best practices, top innovators, and promising case studies while also connecting leading industry, academic, and government officials. Our research focus is the intersection of government and data, ranging from open data and predictive analytics to civic engagement technology. We seek to promote the combination of integrated, cross-agency data with community data to better discover and preemptively address civic problems. To learn more visit us online and follow us on LinkedIn.

Every organization is told they need context for AI to work. Almost none of them know where to start. The answer has been sitting in their metadata all along — but most CDOs haven't connected those dots yet.

Most organizations default to replicating data: copying it from source systems into warehouses and lakes so their tools can reach it. Anu Jain, founder and CEO of Nexus One, thinks that's the wrong answer. Malcolm isn't so sure and that's where it gets interesting.

Enterprises have agents. Most can't run them at scale. IBM's Suzanne Livingston explains what changes when you have hundreds — not two.Full Show NotesScaling agentic AI is not the same problem as building it. At IBM Think 2026 in Boston, I sat down with Suzanne Livingston, VP of Product for IBM watsonx Orchestrate, to talk about where enterprise organizations actually are on this journey — and what it takes to move from a pilot to a production environment running hundreds of agents across dozens of departments.Suzanne walks through the full watsonx portfolio, then goes deep on the challenge she hears from customers constantly: the agent worked in the demo, but now it needs to run reliably at scale, with proper governance, observable across the estate, and permissioned correctly for every user and every system it touches. That is a fundamentally different problem than building the agent in the first place. The new Orchestrate Agent Control Plane is IBM's answer to it.This episode is for enterprise technology leaders who have moved past "should we do agents" and are now asking "how do we run them well." If your organization is somewhere between first pilot and full production deployment, this conversation is the one to listen to this week.What We CoverWhy the jump from generative to agentic AI changes the operating model, not just the technologyWhat agent orchestration means in practice when you have 40 sub-agents reporting to one master agentWhat the Orchestrate Agent Control Plane does and why cross-estate visibility matters more than per-agent optimizationHow enterprises are treating AI agents like digital employees — with identities, goals, managers, and performance reviewsWhy governance isn't optional in an agentic environment and what "governance light" looks like for organizations just getting started.Guest BioSuzanne Livingston is Vice President of Product Management for IBM watsonx Orchestrate, IBM's enterprise AI orchestration platform. She leads the product team responsible for agent building, orchestration, evaluation, and the recently announced Orchestrate Agent Control Plane. Suzanne presented at IBM Think 2026 in Boston.IBM Think profile: https://www.ibm.com/think/author/suzanne-livingstonResources MentionedIBM watsonx Orchestrate 30-day free trial: https://www.ibm.com/products/watsonx-orchestrateIBM Think 2026 content: https://www.ibm.com/thinkLopez Research blog: https://www.lopezresearch.com/research/

Katie and Matt discuss GameStop buying (?) eBay, authorized shares, socks, activism, negging, CDOs, CLOs, structured finance innovations, trading private credit, daily pricing, bad ways to own Anthropic stock, suing dentists and human assembly lines.See omnystudio.com/listener for privacy information.

Dell's CTO built a 4-category agent framework from real production deployments. Most enterprises are ignoring two of the categories that matter most.Full Show NotesEnterprise leaders are mapping AI agents to org charts — building digital employees, agentic teams, AI workers — and then wondering why the results fall short. Dell's Global CTO John Roese has been running agents in production long enough to know exactly why that framing fails, and what to do instead.In this episode, Roese shares a framework Dell developed from actual production deployments, not pilots. It identifies four categories of AI agents defined by two dimensions: how much autonomy you grant the agent, and how complex the underlying process is. Most enterprises are focused on one category. Two of the four are widely overlooked — and they may represent the fastest path to measurable ROI.This is a practical, grounded conversation about where agents are actually delivering value today, how to think about infrastructure cost in the context of agent economics, and why the sequence in which you deploy agents matters as much as which agents you build. If your organization is trying to move from AI experimentation to production, this episode is required listening.3. Chapter titles:[00:00] — Introduction: Dell's dual role as tech vendor and enterprise AI user[01:38] — Why the org chart model for agents fails[03:12] — Decoupling human capacity from work capacity for the first time[04:23] — The two-by-two framework: autonomy vs. process complexity[06:14] — Productivity agents: what most enterprises already have[07:00] — Hygiene agents: the overlooked category that fixes foundational data problems[08:01] — The CRM data example: why every CRM is inaccurate and how agents fix it[10:05] — Latent infrastructure capacity: running agents in GPU white space to cut costs to cents[13:53] — Facilitation agents: removing entropy from complex cross-functional workflows[17:30] — The sequencing insight: hygiene and facilitation as the path to expert agents[19:24] — Why coordination agents aren't agentic bosses — and where human control actually lives[22:21] — Roese's closing advice: become literate, pick a few, get them into production4. Guest BioJohn Roese is the Global Chief Technology Officer and Chief AI Officer at Dell Technologies, where he is responsible for technology strategy, AI deployment, and research and development across the company. He has held senior technology leadership roles at Nortel, Enterasys Networks, Broadcom, and EMC. At Dell, he operates at a rare intersection: leading AI strategy for a major technology vendor while also deploying AI internally at enterprise scale — which means his frameworks are tested against real production constraints, not just market positioning.LinkedIn: linkedin.com/in/johnroeseDell Technologies: dell.comAbout This PodcastAI with Maribel Lopez is a podcast for enterprise technology leaders navigating AI adoption, agentic systems, AI infrastructure, and AI governance. Host Maribel Lopez covers enterprise technology and advises CIOs, CDOs, CMOs, and technology vendors on how to move from AI experimentation to measurable business outcomes. New episodes published bi-weekly.Subscribe on your platform of choice: buzzsprout.com/1947446

Podcast Series: Don't Panic It's Just DataGuest: Mark Duffy, Senior Director, Artificial Intelligence & Analytics at Cognizant and Mark Blake, FSI Industry Practice Lead, Stibo SystemsHost: Scott Taylor, The Data Whisperer and Principal Consultant, MetaMeta ConsultingArtificial intelligence (AI) is prevalent in the insurance industry now, but many firms are not seeing the results they expected. The issue isn't with the AI models; it's pertinent to the data.In the recent episode of the Don't Panic It's Just Data podcast, host Scott Taylor, The Data Whisperer and Principal Consultant at MetaMeta Consulting, is joined by Mark Duffy, Senior Director, Artificial Intelligence & Analytics at Cognizant and Mark Blake, FSI Industry Practice Lead at Stibo Systems. The data industry experts address a key misunderstanding about enterprise AI – that companies can innovate their way out of poor data quality. “Some people think AI is a quick fix for data governance,” said host Scott Taylor. “If I need better data, I just use AI.” Experts warn that this belief is what's holding insurers back. How Frankenstein Data is Impacting AI?Despite significant investments in AI, cloud, and analytics, many insurers remain stuck in pilot mode. According to Mark Blake of Stibo Systems, the problem is the infrastructure. “AI itself isn't the challenge,” he said. “It's the ability to scale it, and that comes back to fixing the data.”In reality, most insurance enterprises face fragmented, siloed data across systems. Customer, policy, claims, and product data often don't align. This results in what Taylor calls “Frankenstein data,” where inconsistent records lead to unreliable outputs.For AI to function effectively at scale, insurers need trusted, governed, and unified data. That's where data governance and master data management (MDM) come in.“For us to truly gain benefits from AI, the end user really has to trust the data,” stated Mark Duffy of Cognizant. “That trust comes from having the right data foundation in place.”Also Watch: Can Your MDM Strategy Survive the Shift to Real-Time AI Decision-Making?How Master Data Management (MDM) Unlocks Scalable AI?One of the key drivers of AI success in insurance is multi-domain master data management, a system that connects core business data across the enterprise. “You always have to have a starting point,” Blake explained. “Then you expand horizontally across the enterprise.”The “horizontal data layer” enables insurers to unify key entities like customers, products, and partners—often referred to as the “nouns of the business.” When these are standardised, AI models can work consistently and accurately.The business impact is substantial, including more accurate underwriting decisions, reduced claims leakage, improved customer experience and retention and better cross-sell and upsell opportunities. Duffy shared a real-world example in which enhancing data management directly sped up AI adoption. “It gave them trust in the data,” he said. “They could run models faster and gain more value because they weren't constantly fixing issues.”Instead of spending 80 per cent of their time cleaning data, teams could finally focus on using it.Why AI Is Coercing a Data Strategy ResetFor years, data governance struggled to gain executives' support, but now AI has shifted that.“There's been a refocus,” Blake said. “They're looking at data in a way they maybe haven't done historically.”Today, AI is a priority for boards, driving alignment among CIOs, CDOs, and IT enterprise leaders. “Every C-suite executive wants to do more AI,” Duffy said. “But they've realised they can't do that without the data foundation.”Still, some enterprises believe AI can fix poor data quality. Experts warn that this is a mistake. “You can use AI to support data quality,” Duffy said. “But you're not going to use AI to build an MDM solution.”What's the Solution to Frankenstein DataAs insurers develop their AI strategies for the next 12 to 24 months, one key ideology was spotlighted – success depends less on speed and more on structure. “Go back to the root cause,” Blake said to Taylor. “Fix that, and then you can move forward with confidence.”In other words, AI highlights the need for strong data foundations; it doesn't eradicate them. For insurers serious about AI transformation, that's no longer optional—it's where they must begin.Also Watch: From Chaos to Launch: Your Product is Ready, Your Data Isn'tKey TakeawaysAI in insurance fails without strong data governance and quality foundations.Master Data Management (MDM) is critical for scaling AI across insurance enterprises.Fragmented “siloed data” is the biggest barrier to AI adoption in insurance.Trusted, unified customer and policy data improves AI accuracy and business outcomes.AI cannot fix bad data—insurers must modernise data management first.Chapters00:00 Introduction to AI Readiness in Insurance03:08 The Importance of Data Foundations06:02 Challenges of Fragmented Data09:06 Modernising Data Foundations for AI11:56 Real-World Use Cases in Insurance15:03 The Role of Master Data Management17:56 Aligning Business and Data Strategies21:06 Final Thoughts on AI and Data GovernanceFor more information, please visit em360tech.com and stibosystems.com.To learn more about AI in the MDM space and how they're progressing enterprise analytics intelligently, follow:Stibo Systems LinkedIn: @StiboSystemsStibo Systems X: @StiboSystemsStibo Systems YouTube: @StiboSystemsGlobalEM360Tech YouTube: @enterprisemanagement360EM360Tech LinkedIn: @EM360TechEM360Tech X: @EM360Tech#MasterDataManagement #DataGovernance #AIinInsurance #EnterpriseTech #BigData #DataStrategy #AIReadiness #InsuranceTechnology #cioinsights #StiboSystems #frankensteindatamaster data management, MDM, data governance, AI strategy, insurance, enterprise technology, big data, chief data officer, CDO, CIO, data quality, data unification, Stibo Systems, Scott Taylor, Mark Duffy, Mark Blake

For a hundred episodes, the data world has been told data is the center of the universe. It isn't — and three of the most credible voices in the industry are finally saying so out loud. Malcolm Hawker sits down with Scott Taylor, Juan Sequeda, and Samir Sharma for a milestone roundtable: why AI isn't Hadoop, why CDOs keep failing for the same reasons, and what it's going to take to survive what's coming.

Jonas Deichmann hat 120 Ironman in 120 Tagen absolviert – mehr als jeder Mensch vor ihm. Die Sportmedizin hat gesagt: körperlich unmöglich. In dieser Folge spreche ich mit ihm darüber, welche Daten im Extremsport wirklich zählen, wann Algorithmen wie der Wearable versagen und was CDOs, Datenteams und Führungskräfte vom Mindset eines Weltrekordlers lernen können. Key Takeaways: → Warum Jonas Deichmanns Wearable ihm jeden Tag '0% Erholung' gesagt hätte – und warum das irrelevant war → Die 2 Datenpunkte, die über 120 Tage wirklich über Erfolg und Verletzung entschieden haben (Spoiler: nicht Watt, nicht VO2max) → Wie sich sein Ruhepuls während des Projekts von 37 auf 70 erhöhte – und der Maximalpuls von 190 auf 115 fiel → Warum klassische Trainingspläne und Tour-de-France-Algorithmen für so ein Projekt scheitern mussten → Wie Deichmann Gewohnheiten designt – und wie Vertriebs-Teams genau dasselbe Prinzip nutzen können → Warum der Kopf entscheidet, wann die Erschöpfung kommt – und was das für langfristige High-Performance heißt Über den Gast: Jonas Deichmann ist Extremsportler, Abenteurer und Keynote-Speaker. Er hat die Welt mit dem Rad umrundet, die Fahrrad-Weltrekorde für alle Kontinente gebrochen und in 120 Tagen 120 Ironman-Distanzen absolviert – Weltrekord. Seine Netflix-Doku 'Das Limit bin ich' zeigt das Projekt. MY DATA IS BETTER THAN YOURS ist ein Projekt von BETTER THAN YOURS, der Marke für richtig gute Podcasts.

This week on the GovNavigators Show, hosts Adam and Robert sit down with Dr. Amanda Cash of the Data Foundation and Dr. Adita Karkera of Deloitte to unpack the latest Federal Chief Data Officer (CDO) Survey and what it reveals about the state of data, AI, and capacity across government.Drawing on six years of survey data, Amanda and Adita explain how the federal CDO role has evolved since the Foundations for Evidence-Based Policymaking Act and why today's environment may be the most challenging yet. With more than half of CDOs operating with five or fewer staff, agencies are being pushed to do more with less, even as expectations around AI, data governance, and transparency continue to rise.The conversation explores the growing overlap between Chief Data Officers and Chief AI Officers, the risks and opportunities of combining those roles, and how agencies can use AI to compensate for workforce gaps. They also highlight the critical role of the federal CDO Council in enabling collaboration and scaling best practices across government.Show Notes:Check out the CDO Survey hereCDO Survey webinar recordingWhat's on the GovNavigators' Radar:Apr 26 – 28: NASCIO's mid year conferenceApr 30: Fed100 Evening of Honors

Welcome to another episode of Data Debrief, the companion show to Driven By Data: The Podcast, where hosts Catherine Dowden-King and Kyle Winterbottom sit down to unpack Tuesday's conversation, share what's been on their minds, and explore what's really happening across the data and AI landscape.Fresh off Kyle's return from holiday, the pair dive into Tuesday's episode with Daragh Kelly, Chief Data Officer at The Economist, unpacking the ideas that stood out most, and a few that challenge the dominant narratives in the market right now.They cover:Why the concept of “Trad AI” (traditional machine learning and data science) is a useful lens, and how the market is blurring the lines between legacy AI and the new wave of generative and agentic capabilitiesThe ongoing hype cycle in AI, why it's nothing new, and how organisations risk getting distracted by buzzwords rather than focusing on real outcomesThe growing gap between building AI solutions and making them scalable, reusable, and commercially viableThe importance of defining what “AI” actually means inside your organisation, and why vague language is creating confusion at the board levelThe tension between speed and direction, and why moving fast means nothing if you're not solving problems that actually matterWhether operating models really need to change for AI, and why Dara's perspective challenges the prevailing narrativeThe shift from analysts as insight generators to “toolmakers”, and what that means for the future of data and analytics rolesThe rise of self-serve capability across organisations, and the risks of uncontrolled experimentation without governanceThe ongoing power struggle between CDOs, CIOs, and CTOs over AI ownership, and why the answer is far from settledThe role of optics, titles, and external brand in shaping career progression for data leaders in an AI-first marketPlus, in this week's Thoughts of the Week, Kyle challenges the long-standing narrative around “having a seat at the table,” arguing that it's often used as an excuse for not delivering value, and that true impact comes from driving outcomes, regardless of reporting lines. Catherine reflects on the role of diversity, equity, and inclusion in the data community, why the conversation is still far from where it should be, and the responsibility leaders have to actively shape a more inclusive industry.Like and subscribe wherever you listen, and if you've got a question or topic you'd like the team to cover, email community@orbitiongroup.com

Podcast: Don't Panic It's Just Data!Guest: Adrian Estala, VP, Field Chief Data & AI Officer, StarburstHost: Doug Laney, Research & Advisory Fellow at BARC and Author of Infonomics & Data JuiceAfter years of heavy investment in data lakes and warehouses, many enterprises still face a frustrating reality. Insights continue to remain slow, fragmented, and hard to trust.In the recent episode of the Don't Panic It's Just Data podcast, host Doug Laney, Research & Advisory Fellow at BARC and Author of Infonomics & Data Juice, is joined by Adrian Estala, VP, Field Chief Data & AI Officer at Starburst. They sat down to discuss why more enterprises are adopting a new architectural approach, the business semantic layer, to speed up AI adoption.What's the Core Issue in AI Data Enterprise?The core issue, Estala argues, is not a lack of infrastructure but an inconsistency between how data is organised and how enterprises think. “No one's really there yet,” he says, reflecting on a decade of backend optimisation. “We don't know what ‘perfect' architecture means, especially in the AI age.”The semantic layer, sometimes called a “context layer,” represents a shift from technical complexity to business usability. Typically, the system requires non-technical users to interpret schemas and pipelines; however, Starburst provides an abstraction that shows data in familiar business terms, along with metadata and governance rules.“If you build it right,” Estala explains, “when a CFO walks in the room and sees their semantic layer, it makes sense to them.”For an enterprise, this is more than just a usability improvement. It reduces duplication, eliminates conflicting metrics, and reduces reliance on IT teams for routine analysis. As Laney notes during the discussion, the goal is not to replace existing systems but to make them “that much more accessible” by layering business meaning on top.Also Watch: AI Is Replacing BI — Here's What CIOs Need to KnowSovereignty, Governance & the European RealityThe conversation is even more acute in regions like Europe, where data sovereignty has become a major concern. Regulatory pressure has led enterprises to rethink not only where data is stored but also how it is accessed and shared.Estala describes a federated model where data stays within national boundaries while still being usable globally. Organisations set up local clusters in countries like Switzerland or the United Kingdom, build data products locally, and apply strict rules for what can be shared centrally.“I can decide which data products are approved to be shared,” he says, alluding to compliance mechanisms that ensure sensitive information cannot be traced back to individuals.This creates a system that satisfies both regulators and business leaders. Executives no longer need to worry about jurisdictional complexities; they work with a unified view of data that has already been filtered, governed, and approved. “For them, it just feels like it's already been brought together,” Estala adds.As AI agents and copilots continue to gain popularity, the discussion also spotlights limitations. One such limitation is trust. Without confidence in the underlying data, even the most advanced AI tools struggle to provide meaningful value.“If they don't trust the answers, it's just a cool toy,” Estala says, describing a common pattern where initial excitement fades once users doubt the reliability of outputs.The semantic layer also tackles this discrepancy by embedding governance, lineage, and business rules directly into data products. Starburst helps enterprises clearly define which data is exposed to AI systems and under what conditions, making it easier to explain and justify decisions.Currently, Estala observes, AI mainly speeds up existing workflows instead of transforming them. Executives are asking the same questions they always have, but getting answers faster and from broader datasets. The real change, he suggests, will come when trust allows leaders to ask entirely new questions and rethink decision-making.How to Drive Business Value in 90 Days?For CIOs and CDOs eager to move past experimentation, the Chief Data and AI officer outlines a focused, business-led approach. Rather than launching large-scale transformations, he suggests starting with a single domain and building momentum from there.The first phase focuses on collaboration, bringing business stakeholders into the design of the semantic layer and defining the data products that are most important. “We design it with the business team in the room,” he explains, stressing ownership from the start.The next stage shifts to enablement, as teams begin to use and expand these data products themselves. This is where self-service takes root, reducing dependence on IT and promoting more exploratory use of data.By the final phase, enterprises are ready to introduce AI agents on top of a trusted foundation. At that stage, technology becomes almost secondary. “Once you get to a semantic layer that you trust, adding an agent is easy,” Estala says.As enterprises continue to adopt AI at larger scales, their competitive edge will come from algorithms and from how effectively they organise, govern, and contextualise their data. In this sense, the semantic layer is quickly becoming the backbone of modern, AI-driven decision-making.Key TakeawaysSemantic layers make governed data accessible for enterprise AI.Data sovereignty drives federated, compliant data architectures.Trusted AI needs governed, metadata-rich data products.Semantic layers deliver business value within 90 days.Virtual layers reduce duplication and speed up analytics.Chapters00:00 The Shift to Business Semantic Layers08:02 Data Sovereignty and Governance in Modern Strategies13:08 Foundational Capabilities for AI Systems18:11 AI Agents and Decision Making23:04 Practical Steps for Implementing Semantic LayersTo learn more about how data products and AI agents are changing enterprise analytics, follow:Starburst LinkedIn: @StarburstStarburst X: @starburstdataStarburst YouTube: @StarburstDataEM360Tech YouTube: @enterprisemanagement360EM360Tech LinkedIn: @EM360TechEM360Tech X: @EM360TechFollow: @EM360Tech on YouTube, LinkedIn and XStay connected for more expert insights, podcast episodes, and enterprise data strategy discussions.#SemanticLayer, #DataGovernance, #EnterpriseAI, #DataStrategy, #DataArchitecture, #AIatScale, #Compliance, #DataSovereignty, #ContextLayer, #AIagents, #DataProducts, #SelfServiceAnalytics, #CIO, #CDO, #Starburst, #AdrianEstala, #DougLaney, #DontPanicItsJustData, #EM360Tech, #TechPodcast

Data governance is the foundation of enterprise AI. If your data is not AI-ready, your copilots, agents, and automations can return bad answers, expose risk, and make the wrong decisions faster.In this episode of the Mostly Unstructured Podcast, Clay and Ed break down why enterprise AI success isn't just about model performance, but starts with data readiness, traceability, audit trails, validation, policy, and clear ownership across the business.Read our KeyMark companion article:https://www.keymarkinc.com/managing-a...Topics explored in this episode:• What data governance for AI actually means• Why many AI failures start with governance failures• How bad data, shadow AI, and weak controls create enterprise risk• Why traceability, monitoring, auditing, and validation matter before agents make decisions• How bias, compliance, privacy, and trust affect enterprise AI rollouts• What CIOs, CDOs, IT leaders, operations leaders, and compliance teams should ask before scaling AIIn this episode, Clay and Ed address key AI questions:• What is data governance for AI?• Why is data governance important for enterprise AI?• What makes enterprise data AI-ready?• Who owns AI governance in an organization?• How do you reduce AI risk without slowing innovation?• How do you govern agentic AI responsibly?If you are evaluating enterprise AI, agentic AI, intelligent document processing, or AI automation, this episode directs seekers in establishing smart AI beginnings with data governance for accurate data, and AI governance for output guardrails.

Welcome to the very first episode of Data Debrief, the companion show to Driven By Data: The Podcast, where hosts Kyle Winterbottom and Catherine Dowden-King sit down every Thursday to unpack Tuesday's episode, share what's been on their minds, and discuss what's really happening across the data and AI market.In this debut episode, Kyle and Catherine reflect on Tuesday's conversation with Kevin Cassar, diving deeper into the themes that sparked the most discussion, and a few that didn't make it into the main episode.They cover:Why the data conversation is shifting from delivering ROI to accelerating it, and what that means for data leaders still stuck in output-factory modeThe growing pressure on organisations to be seen doing AI, regardless of whether it's actually delivering value, and why the optics of inaction are now costlier than wasting budgetThe internal land grab between CDOs and CIOs over who owns AI, and why the answer is often more cultural than technicalWhy rolling out enterprise ChatGPT to an entire bank doesn't necessarily mean your organisation is embracing AIKevin's people-first philosophy, and why understanding where your team actually sits on the AI adoption curve matters more than having an aggressive strategyThe evolving role of the data professional: from coder to builder, from report-puller to AI quality controllerPlus, in their new Thoughts of the Week segment, Catherine raises a question that's hard to shake: are we over-optimising our downtime? From boiling water taps to washing up, she makes the case that friction isn't always the enemy, and that some of our best thinking happens precisely when we're not being productive. Kyle agrees and admits most of his best ideas arrive at 3am or are written on a shower panel.Like and subscribe wherever you listen, and if you've got a question or topic you'd like Kyle and Catherine to cover, email the team at community@orbitiongroup.com

Don and Tom kick things off with a colorful history lesson on 19th-century “bucket shops,” drawing a sharp parallel to today's emerging world of tokenized securities—digital representations of stocks traded on blockchain platforms. While proponents tout 24/7 trading and faster settlement, the hosts question the real value, highlighting added complexity, thin trading, pricing deviations, and unclear ownership structures. They frame tokenized investing as a solution in search of a problem—one that primarily serves speculators rather than long-term investors. The episode reinforces a familiar theme: avoid unnecessary complexity, ignore trading temptations, and stick with disciplined, low-cost investing. Listener questions cover whether retirees still need life insurance (generally no, if financially secure) and clarify that rebalancing means selling winners and buying laggards—not chasing losses.0:05 Intro and setup with historical market story0:24 Bucket shops explained—early stock market gambling1:50 Transition to modern “tokenized securities”2:35 What tokenized stocks are and how they trade 24/75:27 Blockchain explained in plain English6:23 Ownership confusion—what do you actually own?7:53 Custodian risk and structural concerns8:33 Pricing issues and thin trading risks9:01 Tokenization compared to past financial “innovations” (CDOs)10:54 Why investors should ignore tokenized securities11:26 New call-in system for podcast listeners12:03 Listener question: keep or drop term life insurance in retirement13:02 Why life insurance is unnecessary for financially secure retirees15:05 Listener question: selling losers vs. rebalancing16:05 Proper rebalancing strategy explained (sell high, buy low)17:31 Jack Bogle philosophy—do less, win moreQuestions? Comments? Click!

Episode OverviewIn this episode of CDO Matters, Malcolm Hawker sits down with Yext Chief Data Officer Christian Ward to explore how AI is fundamentally reshaping the relationship between data and modern marketing. As traditional playbooks built around search, SEO, and paid media begin to fracture, the conversation dives into what this shift means for CMOs—and why CDOs must step into a far more consultative and strategic role in guiding how organizations prepare their data for an AI-mediated customer journey. The result is a thoughtful discussion on the emerging data realities behind AI-driven discovery, and what data leaders must do today to ensure their marketing partners remain visible, relevant, and competitive in an AI-first world. Episode Links and ResourcesFollow Malcolm Hawker on LinkedInFollow Christian Ward on LinkedIn

Enterprise AI budgets are climbing, but the data foundations beneath them remain uneven. In this episode of Don't Panic, It's Just Data, Kevin Petrie, VP of Research at BARC, and Nathan Turajski, Senior Director, Product Marketing at Informatica, examine the findings of the CDO Insights 2026 report, which argues that executive confidence in AI may be outpacing organisational readiness. The study centres on what it describes as a growing “trust paradox” as Chief Data Officers are accelerating AI initiatives even as data quality, governance maturity, and AI literacy struggle to keep up. The Trust ParadoxThe report exposes a striking disconnect. Turajski points out that while around 65 per cent of data leaders believe employees trust the data powering AI, 75 per cent say upskilling in data and AI literacy is essential. In other words, confidence is high, but readiness is lagging.This is the trust paradox where employees increasingly rely on AI outputs, while data leaders remain cautious about the quality, governance, and lineage behind those results. The risk is not scepticism but rather overconfidence. When AI-generated answers are accepted without scrutiny, flawed data can quietly scale poor decisions. For CDOs, the challenge is cultural as much as technical.AI Adoption Soars While Data Readiness LagsThe harsh reality is that AI experimentation is no longer confined to innovation teams. It's spreading across marketing, operations, finance, and customer experience. As a result, scaling from pilot to production requires more than a model and a use case. To make AI work at scale, organisations need a data strategy that ensures consistency across domains, clear and transparent governance, measurable business impact, and sustainable management of their data assets.Data Quality and GovernanceTurajski explains that organisations are increasingly investing in data management and governance, with 86 per cent expanding data initiatives and 39 per cent prioritising upskilling. Metadata integration also helps unify distributed environments, providing the context AI needs to deliver reliable, trustworthy outputs. Organisations need to remember that AI systems amplify whatever they are given, so if inputs are inconsistent, incomplete, or poorly defined, outputs will reflect those weaknesses which are often at scale. Data quality challenges frequently arise from duplicated or conflicting records, inconsistent definitions across business units, poor lineage visibility, and limited ownership accountability. For example, a retailer might describe the same product in multiple ways across systems. Without standardisation, AI tools trained on that data produce fragmented insights, and when this occurs across thousands of products and regions, the distortions multiply. The takeaway from data leaders is clear: AI performance cannot be separated from disciplined, high-quality data management.Upskilling and Scaling AI AdoptionBoth Petrie and Turajski stress that technology alone won't close the gap. Upskilling employees in data literacy, AI fluency, and governance awareness ensures AI experimentation evolves into measurable, real-world results from improved customer experience to faster, more accurate analytics. The 2026 CDO Insights findings position data leaders at the centre of AI transformation. Their mandate extends beyond infrastructure to trust architecture. The trust paradox isn't a reason to slow down innovation. It's a reminder that lasting results require as much discipline as ambition. In 2026, the organisations that succeed won't be the fastest to adopt new technologies, but those that build the most reliable data foundations to support them.To learn more about this, visit informatica.comTakeawaysThe trust paradox highlights a disconnect between employee confidence in AI and leadership's caution.Data leaders recognise the need for upskilling in data and AI literacy.Building a trusted context is essential for effective AI adoption.The vendor landscape for data management is complex and requires careful navigation.AI is being used to enhance customer experience and loyalty.Measurable results from AI adoption are becoming a priority for organisations.Data governance must keep pace with AI use to mitigate risks.Successful organisations are leveraging unified data management platforms to drive AI value.Chapters00:00 Introduction to the CDO Insights Report03:13 Understanding the Trust Paradox in AI Adoption08:34 Building Trusted Context for AI14:11 The Importance of Data Quality and Completeness20:28 Navigating the Vendor Landscape for Data Management23:09 From Experimentation to Measurable Results27:38 Recommendations for CDOs and CISOs

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.

Interim leadership is no longer the exception in nonprofits, it's becoming the norm. As executive tenures shrink and pressure mounts, organizations increasingly rely on interim CEOs, CDOs, and senior leaders to stabilize, reset, or prepare for what's next. But interims don't just affect the C-suite; they reshape staff behavior, donor confidence, and organizational momentum. In this episode, Randall breaks down the three true roles of interim leaders (caretaker, stabilizer, and change agent) and explains what success actually looks like for both the interim and the team navigating the uncertainty. Whether you are the interim or reporting to one, this episode offers practical clarity when leadership feels temporary but the mission isn't.

What happens when leaders are confident about AI, but the people expected to use it are not ready? In this episode of Tech Talks Daily, I sat down with Caroline Grant from Slalom Consulting to explore one of the most persistent tensions in enterprise AI adoption right now. Boards and executives are spending more, moving faster, and expecting returns sooner than ever, yet many organizations are struggling to translate that ambition into outcomes that scale.  Caroline brings fresh insight from Slalom's latest research into how leadership, culture, and workforce readiness are shaping what actually happens next. We unpack a clear shift in ownership for AI transformation, with CTOs and CDOs increasingly leading organizational redesign rather than HR. That change reflects how deeply AI now cuts across technology, operations, and business models, but it also introduces new risks.  Caroline explains why sidelining people teams can create blind spots around skills, incentives, and trust, especially as roles evolve and uncertainty grows inside the workforce. The result is what Slalom describes as a growing AI disconnect between executive optimism and day-to-day reality. Despite the noise around job losses, the data tells a more nuanced story. Many organizations are creating new AI-related roles at a pace, yet almost all are facing skills gaps that threaten progress. We talk about why reskilling at scale is now unavoidable, how unclear career paths fuel employee distrust, and why focusing only on technical capability misses the human side of adoption.  Caroline also challenges assumptions about skill priorities, warning that deprioritizing empathy, communication, and change leadership could undermine effective human-AI collaboration. We also dig into ROI expectations, with most UK executives now  expecting returns within two years. Caroline shares why that ambition is achievable, where it breaks down, and why so many organizations remain stuck in pilot mode. From governance and decision rights to culture and leadership behavior, this conversation goes beyond tools and platforms to examine what separates experimentation from fundamental transformation. As AI becomes a test of leadership as much as technology, how are you closing the gap between vision and execution within your organization, and are you building a workforce that can keep pace with change rather than resist it? Connect With Caroline Grant from Slalom Consulting The Great AI Disconnect: Slalom's Insights Survey Learn More About Slalom

Episode OverviewIn this episode of CDO Matters, Malcolm Hawker sits down with Sarah Levy, the CEO of Euno, to unpack why traditional data governance is collapsing under the weight of AI. They explore how context, metadata, and probabilistic thinking are redefining what “AI-ready” really means - and why CDOs who don't adapt quickly risk becoming irrelevant.Episode Links and ResourcesFollow Malcolm Hawker on LinkedInFollow Sarah Levy on LinkedIn

On today's manager meeting, Kristen VanGelder speaks with Jonathan Lewinsohn. Kristen is Deputy Chief Investment Officer at Evanston Capital, a $4 billion hedge fund of funds whose CEO and CIO, Adam Blitz, was a past guest on the show. She's spent the last eighteen years at Evanston alongside Adam and the team. Jonathan co-founded Diameter Capital four years ago alongside Scott Goodwin and today manages a $6 billion credit-focused hedge fund alongside $1 billion in CDOs and a $1 billion drawdown fund. The two were colleagues at Anchorage Capital, and Jonathan spent some time at Centerbridge Capital as well before starting Diameter.   Their conversation includes insights into the credit markets, Diameter's approach, and how it all comes together. Before we dive in, Kristen and I discuss how Evanston came to back Diameter on day one and how it fits into their portfolio.   Learn More Follow Ted on Twitter at @tseides or LinkedIn Subscribe to the mailing list Access Transcript with Premium Membership   Editing and post-production work for this episode was provided by The Podcast Consultant (⁠https://thepodcastconsultant.com⁠)

A Conversation with Joe Santana; a DEI original Would you agree that most conversations about DEI today sound loud, polarized, and disconnected from the work itself? In this episode of Everyday Conversations on Race, I talk with Joe Santana—advisor, author, and long-time DEI consultant—about where Diversity, Equity, and Inclusion actually came from and how it was originally practiced inside organizations. What really is DEI, (Diversity, Equity, and Inclusion)? Joe and I have both spent decades doing this work. We've watched DEI evolve, get renamed, repackaged, misunderstood, and in some cases quietly dismantled. What often gets lost is that DEI didn't start as a political position. It started as a business conversation—about how organizations function, how people are evaluated, and how talent is either used or ignored. What is the business case for DEI?  Why are people still talking about diversity, equity, and inclusion? The early thinking behind DEI and why it mattered to organizational performance How good intentions gave way to vague language and inconsistent practice What happens when leaders avoid difference instead of learning how to work with it Why "treating everyone the same" sounds fair but rarely works How Employee and Business Resource Groups can either matter—or miss the point entirely This is a grounded conversation between two practitioners reflecting on what we've learned, what we got wrong, and what still holds value—especially for leaders trying to make sense of the current moment. You'll learn more about the challenges, and strategic importance of Diversity, Equity, and Inclusion (DEI) in organizations. From the historical context provided by pioneers like Roosevelt Thomas to practical advice on optimizing business outcomes, Joe shares a wealth of knowledge on how DEI can drive both social good and financial success in companies. The episode also covers the vital role of Employee Resource Groups (ERGs) and what organizations can do to leverage them effectively. You'll gain valuable insights on turning DEI initiatives into strategic business tools. If you're looking for clarity instead of slogans, and experience instead of soundbites, you'll find it in this episode.   Guest Bio Joseph (Joe) Santana is a business strategy coach and futurist specializing in developing CDOs, ERG/BRG leaders, and Executive Sponsors who drive measurable business impact. He is an author, keynote speaker, and member of the Forbes Business Council and the Fast Company Executive Board and a frequent contributor to articles in both organizations' magazines. His insights and ideas have been shared globally in interviews with media outlets such as ABC, PIX, Fox, Ticker News, and The Black List, a streaming business interview show. His two most recent books, "The New DEI and ERG Frontier" and "SuperCharge Your ERGs," are available on Amazon, offering invaluable guidance to those ready to embark on the journey toward 21st-century business-impacting success. As CEO of Joseph Santana, LLC, an Inc Verified company, he leads multiple brands focused on equipping CDOs and ERG/BRG Chairs in national and global enterprises with the skills and strategies needed to enhance organizational performance. Below is a graphic depiction of the brands owned by Joseph Santana, LLC.   Click here to DONATE and support our podcast All donations are tax deductible through Fractured Atlas. Simma Lieberman, The Inclusionist, helps leaders create inclusive cultures. She is a consultant, speaker, and facilitator. Simma is the creator and host of the podcast, Everyday Conversations on Race. Contact Simma@SimmaLieberman.com to get more information, book her as a speaker for your next event, help you become a more inclusive leader, or facilitate dialogues across differences. Go to www.simmalieberman.com and www.raceconvo.com for more information Simma is a member of and inspired by the global organization IAC (Inclusion Allies Coalition)    Connect with me: Instagram Facebook YouTube Twitter LinkedIn Tiktok Website Previous Episodes Curiosity, Not Cancellation: Real Talk with Dr. Julie Pham Voices of Triumph: Stories of African Women Immigrants in America Black Health Matters: Community, Data, and the Journey to Wellness with Kwame Terra Loved this episode?  Leave us a review and rating

Samantha cuts through the AI hype to discuss what businesses are really experiencing, including thoughts on why investment in AI is high, yet value remains elusive - highlighting challenges with legacy technology, data, governance and workforce readiness.   Emphasising the crucial role of HR in shaping the future of work, working alongside tech and data leaders and the importance of cross-functional collaboration, Samantha highlights a striking insight from Slalom's research: empathy and communication are being deprioritised, even as these human skills become more important for trust, adoption and sustainable change.   Introducing the concept of AQ and the SHIFT framework, Samantha shows how organisations can build human and cultural foundations adept at dealing with continuous change, including AI adoption.   Her practical call to action for HR leaders: connect with your CTOs and CDOs, learn from each other, and help lead a future of work that balances technology with human needs.   How is AI really playing out? Slalom's new research Thank you to Slalom for sponsoring this week's podcast episode. Slalom is a business and technology consulting firm that believes meaningful change starts with people, helping organisations turn change into outcomes that actually last. If you're an HR leader navigating AI and wondering how to move from ambition to adoption, Slalom's latest research offers practical insights you can use right away. Slalom surveyed more than 2,000 global executives to understand how AI is really playing out, where investment is translating into value, where it isn't and, what that means for leadership, skills and cross functional alignment. Download your free summary here: Get Slalom's latest AI research Thanks again to Slalom for supporting the podcast, and thank you for being a brilliant HR Changemaker, we're excited to create more positive change together this year.

In dieser Folge des Venture AI Podcasts spricht Norman Müller mit Dr. Annette Doms über die eigentliche Leerstelle der KI-Debatte. Es geht nicht um Modelle, sondern um Vertrauen. Nicht um Tools, sondern um Infrastruktur. Annette verbindet Kunstgeschichte mit KI-Strategie und zeigt, warum Deutschland mit Verantwortung, Präzision und kultureller Tiefe eine führende Rolle im globalen KI-Zeitalter einnehmen könnte.Das Gespräch spannt den Bogen von Gutenberg bis ChatGPT, von Blockchain als Vertrauensinfrastruktur bis zur Frage nach Superintelligenz. Eine Folge für CEOs, CIOs und CDOs, die verstehen wollen, warum KI Transformation kein Technologieprojekt ist, sondern eine Führungsentscheidung.Zu den Shownotes---Wenn du uns dabei unterstützen möchtest, diesen Podcast zu einer Allianz von Zukunftsarchitekten der KI-Transformation zu machen, in der wir offen über Chancen, Risiken und reale Erfahrungen mit Künstlicher Intelligenz sprechen, dann abonniere uns auf YouTube, Spotify oder Apple Podcasts. Dein Abonnement kostet dich nichts, hilft uns aber sehr, noch mehr herausragende Persönlichkeiten für tiefgehende und inspirierende Podcast Gespräche zu gewinnen. Vielen Dank für deinen Support.Darüber hinaus laden wir dich ein, Teil der Plattform des Bundesverbands für KI-Transformation e.V. zu werden. Hier vernetzen sich mittelständische Unternehmen, KI Expertinnen und Experten, Startups sowie Vertreterinnen und Vertreter aus Forschung und Wissenschaft, um Wissen zu teilen, Erfahrungen auszutauschen und um an konkreten KI-Projekten zu arbeiten. In unserer Podcast Community kannst du dich einbringen, mitdiskutieren und den Bundesverband als Mitglied aktiv unterstützen und mitprägen.Zur Plattform:https://www.venture-ai-germany.spaceVernetze dich mit Norman auf LinkedIn:https://www.linkedin.com/in/muellernorman

Five years ago, we started Leader Generation with a simple premise: clients learning from clients. This year proved why that matters. In 2025, CEOs and their leadership teams stopped debating whether to adopt AI and began wrestling with harder questions: How do we bring our people along? How do we measure what matters instead of what's easy to measure? How do we maintain the relationships that built our business while transforming how we operate? Excerpts from these ten conversations capture what worked. Not theory—actual results from CDOs, COOs, and VPs leading change at companies like Southern Glazer's Wine & Spirits, DoubleVerify, and Digital Remedy. The full episodes are linked in this post. Stuart Goldstein explains why technology is the easy part, and people are where transformations actually stall. Alan Wizemann describes how his team measures success by the time freed up for employees to do valuable work, not headcount reduction. Anna Jankowska walks through preparing teams for scenarios they don't expect, because that's where you see who's ready to lead. These aren't polished case studies. They're candid conversations about the messy reality of leading change when your board wants results, your team is exhausted, and the playbook keeps changing. If you're heading into 2026 with aggressive goals and limited patience for more consultants telling you what you already know, these episodes cut through the noise. They're built for leaders who need to make decisions Monday morning, not attend another conference about the future of work. Links to the full episodes from this top 10 list: #10: https://tenloradio.com/e/ep129-building-your-army-of-ai-agents-what-marketers-need-to-know/ - Fabio Fiss, Aaron Grando, and Javier Lopez #9: https://tenloradio.com/e/ep109-ai-data-marketing-how-to-manage-risks/ - Kevin Purvis #8: https://tenloradio.com/e/ep116-future-of-performance-marketing-data-ai-personalization/ - Jeremy Haft #7: https://tenloradio.com/e/ep126-how-scibids-ai-is-redefining-media-buying-at-doubleverify/  - Wadrille Leroy #6: https://tenloradio.com/e/ep112-leading-through-change-the-human-side-of-marketing-leadership/  - Anna Jankowska #5: https://tenloradio.com/e/ep108-ai-playground-microsoft-copilot-google-notebooklm/ - Alyssa Curci and Jonathan Murray #4: https://tenloradio.com/e/ep140-leading-change-that-sticks-people-processes-platforms-with-stuart-goldstein/ - Stuart Goldstein #3: https://tenloradio.com/e/ep143-time-to-value-innovation-that-puts-customers-first-with-alan-wizemann/ * Alan Wizemann #2: https://tenloradio.com/e/ep119-leading-change-transforming-companies-with-alan-wizemann/ - Alan Wizemann #1: https://tenloradio.com/e/ep121-say-hello-to-brand-agent-ai-that-speaks-your-brand-s-language/ - Aaron Grando   About Tessa Burg: Tessa is the Chief Technology Officer at Mod Op and Host of the Leader Generation podcast. She has led both technology and marketing teams for 15+ years. Tessa initiated and now leads Mod Op's AI/ML Pilot Team, AI Council and Innovation Pipeline. She started her career in IT and development before following her love for data and strategy into digital marketing. Tessa has held roles on both the consulting and client sides of the business for domestic and international brands, including American Greetings, Amazon, Nestlé, Anlene, Moen and many more. Tessa can be reached on LinkedIn or at Tessa.Burg@ModOp.com.

What do a 1970 psychology experiment and the 2008 housing crash have in common? In Episode 6 of Built to Divide, Dimitrius Lynch traces how social identity theory—the instinct to form “us vs. them” groups—became a political weapon that helped sell a bipartisan push for mass homeownership, weaken skepticism, and pave the way for subprime mortgages, mortgage-backed securities (MBS), CDOs, and a crisis engineered by incentives.We move from NAFTA-era globalization and Peter Drucker's “core competencies” mindset, to the dot-com bust, Fed rate cuts, and the explosion of “stated income” lending. The episode spotlights Washington Mutual (WaMu)—from community-friendly bank to shareholder-driven mortgage machine—then follows the collapse, the scapegoating of low-income borrowers, and the rise of institutional investors turning foreclosures into portfolios. A story about housing, finance, and the narratives that keep us divided—even when the math says we share the same stakes.Episode Extras - Photos, videos, sources and links to additional content found during research.Episode Credits:Production in collaboration with Gābl MediaWritten & Executive Produced by Dimitrius LynchAudio Engineering and Sound Design by Jeff Alvarez

GreenLite delivers private construction plan review as an alternative to traditional city permitting processes. After spending six months testing both sides of the construction permitting transaction, the company identified owner-developers as their ICP and built a business model around Florida's privatization legislation—legislation that has now expanded to nine additional states including Texas, Tennessee, and California. In this episode of BUILDERS, we sat down with James Gallagher, CEO and Co-Founder of GreenLite, to explore how his fifth startup leveraged regulatory shifts, rejected workflow software in favor of outcomes, and scaled by targeting chief development officers at enterprise retailers struggling with permitting delays. Topics Discussed: How GreenLite discovered architects were heavy users but wrong customers due to two-part sales dynamics Why owner-developers became the ICP after six months of customer discovery across applicants and agencies The accidental discovery of private plan review through conversations with Fort Worth and Miami-Dade agencies GreenLite's platform combining regulatory permissions, licensed AEC professionals, and AI-augmented software How natural disasters and AEC talent shortages are accelerating privatization legislation nationwide Cold email strategies that converted enterprise retailers by surfacing acute pain points GTM Lessons For B2B Founders: Map two-sided markets to find where purchasing authority and pain intersect: GreenLite pitched a CTO at a major architecture firm who responded positively but said "I just need to talk to my client, my customer." This revealed architects required approval from owner-developers despite being the heaviest product users. James pivoted to owner-developers who "carry the land, carry the construction loans" and feel revenue delays most acutely. The lesson: usage intensity doesn't equal buyer authority. In complex ecosystems, systematically test which party controls budget and feels enough pain to sign contracts independently. Recognize when procurement cycles kill early-stage validation velocity: Cities explicitly told James their "crazy procurement cycles" made early partnership impractical despite genuine interest. State and local education and government sales require specialized expertise and extended timelines that prevent rapid iteration. James chose to prove the model with private sector customers first. For founders: government can be a lucrative eventual market, but unless you have sled sales expertise and 12+ month runway per deal, validate PMF elsewhere first. Capitalize on regulatory tailwinds before markets realize they exist: Only Florida permitted private plan review when GreenLite launched in July 2022. By late 2024, nine states passed enabling legislation driven by natural disaster reconstruction needs and talent shortages in city building departments. James positioned GreenLite to ride this wave rather than selling transformation to resistant agencies. Founders should monitor legislative and regulatory changes in their verticals—new compliance requirements or permissions can suddenly open massive TAMs with minimal incumbent competition. Enterprise cold email converts when you surface non-obvious acute pain: GreenLite cold emailed chief development officers at major retail chains and quick-service restaurants with "Are you missing your openings due to permitting?" The response rate validated that permitting delays—not site selection or construction costs—were a critical path blocker for store rollout velocity. James targeted CDOs rather than real estate or design teams because they own the full development timeline. For enterprise sales: identify the executive accountable for the metric your solution impacts, then lead with how you move that specific number. Validate outcome-based models before building sophisticated workflow tools: GreenLite's customers rejected "another workflow product or system of record" that required API integrations with their ERPs and construction management systems. Instead, they wanted "faster, more predictable, more transparent permits." James built a viable business delivering finished permits through licensed professionals augmented by software, with the AI sophistication coming later. The business was "super viable well before the product was" by early 2023. For founders in industries resistant to software adoption: test whether buyers want tools to operate or outcomes to purchase—outcome-based pricing can achieve PMF faster and command premium willingness-to-pay. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role.  Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

While the role of a chief data officers (CDOs) was traditionally focused on regulatory compliance, it has now expanded to empowering the consistent and effective use of data across organizations to improve business outcomes. One of the most effective ways for CDOs to demonstrate their value is by developing a data strategy that is closely aligned with business goals, processes, and outcomes. In the latest episode of Tech Transformed, host Kevin Petrie, VP of Research at BARC, speaks with Brett Roscoe, Senior Vice President and GM of Cloud Data Governance and Cloud Ops at Informatica, about the evolving role of CDOs. Their conversation explores how CDOs are transitioning from data stewards to strategic leaders, the importance of data governance, and the challenges of managing unstructured data.The Role of the CDO in the Agentic EraAs Roscoe notes, “CDOs are now pivotal in AI strategy,” reflecting how the role has grown from compliance oversight to guiding enterprise initiatives that directly support organizational goals.In this day and age, CDOs are tasked with ensuring that data is both accessible and reliable, providing a foundation for informed decision-making across business units. This includes establishing policies for data quality, access, and governance, which Roscoe highlights as essential: “data governance is foundational for AI.” At the same time, unstructured data ranging from documents and emails to multimedia adds complexity that requires careful management to make it useful while minimizing risk. “Unstructured data presents challenges,” he adds, emphasizing the need for structured oversight to fully leverage these assets.AI StrategyAlthough technology and analytics are evolving rapidly, the CDO's role in aligning data with strategic initiatives is critical. By connecting data assets to business processes, CDOs help ensure that initiatives are informed by reliable, well-governed information and can deliver measurable results.For anyone looking to understand the evolving responsibilities of CDOs, the importance of governance, and strategies for handling unstructured data, this episode of Tech Transformed provides a detailed and practical discussion.For more insights, follow Informatica:X: @informaticaInstagram: @informaticacorpFacebook: https://www.facebook.com/InformaticaLLC/LinkedIn: https://www.linkedin.com/company/informatica/TakeawaysCDOs are now central to shaping AI strategies and driving business growth.Robust data governance is crucial for the successful deployment of AI technologies.Unstructured data presents unique challenges and opportunities for AI development.A balance between centralized governance and federated operations is essential.Securing executive...

After 1,500+ conversations with CDOs and VPs of data , guest Malcolm Hawker noticed a disturbing pattern: a "limiting mindset" that causes data leaders to fail. He argues that too many leaders blame external factors such as "culture" , "data literacy", or a lack of support rather than taking accountability for delivering value.In this conversation, Malcolm breaks down how this mindset is reinforced by the analyst and consultant community and why it leads to a "value fatigue" where no one can prove their own ROI. He offers a clear path forward, starting with a simple 3-question framework for any new CDO and explains why "culture" is actually an outcome of delivering value, not a prerequisite for it. We also discuss his new book, "The Data Hero Playbook," tackle the "AI Ready" myth , explaining why conflating it with "BI Ready" is holding companies back and why your data is likely "good enough" to start right now.

Sujay Dutta and Sidd Rajagopal, authors of "Data as the Fourth Pillar," join the show to make the compelling case that for C-suite leaders obsessed with AI, data must be elevated to the same level as people, process, and technology.They provide a practical playbook for Chief Data Officers (CDOs) to escape the "cost center" trap by focusing on the "demand side" (business value) instead of just the "supply side" (technology). They also introduce frameworks like "Data Intensity" and "Total Addressable Value (TAV)" for data.We also tackle the reality of AI "slopware" and the "Great Pacific garbage patch" of junk data , explaining how to build the critical "context" (or "Data Intelligence Layer") that most GenAI projects are missing. Finally, they explain why the CDO must report directly to the CEO to play "offense," not defense.

Welcome to a special author's episode of The Data Chief, where we delve into the minds of three influential authors who are shaping the conversation around data and AI. First, Geoff Woods, author of The AI-Driven Leader, shares his philosophy of prioritizing strategy over technology to make faster, smarter decisions. Next, Wendy Batchelder, author of The Data Governance Handbook, discusses how to transform governance from a rigid bureaucracy into a business accelerator by focusing on business outcomes. Finally, Malcolm Hawker, author of The Data Hero Playbook, challenges data leaders to adopt a heroic mindset by becoming customer-driven and aligning their incentives with business success. Join us to learn how to lead effectively in the AI era by building a strategy-driven, governed, and customer-centric data function.The Data Chief Podcast: Author Episode Key MomentsGeoff Woods: The AI-Driven LeaderFrom "IT Problem" to Strategic Partner (06:20): Woods advocates for viewing AI as a "strategic thought partner" rather than an assistant or replacement, and emphasizes that AI strategy must align with business strategy.The CRIT Framework for Smarter Prompts (12:25): He introduces the CRIT framework for prompt engineering: Context, Role, Interview, Task. This method helps leaders get non-obvious, high-impact strategies from AI by having the AI ask the right questions.Beyond the Bottom Line: AI's Human Impact (22:17): Woods discusses the ROI of AI, including a case where AI identified savings equivalent to 2% of a company's revenue. Wendy Batchelder: The Data Governance HandbookData Governance as an Accelerator (32:33): Wendy Batchelder addresses the myth that data governance is a "dirty word" or a code for "no," arguing that its true purpose is to be an accelerator.Speaking the Language of Business (35:17): Batchelder emphasizes that data governance should be embedded from the start of a project, not as an afterthought. She provides an example of "bad" vs. "good" communication, urging data professionals to speak the language of the business.Measuring Value with Business Outcomes (40:00): She outlines how to measure the value of data governance by connecting it to business outcomes like increased revenue or improved customer service. Malcolm Hawker: The Data Hero PlaybookFrom Limiting Mindset to Growth Mindset (56:00): Hawker discusses why he wrote the book, calling the current moment a "do or die" opportunity for CDOs. He challenges the "limiting mindset" that leads to defeatism.Customer-Driven, Not Data-Driven (1:08:00): He urges data leaders to be "customer-driven, not data-driven," emphasizing the need for data teams to become more business literate.The Power of Product Management (1:14:00): Hawker advocates for bringing product management disciplines into data teams. This approach focuses on putting the customer at the center and ensures that data products are economically viable and tied to ROI.Key Quotes:"It is not technology first, strategy second. It is strategy first, technology second.” - Geoff Woods"The companies that are treating data as something that helps drive business outcomes are thinking about data at the beginning and set up at the end." - Wendy Batchelder“If you deliver value to your customers, if you are the lever of change and transformation in your organization, if you show value from data, you will get a seat at the table." - Malcolm HawkerMentionsThe AI-Driven Leader: Harnessing AI to Make Faster, SmarterHow AI is transforming strategy developmentData Governance Handbook: A practical approach to building trust in data5 key reasons why data analytics is important to businessThe Data Hero Playbook: Developing Your Data Leadership SuperpowersCDOs and CDAOs: Rethink your role or fade awayGuest Bios:About Geoff Woods Geoff Woods is the #1 bestselling author of The AI-Driven Leader, host of the AI-Driven Leader podcast, and Founder of AI Leadership and The AI-Driven Leadership Collective™, a highly vetted network of executives collaborating to harness AI to build better businesses and better lives. As the former Chief Growth Officer of Jindal Steel & Power, Geoff's strategic leadership helped the company grow its market cap from $750 million to over $12 billion in just four years. Prior to that, he co-founded the training and consulting company behind The ONE Thing, advising businesses ranging from $10 million to $60 billion in annual revenue.About Wendy Batchelder Wendy Batchelder is a three-time Chief Data Officer across financial services, technology & healthcare industries, with a wide understanding of how to take highly technical aspects of data management and translate them into simple, concise business valued solutions that are practical and simple to understand. Her background has led her to lead global data & analytics organizations at four Fortune 500 companies. She approaches situations with curiosity and humility, which has led to applying innovative data solutions to challenges with increased complexity to deliver value that companies can measure.A lifelong learner, Wendy graduated from Miami University with a B.S. in Accounting and Information Systems, from Drake University with a Masters of Accountancy, from University of Iowa with an Executive MBA, and pursues ongoing education through Harvard Business School. Her work history includes EY, KPMG, Aviva, Wells Fargo, VMware and Salesforce.About Malcolm HawkerMalcolm helps senior business leaders harness the power of data to transform their businesses. As a former Gartner analyst, he has consulted with some of the world's largest and best-known brands on their enterprise information management strategies and digital transformation initiatives.He is a frequent public speaker on data and analytics best practices with a passion for Master Data Management (MDM) and Data Governance. He welcomes the opportunity to share practical and actionable insights on how companies can become truly data-driven by implementing the cultural, technical, and organizational changes needed for success in the digital age. He is also the author of The Data Hero Playbook. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

In this episode, Generation AI analyzes groundbreaking research from OpenAI and Anthropic that reveals how AI usage is fundamentally different than expected. Hosts Ardis Kadiu and Dr. JC Bonilla dissect OpenAI's study of 1.5 million ChatGPT conversations, uncovering that 70% of usage is now personal rather than work-related - a complete reversal from initial predictions about enterprise productivity gains. They explore how ChatGPT has reached 700 million weekly active users with 90% of usage now outside the US in less than 3 years (compared to 23 years for the internet), while Claude data shows enterprise users focusing heavily on coding (36% of usage) and autonomous workflows (39% of conversations). The discussion reveals critical implications for higher education: while consumer AI adoption explodes globally with gender parity achieved (52% women users), institutions remain stuck with budget constraints, scattered use cases, and talent retention issues. This episode provides essential insights for education leaders on why the shift toward personal productivity and home-based AI usage creates both untapped opportunities and urgent challenges for institutional AI strategy heading into 2026.OpenAI's Massive ChatGPT Usage Study Overview (00:02:08)Analysis of 1.5 million ChatGPT conversations through NBER working paper700 million weekly active users, most comprehensive AI usage study everCollaboration between OpenAI Economic Research, Harvard economist David Deming, and NBERConsumer plans only - excludes enterprise and API usageSample represents massive scale given ChatGPT's global reachExplosive Growth Patterns and Metrics (00:05:27)Reached 100 million weekly users in under one year (unprecedented speed)Message volume growing even faster than user countAverage user sends 7-8 messages per day (up from 2x in 2024)Cohort analysis shows steady usage for existing users, new users driving intensityGrowth accelerates with each major model releaseGlobal Adoption Outpacing All Previous Technologies (00:08:09)90% of usage now outside North America (achieved in under 3 years)Internet took 23 years to reach same international distributionLower-income countries showing fastest adoption ratesImplications for international marketing and student recruitment strategiesGlobal phenomenon across all economic levelsGender Parity Achievement (00:11:30)Women users increased from 37% (January 2024) to 52% (July 2025)Based on analysis of typically feminine vs masculine namesReflects natural population distribution (50/50 split)Usage patterns now mirror general population demographicsThe Personal vs. Work Usage Revelation (00:13:24)Work-related usage dropped from 47% to only 27%Over 70% of ChatGPT usage is personal/non-work relatedHidden economics of home productivity emerging (not captured in GDP)Similar pattern to mobile device "bring your own device" adoptionEnterprise adoption significantly slower than consumerUsage Intent Categories and Detailed Breakdown (00:16:37)Three main categories: Asking (49%), Doing (40%), Expressing (11%)Practical guidance: 28.8% (top use case)Seeking information: 24.4% (up from 18% year-over-year)Writing: 23.9% (declining as users discover new applications)Multimedia: 7.3% (peaked at 12% after GPT-4o image features)Technical help: ~5%Self-expression: ~5%Specific High-Demand Use Cases (00:19:32)Tutoring/teaching: 10.2% (major opportunity for ed-tech)How-to advice: 8.5% (vertical SaaS potential)Personal writing & editing: 18% (demand for AI co-pilots)Coding in ChatGPT: Only 4.2% (compared to 36% in Claude)Each use case bar represents potential startup opportunity or graveyardClaude/Anthropic Enterprise Usage Analysis (00:27:42)Coding dominates: 36% of Claude usageAutonomous workflows: 39% of conversations (up from 27%)API automation: 77% of business API tasks are full automationMore complex multi-step workflows emergingGeographic usage reflects local economies (NYC: finance, Hawaii: tourism, Massachusetts: science)The Context and Data Bottleneck (00:34:52)Major enterprise bottleneck: Data/context readinessShift from prompt engineering to context orchestration for 2026Context engineering becoming the critical capabilityIntegration with existing platforms determines successOrchestration requires both technology and specialized talentEnterprise AI Economics and Priorities (00:37:26)Companies prioritize capability over cost savingsModel capabilities drive adoption more than pricingBusinesses "lean into automation over cost savings"Not yet highly price sensitive - capacity matters moreBudget lines for AI becoming essential planning itemHigher Education Specific Challenges (00:42:41)Minority of institutions identify as AI leaders75% of CDOs see moderate risk to academic integrityMost exploring scattered use cases vs. campus-wide programsBudget constraints remain primary blockerMarketing and enrollment teams leading adoptionStudent support and advising showing strong use casesTalent retention crisis as AI champions leave for better opportunitiesLabor Market Implications and Timeline (00:45:48)Fortune reports AI potentially replacing entry-level workersContext-heavy work remains difficult to fully automateAnthropic predicts powerful automated systems by late 2026-early 2027Low-hanging fruit automation tasks already saturatingNeed to view AI as outcomes rather than featuresKey Strategic Takeaways (00:46:47)Consolidation into integrated platforms expected for 2026Data connectors and ecosystem integration criticalConsumer adoption patterns informing enterprise strategyHome productivity gains creating new economic value unmeasured by GDPInstitutions need separate AI budget lines immediatelyPlatform strategy required vs. point solutions - - - -Connect With Our Co-Hosts:Ardis Kadiuhttps://www.linkedin.com/in/ardis/https://twitter.com/ardisDr. JC Bonillahttps://www.linkedin.com/in/jcbonilla/https://twitter.com/jbonillxAbout The Enrollify Podcast Network:Generation AI is a part of the Enrollify Podcast Network. If you like this podcast, chances are you'll like other Enrollify shows too! Enrollify is made possible by Element451 — The AI Workforce Platform for Higher Ed. Learn more at element451.com. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Episode OverviewIn this episode of CDO Matters, Malcolm Hawker sits down with Andreas Blumauer to explore how knowledge graphs are transforming enterprise data strategies. They discuss why semantics and domain models are critical for making data truly AI-ready, and how CDOs can use these tools to bridge the gap between data governance and AI innovation. If you're leading data initiatives and want to understand the future of knowledge management, this conversation is essential listening.Episode Links and ResourcesFollow Malcolm Hawker on LinkedInFollow Andreas Blumauer on LinkedIn

Many companies spend a lot on data technology, but often forget about the importance of data and AI literacy. Without the right skills, even the best platforms can fail to deliver results. Teams need to understand how to work with data and AI to make any strategy successful.In this episode of Tech Transformed, EM360Tech's Trisha Pillay chats with Greg Freeman, the founder of Data Literacy Academy about why knowing data and AI matters for anyone building a digital strategy.Data and AI LiteracyFreeman points out that many data strategies end up as technical documents rather than actionable roadmaps. He explains that organisations often spend heavily on infrastructure, expecting better tools to solve their problems but without employees who understand how to work with data and why it matters, these investments rarely deliver results.Freeman explains that data strategies often fail because only a small portion of employees less than 20 per cent are truly enthusiastic about data. Most strategies are designed with this minority in mind, creating an echo chamber that leaves the majority behind. As a result, data stays siloed, and business decisions don't improve. The Data Literacy Academy founder stresses that unless organisations engage the 80 per cent of employees who aren't already invested, their strategies are unlikely to succeed. When the focus is on tools rather than people, adoption falls behind.TakeawaysData and AI literacy are key to turning strategy into value.Tools alone don't work; people need confidence and context.Focus on engaging the data-hesitant majority, not just the enthusiasts.Cultural change, not just technical change, is what drives ROIChapters00:00 – Introduction02:07 – Beyond the Tech Stack04:41 – Why Strategies Fail08:41 – Literacy Barriers12:08 – Success in the Real World17:17 – Building Lasting Literacy22:20 – AI Needs Literacy Too26:33 – Final TakeawaysAbout Greg FreemanGreg Freeman is the founder and CEO of Data Literacy Academy, where he works with CDOs, CIOs, and business leaders to drive real cultural change around data. His mission is to help organisations tackle data illiteracy by building confidence and capability from the ground up, especially for employees who feel disengaged or anxious about data.With a background in sales leadership and tech startups, Greg brings both strategic insight and real-world experience.

In Episode 37, of Season 5 of Driven by Data: The Podcast, Kyle Winterbottom was joined by Ash Dhupar, Chief Data & AI Officer at Analog Devices (Fortune 500) where we explore how data leaders can link their work directly to measurable EBIT and revenue gains, and how the Chief Data Officer role has shifted from “data quality” to “profitability driver.” Ash shares a real life example of unlocking $50M in yield improvements by connecting siloed manufacturing data, and why predictive AI is key to stopping costly waste before it happens. We unpack the importance of the three-legged stool; balancing technology, business alignment, and process change, and why CDOs must own delivery end-to-end to shift the outdated perception that data is “just reporting.” You'll hear why scaling AI demands new skills, how too many “chiefs” has slowed progress can slow progress. We also discuss why agentic AI at scale is still 18–24 months away, and how AI-first strategies can multiply EBIT impact by 2–10x to create lasting competitive advantage.Thanks to our sponsor, Data Literacy Academy.Data Literacy Academy is leading the way in transforming enterprise workforces with data literacy across the organisation, through a combination of change management and education. In today's data-centric world, being data literate is no longer a luxury, it's a necessity.If you want successful data product adoption, and to keep driving innovation within your business, you need to start with data literacy first.At Data Literacy Academy, we don't just teach data skills. We empower individuals and teams to think critically, analyse effectively, and make decisions confidently based on data. We're bridging the gap between business and data teams, so they can all work towards aligned outcomes.From those taking their first steps in data literacy to seasoned experts looking to fine-tune their skills, our data experts provide tailored classes for every stage. But it's not just learning tracks that we offer. We embed a deep data culture shift through a transformative change management programme.We take a people-first approach, working closely with your executive team to win the hearts and minds. We know this will drive the company-wide impact that data teams want to achieve.Get in touch and find out how you can unlock the full potential of data in your organisation. Learn more at www.dl-academy.com.

In this episode, Junaid reflects on the CDOIQ Symposium in Boston, emphasizing the overwhelming focus on AI, especially AI agents, and their impact on white-collar jobs. We discuss the immense value of networking at conferences and debate whether CDOs overemphasize data quality at the expense of other critical areas like culture and literacy. And finally, we explore where CDO's oversteer and what they under value. 

On the 54th episode of Enterprise AI Innovators, hosts Evan Reiser (Abnormal AI) and Saam Motamedi (Greylock Partners) talk with Max Chan, Senior Vice President and Chief Information Officer at Avnet. Avnet is a $20 billion global technology distribution company that plays a critical role in the electronics supply chain, supporting the design, production, and delivery of devices worldwide. In this episode, Max shares how Avnet is utilizing generative AI to transform the way work is done across product design, quoting, customer service, and IT. He outlines their strategic maturity model for AI adoption and why CIOs must lead with experimentation.Quick Hits from Max:On evolving IT's role: "[We] moved away from the monolithic type of solutions into a more cloud-first, digital-centric composable architecture. That change truly helped with driving any and every innovation that we are talking about today.”On framing enterprise AI: "We bucket [AI use] into three types. First, we talk about out-of-the-box generative AI tools… The second bucket is what we like to call embedded AI… Last is truly custom AI solutions."On generative design: "The engineers, instead of coming with three designs [and having] the customer look at it, come back with some recommendations or questions, [and then] they go back to the drawing board; now they can immediately get in front of a customer [and] say, 'hey, look, these are the things that we can do if this is what you want [and] these are the parameters that you're changing.'"Recent Book Recommendation: Competing in the Age of AI by Karim Lakhani.  "A great cheat sheet for CIOs and CDOs on how to get started and what to avoid."-- Like what you hear? Leave us a review and subscribe to the show on Apple, Google, Spotify, Stitcher, or wherever you listen to podcasts.Enterprise AI Innovators is a show where top technology executives share how AI is transforming the enterprise. Each episode covers the real-world applications of AI, from improving products and optimizing operations to redefining the customer experience. Find more great insights from technology leaders and enterprise software experts at https://www.enterprisesoftware.blog/ Enterprise AI Innovators is produced by Josh Meer.

Episode OverviewIn this episode, Malcolm welcomes Mark Stouse back to unpack the Delaware court ruling that's sending shockwaves through boardrooms. They cut through the BS on what fiduciary duty now means for data leaders, why ROI theater won't cut it anymore, and how CDOs must evolve their approach to value or risk becoming irrelevant.Episode Links and ResourcesFollow Malcolm Hawker on LinkedInFollow Mark Stouse on LinkedIn

Welcome to The Prompt, a short-format minisode of How I Met Your Data, where hosts Junaid, Karen, and Anjali delve into the evolving landscape of data and AI. In this lively discussion, they explore the pivotal question of whether the Chief Data Officer (CDO) or Chief Data & AI Officer (CDAO) should report directly to a CEO, a CIO, or another C-level executive. Each host shares sharp insights based on their professional experience, addressing the challenges facing CDOs, such as their typically short tenures and the essential components required for their success. Throughout the episode, the trio touches on the significance of building a data-driven culture, assessing whether data should be seen as a cost center or a valuable asset, and the complexities of integrating data literacy within corporate strategy. They also tackle the important consideration of hiring CDOs from within the organization versus bringing in external change agents, weighing the benefits and drawbacks of each approach. Join them in this engaging conversation that challenges conventional wisdom and highlights the critical role data plays in defining today's business landscape.

Back in 2016, the Internet and Mobile Association of India set up an all new club for what was then a very small cohort of digital leaders in corporate India. It was called the all-India Chief Digital Officer club. Back then, there were only about five-six CDOs that were members. The point of the initiative was to give legitimacy to this new, emerging role. But soon enough, the initiative fizzled out. Not because the role didn't take off or anything. Actually, the opposite. The initiative became redundant because the role became even more popular than they had anticipated. So it started with 5-6 members, but within the next four years its membership rose to 50 and then doubled the next year. You see, digital transformation has become THE buzzword for corporate India. And in the process, the CDO has become part of the companies top leadership. But the question is — where does that leave the CIO? Tune in. *This episode was originally published on 18 December, 2024 P.S The Ken's podcast team is hiring! Here's what we're looking for.Daybreak is produced from the newsroom of The Ken, India's first subscriber-only business news platform. Subscribe for more exclusive, deeply-reported, and analytical business stories.Listen to the latest episode of Two by Two here

This blog offers five guiding principles to help CIOs, CDOs, and team leaders optimize hybrid data environments. Published at: https://www.eckerson.com/articles/balancing-act-five-principles-to-optimize-hybrid-cloud-environments

Episode OverviewThe expanding data landscape is increasingly complex, making the role of the Data Architect more critical than ever before. On this week's episode of the CDO Matters Podcast, Pete Cooney, the Lead Enterprise Architect with Jackson, shares his wealth of experience on how CDOs can leverage their data architecture to drive maximum value for their organizations.Episode Links and ResourcesFollow Malcolm Hawker on LinkedInFollow Pete Cooney on LinkedIn

Genevieve Hayes Consulting Episode 53: A Wake-Up Call from 3 Tech Leaders on Why You're Failing as a Data Scientist Are your data science projects failing to deliver real business value?What if the problem isn’t the technology or the organization, but your approach as a data scientist?With only 11% of data science models making it to deployment and close to 85% of big data projects failing, something clearly isn’t working.In this episode, three globally recognised analytics leaders, Bill Schmarzo, Mark Stouse and John Thompson, join Dr Genevieve Hayes to deliver a tough love wake-up call on why data scientists struggle to create business impact, and more importantly, how to fix it.This episode reveals:Why focusing purely on technical metrics like accuracy and precision is sabotaging your success — and what metrics actually matter to business leaders. [04:18]The critical mindset shift needed to transform from a back-room technical specialist into a valued business partner. [30:33]How to present data science insights in ways that drive action — and why your fancy graphs might be hurting rather than helping. [25:08]Why “data driven” isn’t enough, and how to adopt a “data informed” approach that delivers real business outcomes. [54:08] Guest Bio Bill Schmarzo, also known as “The Dean of Big Data,” is the AI and Data Customer Innovation Strategist for Dell Technologies' AI SPEAR team, and is the author of six books on blending data science, design thinking, and data economics from a value creation and delivery perspective. He is an avid blogger and is ranked as the #4 influencer worldwide in data science and big data by Onalytica and is also an adjunct professor at Iowa State University, where he teaches the “AI-Driven Innovation” class.Mark Stouse is the CEO of ProofAnalytics.ai, a causal AI company that helps companies understand and optimize their operational investments in light of their targeted objectives, time lag, and external factors. Known for his ability to bridge multiple business disciplines, he has successfully operationalized data science at scale across large enterprises, driven by his belief that data science’s primary purpose is enabling better business decisions.John Thompson is EY's Global Head of AI and is the author of four books on AI, data and analytics teams. He was named one of dataIQ's 100 most influential people in data in 2023 and is also an Adjunct Professor at the University of Michigan, where he teaches a course based on his book “Building Analytics Teams”. Links Connect with Bill on LinkedInConnect with Mark on LinkedInConnect with John on LinkedIn Connect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE Read Full Transcript [00:00:00] Dr Genevieve Hayes: Hello, and welcome to Value Driven Data Science, the podcast that helps data scientists transform their technical expertise into tangible business value, career autonomy, and financial reward. I’m Dr. Genevieve Hayes, and today I’m joined by three globally recognized innovators and leaders in AI, analytics, and data science.[00:00:24] Bill Schmarzo, Mark Stouse, and John Thompson. Bill? Also known as the Dean of Big Data, is the AI and Data Customer Innovation Strategist for Dell Technologies AI Spear Team, and is the author of six books on blending data science, design thinking, and data economics from a value creation and delivery perspective.[00:00:49] He is an avid blogger and is ranked as the number four influencer worldwide in data science and big data Analytica. And he’s also an adjunct professor at Iowa State University, where he teaches AI driven innovation. Mark is the CEO of proofanalytics. ai, a causal AI company that helps organizations understand and optimize their operational investments in light of their targeted objectives, time lag and external factors.[00:01:23] Known for his ability to bridge multiple business disciplines, he has successfully operationalized data science at scale across large enterprises. Driven by his belief that data science’s primary purpose is enabling better business decisions. And John is EY’s global head of AI and is the author of four books on AI data and analytics teams.[00:01:49] He was named one of DataIQ’s 100 most influential people in data in 2023. and is also an adjunct professor at the University of Michigan, where he teaches a course based on his book, Building Analytics Teams. Today’s episode will be a tough love wake up call for data scientists on why you are failing to deliver real business value and more importantly, what you can do about it.[00:02:17] So get ready to boost your impact. Earn what you’re worth and rewrite your career algorithm. Bill, Mark, John, welcome to the show.[00:02:25] Mark Stouse: Thank[00:02:26] Bill Schmarzo: Thanks for having us.[00:02:27] John Thompson: to be here.[00:02:28] Dr Genevieve Hayes: Only 11 percent of data scientists say their models always deploy. Only 10 percent of companies obtain significant financial benefits from AI technologies and close to 85 percent of big data projects fail. These statistics, taken from research conducted by Rexa Analytics, the Boston Consulting Group and Gartner respectively, paint a grim view of what it’s like working as a data scientist.[00:02:57] The reality is, you’re probably going to fail. And when that reality occurs, it’s not uncommon for data scientists to blame either the executive for not understanding the brilliance of their work, or the corporate culture for not being ready for data science. And maybe this is true for some organizations.[00:03:20] Particularly those relatively new to the AI adoption path. But it’s now been almost 25 years since William Cleveland first coined the term data science. And as the explosive uptake of generative AI tools, such as chat GPT demonstrate with the right use case. People are very willing to take on AI technologies.[00:03:42] So perhaps it’s finally time to look in the mirror and face the truth. Perhaps the problem is you, the data scientist. But if this is the case, then don’t despair. In many organizations, the leadership just don’t have the time to provide data scientists with the feedback necessary to improve. But today, I’m sitting here with three of the world’s best to provide that advice just for you.[00:04:09] So, let’s cut to the chase what are the biggest mistakes you see data scientists making when it comes to demonstrating their value?[00:04:18] Mark Stouse: I think that you have to start with the fact that they’re not demonstrating their value, right? I mean, if you’re a CEO, a CFO, head of sales really doesn’t matter if you’re trying to make better business decisions over and over and over again. As Bill talks about a lot, the whole idea here is economic,[00:04:39] and it is. About engaging, triggering the laws of compounding you’ve got to be able to do stuff that makes that happen. Data management, for example, even though we all agree that it’s really necessary, particularly if you’re launching, you know, big data solutions. You can’t do this sequentially and be successful.[00:05:04] You’re going to have to find some areas probably using, you know, old fashioned math around causal analytics, multivariable linear regression, things like that, to at least get the ball rolling. In terms of delivering better value, the kind of value that business leaders actually see as valuable[00:05:29] I mean, one of the things that I feel like I say a lot is, you have to have an understanding of your mission, the mission of data science. As somebody who, as a business leader champions it. Is to help people make those better and better and better decisions. And if you’re not doing that, you’re not creating value.[00:05:52] Full stop.[00:05:53] Bill Schmarzo: Totally agree with Mark. I think you’re going to find that all three of us are in violent agreement on a lot of this stuff. What I find interesting is it isn’t just a data scientist fault. Genevieve, you made a comment that leadership lacks the time to provide guidance to data scientists. So if leadership Is it treating data and analytics as an economics conversation if they think it’s a technology conversation is something that should be handled by the CIO, you’ve already lost, you’ve already failed, you already know you failed,[00:06:24] Mark mentioned the fact that this requires the blending of both sides of the aisle. It requires a data scientist to have the right mindset to ask questions like what it is that we’re trying to achieve. How do we create value? What are our desired outcomes? What are the KPIs metrics around which are going to make your success?[00:06:39] Who are our key stakeholders? There’s a series of questions that the data scientist must be empowered to ask and the business Leadership needs to provide the time and people and resources to understand what we’re trying to accomplish. It means we can go back old school with Stephen Covey, begin with an end in mind.[00:07:01] What is it we’re trying to do? Are we trying to improve customer retention? We try to do, you know, reduce unplanned operational downtime or improve patient outcomes. What is it we’re trying to accomplish? The conversation must, must start there. And it has to start with business leadership, setting the direction, setting the charter, putting the posts out where we want to go, and then the data science team collaborating with the stakeholders to unleash that organizational tribal knowledge to actually solve[00:07:32] Dr Genevieve Hayes: think a lot of the problem comes with the fact that many business leaders see data science as being like an IT project. So, if you’ve got your Windows upgrade, the leadership It gives the financing to IT, IT goes along and does it. And then one morning you’re told, when you come into work, your computer will magically upgrade to the latest version of Windows.[00:07:55] So no one really gets bothered by it. And I think many business leaders treat data science as just another IT project like that. They think they can just Give the funding, the data scientists will go away and then they’ll come in one morning and the data science will magically be on their computer.[00:08:15] Bill Schmarzo: Yeah, magic happens, right? No, no, magic doesn’t happen, it doesn’t happen. There has to be that leadership commitment to be at the forefront, not just on the boat, but at the front of the boat saying this is the direction we’re going to go.[00:08:29] John Thompson: That’s the whole reason this book was written. The whole point is that, analytics projects are not tech projects. Analytics projects are cultural transformation projects, is what they are. And if you’re expecting the CEO, CFO, CIO, COO, whoever it is, to go out there and set the vision.[00:08:50] That’s never going to happen because they don’t understand technology, and they don’t understand data. They’d rather be working on building the next factory or buying another company or something like that. What really has to happen is the analytics team has to provide leadership to the leadership for them to understand what they’re going to do.[00:09:12] So when I have a project that we’re trying to do, my team is trying to do, and if we’re working for, let’s say, marketing, I go to the CMO and I say, look, you have to dedicate and commit. that your subject matter experts are going to be in all the meetings. Not just the kickoff meetings, not just the quarterly business review, the weekly meetings.[00:09:36] Because when we go off as an analytics professionals and do things on our own, we have no idea what the business runs like. , we did analytics at one company that I work for. We brought it back and we showed it to the they said, the numbers are wildly wrong. And we said, well, why? And they said, well, you probably don’t understand that what we do is illegal in 10 US states.[00:10:00] So you probably have the data from all those 10 states in the analysis. And we did. So, we took it all out and they look down there and go, you got it right. It’s kind of surprising. You didn’t know what you were doing and you got it right. So, it has to be a marriage of the subject matter experts in the business.[00:10:17] And the data scientists, you can’t go to the leadership and say, tell us what you want. They don’t know what they want. They’d want another horse in Henry Ford’s time, or they glue a, a Walkman onto a radio or something in Steve Jobs time. They don’t know what they want. So you have to come together.[00:10:36] And define it together and you have to work through the entire project together.[00:10:42] Mark Stouse: Yeah, I would add to that, okay, that a lot of times the SMEs also have major holes in their knowledge that the analytics are going to challenge and give them new information. And so I totally agree. I mean, this is an iterative learning exchange. That has profound cultural implications.[00:11:11] One of the things that AI is doing right now is it is introducing a level of transparency and accountability into operations, corporate operations, my operations, your operations, that honestly, none of us are really prepared for. None of us are really prepared for the level of learning that we’re going to have to do.[00:11:36] And very few of us are aware of how polymathic. Most of our challenges, our problems, our objectives really are one of the things that I love to talk about in this regard is analytics made me a much better person. That I once was because it showed me the extent of my ignorance.[00:12:01] And when I kind of came to grips with that and I started to use really the modicum of knowledge that I have as a way of curating my ignorance. And I got humble about it made a big difference[00:12:16] John Thompson: Well, that’s the same when I was working shoulder to shoulder with Bill, I just realized how stupid I was. So, then I just, really had to, come back and, say, oh, God nowhere near the summit, I have a long way to go.[00:12:31] Bill Schmarzo: Hey, hey, Genevie. Let me throw something out there at you and it builds on what John has said and really takes off on what Mark is talking about is that there is a cultural preparation. It needs to take place across organizations in order to learn to master the economies of learning,[00:12:48] the economies of learning, because you could argue in knowledge based industries that what you are learning is more important than what you know. And so if what you know has declining value, and what you’re learning has increasing value, then what Mark talked about, and John as well, both city presenting data and people saying, I didn’t know that was going on, right?[00:13:09] They had a certain impression. And if they have the wrong cultural mindset. They’re going to fight that knowledge. They’re going to fight that learning, oh, I’m going to get fired. I’m going to get punished. No, we need to create cultures that says that we are trying to master the economies and learning and you can’t learn if you’re not willing to fail.[00:13:29] And that is what is powerful about what AI can do for us. And I like to talk about how I’m a big fan of design thinking. I integrate design thinking into all my workshops and all my training because it’s designed to. Cultivate that human learning aspect. AI models are great at cultivating algorithmic learning.[00:13:50] And when you bring those two things together around a learning culture that says you’re going to try things, you’re going to fail, you’re going to learn, those are the organizations that are going to win.[00:13:59] John Thompson: Yeah, you know, to tie together what Mark and Bill are saying there is that, you need people to understand that they’re working from an outmoded view of the business. Now, it’s hard for them to hear that. It’s hard for them to realize it. And what I ask data scientists to do that work for me is when we get a project and we have an operational area, sales, marketing, logistics, finance, manufacturing, whatever it is.[00:14:26] They agreed that they’re going to go on the journey with us. We do something really simple. We do an exploratory data analysis. We look at means and modes and distributions and things like that. And we come back and we say, this is what the business looks like today. And most of the time they go, I had no idea.[00:14:44] You know, I didn’t know that our customers were all, for the most part, between 70 and 50. I had no idea that our price point was really 299. I thought it was 3, 299. So you then end up coming together. You end up with a shared understanding of the business. Now one of two things is generally going to happen.[00:15:05] The business is going to freak out and leave the project and say, I don’t want anything to do with this, or they’re going to lean into it and say, I was working from something that was, as Bill said, declining value. Okay. Now, if they’re open, like a AI model that’s being trained, if they’re open to learning, they can learn what the business looks like today, and we can help them predict what the business should look like tomorrow.[00:15:31] So we have a real issue here that the three of us have talked about it from three different perspectives. We’ve all seen it. We’ve all experienced it. It’s a real issue, we know how people can come together. The question is, will they?[00:15:46] Dr Genevieve Hayes: think part of the issue is that, particularly in the area of data science, there’s a marked lack of leadership because I think a lot of people don’t understand how to lead these projects. So you’ve got Many data scientists who are trained heavily in the whole technical aspect of data science, and one thing I’ve come across is, you know, data scientists who’ll say to me, my job is to do the technical work, tell me what to do.[00:16:23] I’ll go away and do it. Give it to you. And then you manager can go and do whatever you like with it.[00:16:29] Mark Stouse: Model fitment.[00:16:31] Dr Genevieve Hayes: Yeah. And then one thing I’ve experienced is many managers in data science are, you know, It’s often the area that they find difficult to find managers for, so we’ll often get people who have no data science experience whatsoever[00:16:46] and so I think part of the solution is teaching the data scientists that they have to start managing up because they’re the ones who understand what they’re doing the best, but no one’s telling them that because the people above them often don’t know that they should be telling the data[00:17:08] John Thompson: Well, if that’s the situation, they should just fire everybody and save the money. Because it’s never going to go anywhere. But Bill, you were going to say something. Go ahead.[00:17:16] Bill Schmarzo: Yeah, I was going to say, what’s interesting about Genevieve, what you’re saying is that I see this a lot in not just data scientists, but in a lot of people who are scared to show their ignorance in new situations. I think Mark talked about this, is it because they’re, you think about if you’re a data scientist, you probably have a math background. And in math, there’s always a right answer. In data science, there isn’t. There’s all kinds of potential answers, depending on the situation and the circumstances. I see this all the time, by the way, with our sales folks. Who are afraid we’re selling technology. We’re afraid to talk to the line of business because I don’t understand their business Well, you don’t need to understand their business, but you do need to become like socrates and start asking questions What are you trying to accomplish?[00:18:04] What are your goals? What are your desired outcomes? How do you measure success? Who are your stakeholders ? You have to be genuinely interested In their success and ask those kind of questions if you’re doing it to just kind of check a box off Then just get chad gpt to rattle it off But if you’re genuinely trying to understand what they’re trying to accomplish And then thinking about all these marvelous different tools you have because they’re only tools And how you can weave them together to help solve that now you’ve got That collaboration that john’s book talks about about bringing these teams together Yeah[00:18:39] Mark Stouse: is, famously paraphrased probably did actually say something like this, . But he’s famously paraphrased as saying that he would rather have a really smart question than the best answer in the world. And. I actually experienced that two days ago,[00:18:57] in a conversation with a prospect where I literally, I mean, totally knew nothing about their business. Zero, but I asked evidently really good questions. And so his impression of me at the end of the meeting was, golly, you know, so much about our business. And I wanted to say, yeah, cause you just educated me.[00:19:21] Right. You know, I do now. And so I think there’s actually a pattern here that’s really worth elevating. So what we are seeing right now with regard to data science teams is scary similar to what happened with it after Y2K, the business turned around and looked at him and said, seriously, we spend all that money,[00:19:45] I mean, what the heck? And so what happened? The CIO got, demoted organizationally pretty far down in the company wasn’t a true C suite member anymore. Typically the whole thing reported up into finance. The issue was not. Finance, believing that they knew it better than the it people,[00:20:09] it was, we are going to transform this profession from being a technology first profession to a business outcomes. First profession, a money first profession, an economics organization, that has more oftentimes than not been the outcome in the last 25 years. But I think that that’s exactly what’s going on right now with a lot of data science teams.[00:20:39] You know, I used to sit in technology briefing rooms, listening to CIOs and other people talk about their problems. And. This one CIO said, you know, what I did is I asked every single person in my organization around the world to go take a finance for non financial managers course at their local university.[00:21:06] They want credit for it. We’ll pay the bill. If they just want to audit it, they can do that. And they started really cross pollinating. These teams to give them more perspective about the business. I totally ripped that off because it just struck me as a CMO as being like, so many of these problems, you could just do a search and replace and get to marketing.[00:21:32] And so I started doing the same thing and I’ve made that suggestion to different CDOs, some of whom have actually done it. So it’s just kind of one of those things where you have to say, I need to know more. So this whole culture of being a specialist is changing from.[00:21:53] This, which, this is enough, this is okay , I’m making a vertical sign with my hand, to a T shaped thing, where the T is all about context. It’s all about everything. That’s not part of your. Profession[00:22:09] John Thompson: Yeah, well, I’m going to say that here’s another book that you should have your hands on. This is Aristotle. We can forget about Socrates. Aristotle’s the name. But you know. But , Bill’s always talking about Socrates. I’m an Aristotle guy myself. So, you[00:22:23] Bill Schmarzo: Okay, well I Socrates had a better jump shot. I’m sorry. He could really nail that[00:22:28] John Thompson: true. It’s true. Absolutely. Well, getting back , to the theme of the discussion, in 1 of the teams that I had at CSL bearing, which is an Australian company there in Melbourne, I took my data science team and I brought in speech coaches.[00:22:45] Presentation coaches people who understand business, people who understood how to talk about different things. And I ran them through a battery of classes. And I told them, you’re going to be in front of the CEO, you’re going to be in front of the EVP of finance, you’re going to be in front of all these different people, and you need to have the confidence to speak their language.[00:23:07] Whenever we had meetings, we talk data science talk, we talk data and integration and vectors and, algorithms and all that kind of stuff. But when we were in the finance meeting, we talked finance. That’s all we talked. And whenever we talked to anybody, we denominated all our conversations in money.[00:23:25] Whether it was drachma, yen, euros, pounds, whatever it was, we never talked about speeds and feeds and accuracy and results. We always talked about money. And if it didn’t make money, we didn’t do it. So, the other thing that we did that really made a difference was that when the data scientists and data scientists hate this, When they went into a meeting, and I was there, and even if I wasn’t there, they were giving the end users and executives recommendations.[00:23:57] They weren’t going in and showing a model and a result and walking out the door and go, well, you’re smart enough to interpret it. No, they’re not smart enough to interpret it. They actually told the marketing people. These are the 3 things you should do. And if your data scientists are not being predictive and recommending actions, they’re not doing their job.[00:24:18] Dr Genevieve Hayes: What’s the, so what test At the end of everything, you have to be able to say, so what does this mean to whoever your audience is?[00:24:25] Mark Stouse: That’s right. I mean, you have to be able to say well, if the business team can’t look at your output, your data science output, and know what to do with it, and know how to make a better decision, it’s like everything else that you did didn’t happen. I mean it, early in proof, we were working on. UX, because it became really clear that what was good for a data scientist wasn’t working. For like everybody else. And so we did a lot of research into it. Would you believe that business teams are okay with charts? Most of them, if they see a graph, they just totally freeze and it’s not because they’re stupid.[00:25:08] It’s because so many people had a bad experience in school with math. This is a psychological, this is an intellectual and they freeze. So in causal analytics, one of the challenges is that, I mean, this is pretty much functioning most of the time anyway, on time series data, so there is a graph,[00:25:31] this is kind of like a non negotiable, but we had a customer that was feeding data so fast into proof that the automatic recalc of the model was happening like lickety split. And that graph all of a sudden looked exactly like a GPS. It worked like a GPS. In fact, it really is a GPS. And so as soon as we stylized.[00:26:01] That graph to look more like a GPS track, all of a sudden everybody went, Oh,[00:26:10] Dr Genevieve Hayes: So I got rid of all the PTSD from high school maths and made it something familiar.[00:26:16] Mark Stouse: right. And so it’s very interesting. Totally,[00:26:21] Bill Schmarzo: very much mirrors what mark talked about So when I was the new vice president of advertiser analytics at yahoo we were trying to solve a problem to help our advertisers optimize their spend across the yahoo ad network and because I didn’t know anything about that industry We went out and my team went out and interviewed all these advertisers and their agencies.[00:26:41] And I was given two UEX people and zero data. Well, I did have one data scientist. But I had mostly UX people on this project. My boss there said, you’re going to want UX people. I was like, no, no, I need analytics. He said, trust me in UX people and the process we went through and I could spend an hour talking about the grand failure of the start and the reclamation of how it was saved at a bar after too many drinks at the Waldorf there in New York.[00:27:07] But what we’ve realized is that. For us to be effective for our target audience was which was media planners and buyers and campaign managers. That was our stakeholders. It wasn’t the analysts, it was our stakeholders. Like Mark said, the last thing they wanted to see was a chart. And like John said, what they wanted the application to do was to tell them what to do.[00:27:27] So we designed this user interface that on one side, think of it as a newspaper, said, this is what’s going on with your campaign. This audience is responding. These sites are this, these keywords are doing this. And the right hand side gave recommendations. We think you should move spend from this to this.[00:27:42] We think you should do this. And it had three buttons on this thing. You could accept it and it would kick into our advertising network and kick in. And we’d measure how effective that was. They could reject it. They didn’t think I was confident and we’d measure effectiveness or they could change it. And we found through our research by putting that change button in there that they had control, that adoption went through the roof.[00:28:08] When it was either yes or no, adoption was really hard, they hardly ever used it. Give them a chance to actually change it. That adoption went through the roof of the technology. So what John was saying about, you have to be able to really deliver recommendations, but you can’t have the system feel like it’s your overlord.[00:28:27] You’ve got to be like it’s your Yoda on your shoulder whispering to your saying, Hey, I think you should do this. And you’re going, eh, I like that. No, I don’t like this. I want to do that instead. And when you give them control, then the adoption process happens much smoother. But for us to deliver those kinds of results, we had to know in detail, what decisions are they trying to make?[00:28:45] How are they going to measure success? We had to really understand their business. And then the data and the analytics stuff was really easy because we knew what we had to do, but we also knew what we didn’t have to do. We didn’t have to boil the ocean. We were trying to answer basically 21 questions.[00:29:01] The media planners and buyers and the campaign managers had 21 decisions to make and we built analytics and recommendations for each Of those 21[00:29:10] John Thompson: We did the same thing, you know, it blends the two stories from Mark and Bill, we were working at CSL and we were trying to give the people tools to find the best next location for plasma donation centers. And, like you said, there were 50, 60 different salient factors they had, and when we presented to them in charts and graphs, Information overload.[00:29:34] They melted down. You can just see their brains coming out of their ears. But once we put it on a map and hit it all and put little dials that they could fiddle with, they ran with it.[00:29:49] Bill Schmarzo: brilliant[00:29:50] Mark Stouse: totally, totally agree with that. 100% you have to know what to give people and you have to know how to give them, control over some of it, nobody wants to be an automaton. And yet also they will totally lock up if you just give them the keys to the kingdom. Yeah.[00:30:09] Dr Genevieve Hayes: on what you’ve been saying in the discussion so far, what I’m hearing is that the critical difference between what data scientists think their role is and what business leaders actually need is the data scientists is. Well, the ones who aren’t performing well think their role is to just sit there in a back room and do technical work like they would have done in their university assignments.[00:30:33] What the business leaders need is someone who can work with them, ask the right questions in order to understand the needs of the business. make recommendations that answer those questions. But in answering those questions, we’re taking a data informed approach rather than a data driven approach. So you need to deliver the answers to those questions in such a way that you’re informing the business leaders and you’re delivering it in a way that Delivers the right user experience for them, rather than the user experience that the data scientists might want, which would be your high school maths graphs.[00:31:17] Is that a good summary?[00:31:20] John Thompson: Yeah, I think that’s a really good summary. You know, one of the things that Bill and I, and I believe Mark understands is we’re all working to change, you know, Bill and I are teaching at universities in the United States. I’m on the advisory board of about five. Major universities. And whenever I go in and talk to these universities and they say, Oh, well, we teach them, these algorithms and these mathematical techniques and these data science and this statistics.[00:31:48] And I’m like, you are setting these people up for failure. You need to have them have presentation skills, communication skills, collaboration. You need to take about a third of these credits out and change them out for soft skills because you said it Genevieve, the way we train people, young people in undergraduate and graduate is that they have a belief that they’re going to go sit in a room and fiddle with numbers.[00:32:13] That’s not going to be successful.[00:32:16] Mark Stouse: I would give one more point of dimensionality to this, which is a little more human, in some respects, and that is that I think that a lot of data scientists love the fact that they are seen as Merlin’s as shamans. And the problem that I personally witnessed this about two years ago is when you let business leaders persist in seeing you in those terms.[00:32:46] And when all of a sudden there was a major meltdown of some kind, in this case, it was interest rates, and they turn around and they say, as this one CEO said in this meeting Hey, I know you’ve been doing all kinds of really cool stuff back there with AI and everything else. And now I need help.[00:33:08] Okay. And the clear expectation was. I need it now, I need some brilliant insight now. And the answer that he got was, we’re not ready yet. We’re still doing the data management piece. And this CEO dropped the loudest F bomb. That I think I have ever heard from anybody in almost any situation,[00:33:36] and that guy, that data science leader was gone the very next day. Now, was that fair? No. Was it stupid? For the data science leader to say what he said. Yeah, it was really dumb.[00:33:52] Bill Schmarzo: Don’t you call that the tyranny of perfection mark? Is that your term that you always use? is that There’s this idea that I gotta get the data all right first before I can start doing analysis And I think it’s you I hear you say the tyranny of perfection is what hurts You Progress over perfection, learning over absolutes, and that’s part of the challenge is it’s never going to be perfect.[00:34:13] Your data is never going to be perfect, you got to use good enough data[00:34:17] Mark Stouse: It’s like the ultimate negative version of the waterfall.[00:34:22] John Thompson: Yeah,[00:34:23] Mark Stouse: yet we’re all supposedly living in agile paradise. And yet very few people actually operate[00:34:30] John Thompson: that’s 1 thing. I want to make sure that we get in the recording is that I’ve been on record for years and I’ve gone in front of audiences and said this over and over again. Agile and analytics don’t mix that is. There’s no way that those 2 go together. Agile is a babysitting methodology. Data scientists don’t do well with it.[00:34:50] So, you know, I’ll get hate mail for that, but I will die on that hill. But, the 1 thing that, Mark, I agree with 100 percent of what you said, but the answer itself or the clue itself is in the title. We’ve been talking about. It’s data science. It’s not magic. I get people coming and asking me to do magical things all the time.[00:35:11] And I’m like. Well, have you chipped all the people? Do you have all their brain waves? If you have that data set, I can probably analyze it. But, given that you don’t understand what’s going on inside their cranium, that’s magic. I can’t do that. We had the same situation when COVID hit, people weren’t leaving their house.[00:35:29] So they’re not donating plasma. It’s kind of obvious, so, people came to us and said, Hey, the world’s gone to hell in a handbasket in the last two weeks. The models aren’t working and I’m like, yeah, the world’s changed, give us four weeks to get a little bit of data.[00:35:43] We’ll start to give you a glimmer of what this world’s going to look like two months later. We had the models working back in single digit error terms, but when the world goes haywire, you’re not going to have any data, and then when the executives are yelling at you, you just have to say, look, this is modeling.[00:36:01] This is analytics. We have no precedent here.[00:36:05] Bill Schmarzo: to build on what John was just saying that the challenge that I’ve always seen with data science organizations is if they’re led by somebody with a software development background, getting back to the agile analytics thing, the problem with software development. is that software development defines the requirements for success.[00:36:23] Data science discovers them. It’s hard to make that a linear process. And so, if you came to me and said, Hey, Schmarz, you got a big, giant data science team. I had a great data science team at Hitachi. Holy cow, they were great. You said, hey, we need to solve this problem. When can you have it done?[00:36:38] I would say, I need to look at the problem. I need to start exploring it. I can’t give you a hard date. And that drove software development folks nuts. I need a date for when I, I don’t know, cause I’ve got to explore. I’m going to try lots of things. I’m going to fail a lot.[00:36:51] I’m going to try things that I know are going to fail because I can learn when I fail. And so, when you have an organization that has a software development mindset, , like John was talking about, they don’t understand the discovery and learning process that the data science process has to go through to discover the criteria for success.[00:37:09] Mark Stouse: right. It’s the difference between science and engineering.[00:37:13] John Thompson: Yes, exactly. And 1 of the things, 1 of the things that I’ve created, it’s, you know, everybody does it, but I have a term for it. It’s a personal project portfolio for data scientists. And every time I’ve done this and every team. Every data scientist has come to me individually and said, this is too much work.[00:37:32] It’s too hard. I can’t[00:37:34] Bill Schmarzo: Ha, ha, ha,[00:37:35] John Thompson: three months later, they go, this is the only way I want to work. And what you do is you give them enough work so when they run into roadblocks, they can stop working on that project. They can go out and take a swim or work on something else or go walk their dog or whatever.[00:37:53] It’s not the end of the world because the only project they’re working on can’t go forward. if they’ve got a bunch of projects to time slice on. And this happens all the time. You’re in, team meetings and you’re talking and all of a sudden the data scientist isn’t talking about that forecasting problem.[00:38:09] It’s like they ran into a roadblock. They hit a wall. Then a week later, they come in and they’re like, Oh, my God, when I was in the shower, I figured it out. You have to make time for cogitation, introspection, and eureka moments. That has to happen in data science.[00:38:28] Bill Schmarzo: That is great, John. I love that. That is wonderful.[00:38:30] Mark Stouse: And of course the problem is. Yeah. Is that you can’t predict any of that, that’s the part of this. There’s so much we can predict. Can’t predict that.[00:38:42] Bill Schmarzo: you know what you could do though? You could do Mark, you could prescribe that your data science team takes multiple showers every day to have more of those shower moments. See, that’s the problem. I see a correlation. If showers drive eureka moments, dang it.[00:38:54] Let’s give him more showers.[00:38:56] John Thompson: Yep. Just like firemen cause fires[00:38:59] Mark Stouse: Yeah, that’s an interesting correlation there, man.[00:39:05] Dr Genevieve Hayes: So, if businesses need something different from what the data scientists are offering, why don’t they just articulate that in the data scientist’s role description?[00:39:16] John Thompson: because they don’t know they need it.[00:39:17] Mark Stouse: Yeah. And I think also you gotta really remember who you’re dealing with here. I mean, the background of the average C suite member is not highly intellectual. That’s not an insult, that’s just they’re not deep thinkers. They don’t think a lot. They don’t[00:39:37] John Thompson: that with tech phobia.[00:39:38] Mark Stouse: tech phobia and a short termism perspective.[00:39:43] That arguably is kind of the worst of all the pieces.[00:39:48] John Thompson: storm. It’s a[00:39:49] Mark Stouse: It is, it is a[00:39:50] John Thompson: know, I, I had, I’ve had CEOs come to me and say, we’re in a real crisis here and you guys aren’t helping. I was like, well, how do you know we’re not helping? You never talked to us. And, in this situation, we had to actually analyze the entire problem and we’re a week away from making recommendations.[00:40:08] And I said that I said, we have an answer in 7 days. He goes, I need an answer today. I said, well, then you should go talk to someone else because in 7 days, I’ll have it. But now I don’t. So, I met with him a week later. I showed them all the data, all the analytics, all the recommendations. And they said to me, we don’t really think you understand the business well enough.[00:40:27] We in the C suite have looked at it and we don’t think that this will solve it. And I’m like, okay, fine, cool. No problem. So I left, and 2 weeks later, they called me in and said, well, we don’t have a better idea. So, what was that you said? And I said, well, we’ve coded it all into the operational systems.[00:40:43] All you have to do is say yes. And we’ll turn it on and it was 1 of the 1st times and only times in my life when the chart was going like this, we made all the changes and it went like that. It was a perfect fit. It worked like a charm and then, a month later, I guess it was about 6 months later, the CEO came around and said, wow, you guys really knew your stuff.[00:41:07] You really were able to help us. Turn this around and make it a benefit and we turned it around faster than any of the competitors did. And then he said, well, what would you like to do next? And I said, well, I resigned last week. So, , I’m going to go do it somewhere else.[00:41:22] And he’s like, what? You just made a huge difference in the business. And I said, yeah, you didn’t pay me anymore. You didn’t recognize me. And I’ve been here for nearly 4 years, and I’ve had to fight you tooth and nail for everything. I’m tired of it.[00:41:34] Mark Stouse: Yeah. That’s what’s called knowing your value. One of the things that I think is so ironic about this entire conversation is that if any function has the skillsets necessary to forecast and demonstrate their value as multipliers. Of business decisions, decision quality, decision outcomes it’s data science.[00:42:05] And yet they just kind of. It’s like not there. And when you say that to them, they kind of look at you kind of like, did you really just say that, and so it is, one of the things that I’ve learned from analytics is that in the average corporation, you have linear functions that are by definition, linear value creators.[00:42:32] Sales would be a great example. And then you have others that are non linear multipliers. Marketing is one, data science is another, the list is long, it’s always the non linear multipliers that get into trouble because they don’t know how to show their value. In the same way that a linear creator can show it[00:42:55] John Thompson: And I think that’s absolutely true, Mark. And what I’ve been saying, and Bill’s heard this until he’s sick of it. Is that, , data science always has to be denominated in currency. Always, if you can’t tell them in 6 months, you’re going to double the sales or in 3 months, you’re going to cut cost or in, , 5 months, you’re going to have double the customers.[00:43:17] If you’re not denominating that in currency and whatever currency they care about, you’re wasting your time.[00:43:23] Dr Genevieve Hayes: The problem is, every single data science book tells you that the metrics to evaluate models by are, precision, recall, accuracy, et[00:43:31] John Thompson: Yeah, but that’s technology. That’s not business.[00:43:34] Dr Genevieve Hayes: exactly. I’ve only ever seen one textbook where they say, those are technical metrics, but the metrics that really count are the business metrics, which are basically dollars and cents.[00:43:44] John Thompson: well, here’s the second one that says it.[00:43:46] Dr Genevieve Hayes: I will read that. For the audience it’s Business Analytics Teams by John Thompson.[00:43:51] John Thompson: building analytics[00:43:52] Dr Genevieve Hayes: Oh, sorry, Building[00:43:54] Mark Stouse: But, but I got to tell you seriously, the book that John wrote that everybody needs to read in business. Okay. Not just data scientists, but pretty much everybody. Is about causal AI. And it’s because almost all of the questions. In business are about, why did that happen? How did it happen? How long did it take for that to happen?[00:44:20] It’s causal. And so, I mean, when you really look at it that way and you start to say, well, what effects am I causing? What effects is my function causing, all of a sudden the scales kind of have a way of falling away from your eyes and you see things. Differently.[00:44:43] John Thompson: of you to say that about that book. I appreciate that.[00:44:46] Mark Stouse: That kick ass book, kick[00:44:48] John Thompson: Well, thank you. But, most people don’t understand that we’ve had analytical or foundational AI for 70 years. We’ve had generative AI for two, and we’ve had causal for a while, but only people understand it are the people on this call and Judea Pearl and maybe 10 others in the world, but we’re moving in a direction where those 3 families of AI are going to be working together in what I’m calling composite AI, which is the path to artificial, or as Bill says, average general intelligence or AGI.[00:45:24] But there are lots of eight eyes people talk about it as if it’s one thing and it’s[00:45:29] Mark Stouse: Yeah, correct. That’s right.[00:45:31] Dr Genevieve Hayes: I think part of the problem with causal AI is it’s just not taught in data science courses.[00:45:37] John Thompson: it was not taught anywhere. The only place it’s taught is UCLA.[00:45:40] Mark Stouse: But the other problem, which I think is where you’re going with it Genevieve is even 10 years ago, they weren’t even teaching multivariable linear regression as a cornerstone element of a data science program. So , they basically over rotated and again, I’m not knocking it.[00:46:01] I’m not knocking machine learning or anything like that. Okay. But they over rotated it and they turned it into some sort of Omni tool, that could do it all. And it can’t do it all.[00:46:15] Dr Genevieve Hayes: think part of the problem is the technical side of data science is the amalgamation of statistics and computer science . But many data science university courses arose out of the computer science departments. So they focused on the machine learning courses whereas many of those things like.[00:46:34] multivariable linear analysis and hypothesis testing, which leads to things like causal AI. They’re taught in the statistics courses that just don’t pop up in the data science programs.[00:46:46] Mark Stouse: Well, that’s certainly my experience. I teach at USC in the grad school and that’s the problem in a nutshell right there. In fact, we’re getting ready to have kind of a little convocation in LA about this very thing in a couple of months because it’s not sustainable.[00:47:05] Bill Schmarzo: Well, if you don’t mind, I’m going to go back a second. We talked about, measuring success as currency. I’m going to challenge that a little bit. We certainly need to think about how we create value, and value isn’t just currency. John held up a book earlier, and I’m going to hold up one now, Wealth of Nations,[00:47:23] John Thompson: Oh yeah.[00:47:25] Bill Schmarzo: Page 28, Adam Smith talks about value he talks about value creation, and it isn’t just about ROI or net present value. Value is a broad category. You got customer value, employee value, a partner stakeholder. You have society value, community value of environmental value.[00:47:43] We have ethical value. And as we look at the models that we are building, that were guided or data science teams to build, we need to broaden the definition of value. It isn’t sufficient if we can drive ROI, if it’s destroying our environment and putting people out of work. We need to think more holistically.[00:48:04] Adam Smith talks about this. Yeah, 1776. Good year, by the way, it’s ultimate old school, but it’s important when we are As a data science team working with the business that we’re broadening their discussions, I’ve had conversations with hospitals and banks recently. We run these workshops and one of the things I always do, I end up pausing about halfway through the workshop and say, what are your desired outcomes from a community perspective?[00:48:27] You sit inside a community or hospital. You have a community around you, a bank, you have a community around you. What are your desired outcomes for that community? How are you going to measure success? What are those KPIs and metrics? And they look at me like I got lobsters crawling out of my ears.[00:48:40] The thing is is that it’s critical if we’re going to Be in champion data science, especially with these tools like these new ai tools causal predictive generative autonomous, these tools allow us to deliver a much broader range of what value is And so I really rail against when somebody says, you know, and not trying to really somebody here but You know, we gotta deliver a better ROI.[00:49:05] How do you codify environmental and community impact into an ROI? Because ROI and a lot of financial metrics tend to be lagging indicators. And if you’re going to build AI models, you want to build them on leading indicators.[00:49:22] Mark Stouse: It’s a lagging efficiency metric,[00:49:24] Bill Schmarzo: Yeah, exactly. And AI doesn’t do a very good job of optimizing what’s already happened.[00:49:29] That’s not what it does.[00:49:30] John Thompson: sure.[00:49:31] Bill Schmarzo: I think part of the challenge, you’re going to hear this from John and from Mark as well, is that we broaden this conversation. We open our eyes because AI doesn’t need to just deliver on what’s happened in the past, looks at the historical data and just replicates that going forward.[00:49:45] That leads to confirmation bias of other things. We have a chance in AI through the AI utility function to define what it is we want our AI models to do. from environmental, society, community, ethical perspective. That is the huge opportunity, and Adam Smith says that so.[00:50:03] John Thompson: There you go. Adam Smith. I love it. Socrates, Aristotle, Adam[00:50:08] Bill Schmarzo: By the way, Adam Smith motivated this book that I wrote called The Economics of Data Analytics and Digital Transformation I wrote this book because I got sick and tired of walking into a business conversation and saying, Data, that’s technology. No, data, that’s economics.[00:50:25] Mark Stouse: and I’ll tell you what, you know what, Genevieve, I’m so cognizant of the fact in this conversation that the summer can’t come fast enough when I too will have a book,[00:50:39] John Thompson: yay.[00:50:41] Mark Stouse: yeah, I will say this, One of the things that if you use proof, you’ll see this, is that there’s a place where you can monetize in and out of a model, but money itself is not causal. It’s what you spend it on. That’s either causal or in some cases, not[00:51:01] That’s a really, really important nuance. It’s not in conflict with what John was saying about monetizing it. And it’s also not in conflict with what. My friend Schmarrs was saying about, ROI is so misused as a term in business. It’s just kind of nuts.[00:51:25] It’s more like a shorthand way of conveying, did we get value[00:51:31] John Thompson: yeah. And the reason I say that we denominated everything in currency is that’s generally one of the only ways. to get executives interested. If you go in and say, Oh, we’re going to improve this. We’re going to improve that. They’re like, I don’t care. If I say this project is going to take 6 months and it’s going to give you 42 million and it’s going to cost you nothing, then they’re like, tell me more, and going back to what Bill had said earlier, we need to open our aperture on what we do with these projects when we were at Dell or Bill and I swapped our times at Dell, we actually did a project with a hospital system in the United States and over 2 years.[00:52:11] We knocked down the incidence of post surgical sepsis by 72%. We saved a number of lives. We saved a lot of money, too, but we saves people’s lives. So analytics can do a lot. Most of the people are focused on. Oh, how fast can we optimize the search engine algorithm? Or, how can we get the advertisers more yield or more money?[00:52:32] There’s a lot of things we can do to make this world better. We just have to do it.[00:52:36] Mark Stouse: The fastest way to be more efficient is to be more effective, right? I mean, and so when I hear. CEOs and CFOs, because those are the people who use this language a lot. Talk about efficiency. I say, whoa, whoa, hold on. You’re not really talking about efficiency. You’re talking about cost cutting.[00:52:58] Those two things are very different. And it’s not that you shouldn’t cut costs if you need to, but it’s not efficiency. And ultimately you’re not going to cut your way into better effectiveness. It’s just not the way things go.[00:53:14] John Thompson: Amen.[00:53:15] Mark Stouse: And so, this is kind of like the old statement about physicists,[00:53:18] if they’re physicists long enough, they turn into philosophers. I think all three of us, have that going on. Because we have seen reality through a analytical lens for so long that you do actually get a philosophy of things.[00:53:38] Dr Genevieve Hayes: So what I’m hearing from all of you is that for data scientists to create value for the businesses that they’re working for, they need to start shifting their approach to basically look at how can we make the businesses needs. And how can we do that in a way that can be expressed in the business’s language, which is dollars and cents, but also, as Bill pointed out value in terms of the community environment.[00:54:08] So less financially tangible points of view.[00:54:11] Bill Schmarzo: And if I could just slightly add to that, I would say first thing that they need to do is to understand how does our organization create value for our constituents and stakeholders.[00:54:22] Start there. Great conversation. What are our desired outcomes? What are the key decisions? How do we measure success? If we have that conversation, by the way, it isn’t unusual to have that conversation with the business stakeholders and they go I’m not exactly sure.[00:54:37] John Thompson: I don’t know how that works.[00:54:38] Bill Schmarzo: Yeah. So you need to find what are you trying to improve customer retention? You’re trying to increase market share. What are you trying to accomplish and why and how are you going to measure success? So the fact that the data science team is asking that question, because like John said, data science can solve a whole myriad of problems.[00:54:54] It isn’t that it can’t solve. It can solve all kinds. That’s kind of the challenge. So understanding what problems we want to solve starts by understanding how does your organization create value. If you’re a hospital, like John said, reducing hospital acquired infections, reducing long term stay, whatever it might be.[00:55:09] There are some clear goals. Processes initiatives around which organizations are trying to create value[00:55:18] Dr Genevieve Hayes: So on that note, what is the single most important change our listeners could make tomorrow to accelerate their data science impact and results?[00:55:28] John Thompson: I’ll go first. And it’s to take your data science teams and not merge them into operational teams, but to introduce the executives that are in charge of these areas and have them have an agreement that they’re going to work together. Start there.[00:55:46] Bill Schmarzo: Start with how do you how does the organization create value? I mean understand that fundamentally ask those questions and keep asking until you find somebody in the organization who can say we’re trying to do this[00:55:57] Mark Stouse: to which I would just only add, don’t forget the people are people and they all have egos and they all want to appear smarter and smarter and smarter. And so if you help them do that, you will be forever in there must have list, it’s a great truth that I have found if you want to kind of leverage bills construct, it’s the economies of ego.[00:56:24] Bill Schmarzo: I like[00:56:24] John Thompson: right, Mark, wrap this up. When’s your book coming out? What’s the title?[00:56:28] Mark Stouse: It’s in July and I’ll be shot at dawn. But if I tell you the title, but so I interviewed several hundred fortune, 2000 CEOs and CFOs about how they see go to market. The changes that need to be made in go to market. The accountability for it all that kind of stuff. And so the purpose of this book really in 150, 160 pages is to say, Hey, they’re not all correct, but this is why they’re talking to you the way that they’re talking to you, and this is why they’re firing.[00:57:05] People in go to market and particularly in B2B at an unprecedented rate. And you could, without too much deviation, do a search and replace on marketing and sales and replace it with data science and you’d get largely the same stuff. LinkedIn,[00:57:25] Dr Genevieve Hayes: for listeners who want to get in contact with each of you, what can they do?[00:57:29] John Thompson: LinkedIn. John Thompson. That’s where I’m at.[00:57:32] Mark Stouse: Mark Stouse,[00:57:34] Bill Schmarzo: And not only connect there, but we have conversations all the time. The three of us are part of an amazing community of people who have really bright by diverse perspectives. And we get into some really great conversations. So not only connect with us, but participate, jump in. Don’t be afraid.[00:57:51] Dr Genevieve Hayes: And there you have it, another value packed episode to help you turn your data skills into serious clout, cash, and career freedom. If you found today’s episode useful and think others could benefit, please leave us a rating and review on your podcast platform of choice. That way we’ll be able to reach more data scientists just like you.[00:58:11] Thanks for joining me today, Bill, Mark, and John.[00:58:16] Mark Stouse: Great being with[00:58:16] John Thompson: was fun.[00:58:18] Dr Genevieve Hayes: And for those in the audience, thanks for listening. I’m Dr. Genevieve Hayes, and this has been value driven data science. The post Episode 53: A Wake-Up Call from 3 Tech Leaders on Why You're Failing as a Data Scientist first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

What happens when an industry as heavily regulated and historically slow-moving as pharma is forced to accelerate digital transformation? In today's episode, I welcome Florian Schnappauf, Vice President of Enterprise Commercial Strategy at Veeva Systems, to discuss how Chief Digital Officers (CDOs) are reshaping the pharmaceutical landscape and why their role is now more critical than ever. The pharmaceutical sector faces mounting pressure to innovate faster, manage costs, and compete with digital-first biotechs. Research predicts the industry will spend $4.5 billion on digital transformation by 2030, a shift that has led to the emergence of CDOs in the pharma C-suite. But what does this role actually entail, and how does it help companies navigate the complexities of drug development, clinical trials, and commercialization? Florian shares insights on how CDOs are not just supporting digital initiatives but actively orchestrating, building, and operating them. From managing the sheer volume of data generated by clinical trials to ensuring that digital tools enhance—not hinder—the drug development process, the CDO is now a key differentiator between industry leaders and laggards. We also explore how effective digital leadership can shorten timeframes from drug discovery to patient treatment, improve communication with healthcare providers, and ultimately ensure that pharma companies achieve more with fewer resources. With regulatory hurdles, technological advancements, and shifting market dynamics, the pharmaceutical industry is at a pivotal moment. So, what does the future hold for digital leaders in pharma? How will CDOs continue to evolve, and what lessons can other industries learn from their journey? Join us as we break down the digital transformation of pharma and the leadership required to drive meaningful change. And as always, I'd love to hear your thoughts—do you think pharma is adapting quickly enough, or is there still a long way to go? Check out the What Pharma Needs Next podcast.  

Episode OverviewData leaders have a massive opportunity to drive transformational value with AI, but many are running on outdated operating models. On this episode of the CDO Matters Podcast, Katharine Shaw Paffett, the Cross Solution AI lead for UK and Ireland at Avanade, shares insights on how CDOs can re-envision their organizations to be more AI-ready. Katharine is at the forefront of the early adoption of GenAI for many large organizations, and her guidance for any company seeking to implement AI is not to be missed.Episode Links and ResourcesFollow Malcolm Hawker on LinkedInFollow Katharine Shaw Paffett on LinkedIn

Tom Bodrovics welcomes back long-term contrarian investor and entrepreneur Simon Mikhailovich for a discussion centered around first principles, focusing on precious metals, commodities, economics, geopolitics, trade, and monetary matters. The conversation begins with the acknowledgement of high levels of uncertainty and complexity, making accurate forecasts challenging. Mikhailovich distinguishes between speculating on precious metals versus using them as a reserve asset. For speculation, market drivers are pertinent. However, for gold as a reserve asset, its unique property as the only financial asset without a counterparty makes it inversely correlated to confidence and trust in other people's promises. The conversation touches upon the concept of the fourth turning and where we are in this cycle. Mikhailovich underscores the significance of understanding current problems before predicting future demand for gold. He also discusses how post-World War II arrangements have led to the United States' hegemonic role economically and militarily, and the start of financialization and globalization. Mikhailovich raises concerns about understated inflation and its potential impact on real economic growth or contraction. He also highlights the lack of clear guidance from Federal Reserve Chairman Jay Powell in navigating through uncertain conditions. They explore the winners and losers of the global economy, with tactical gains for Wall Street investors, technology industries, and certain countries like China. However, working people have been losing due to job outsourcing. Mikhailovich mentions China's growing power and desire for independence from the United States as potential challenges to the current economic order. The conversation delves into geopolitical tensions in the Middle East, with borders becoming less inviolable after World War One and World War Two. The Suez Canal's declining traffic and resulting increased costs serve as an example of inflationary pressures. Mikhailovich discusses the significance of gold as a financial asset and its increasing demand, particularly from China and other countries, as a response to a loss of confidence in the global financial system. He also mentions the relationship between digital currencies like Bitcoin and the US dollar, suggesting that regulatory actions could impact their independence from the dollar and the broader financial system. Lastly, Simon emphasizes understanding the complexities, considering various data points, focusing on resiliency, and looking at first principles. Time Stamp References:0:00 - Introduction0:44 - Uncertainties & Metals4:22 - The Fourth Turning9:00 - Statistics & Reality17:00 - Wars, Rumors & Borders26:47 - Economic Fragility33:55 - Gold & Eastern Buying38:30 - Trump & U.S. Dollar41:18 - Gold & Confidence50:07 - Trump & Bond Markets53:56 - World Has Changed1:03:02 - Inflation Vs. Panic1:05:20 - Socialism & Competence1:10:02 - A Serious Situation1:13:13 - Wrap Up Talking Points From This Episode Gold as a reserve asset is inversely correlated to confidence in other people's promises. Understanding current problems before predicting future demand for gold is crucial. Concerns about understated inflation, lack of clear guidance from Jay Powell, and China's growing power pose challenges. Guest Links:Twitter: https://c.com/S_MikhailovichWebsite: https://www.bullionreserve.com Simon A. Mikhailovich is a co-founder, lead manager of The Bullion Reserve, and a director. Mr. Mikhailovich is an entrepreneur and contrarian investor who predicted and profited from the financial crises of 2000 and 2008. Before co-founding TBR in 2014, Mr. Mikhailovich co-founded Eidesis Capital, a special situations investment firm. Between 1998 and 2014, the Eidesis team deployed over $2.5B of capital through special opportunity funds focused on high yield corporate bonds and loans, credit derivatives, distressed CDOs and MBS, and gold.

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P.M. Edition for Oct. 23. Matt Wirz, who writes about credit for The Wall Street Journal talks about why Wall Street is excited about NAVs, SRTs and CDOs. And U.S. home sales hit another nearly 30-year low. Journal housing reporter Nicole Friedman explains why new buyers are staying on the housing market sidelines. Plus, with deadlocked polls and the memory of 2016, White House reporter Tarini Parti says Democrats are becoming more anxious ahead of Election Day. Tracie Hunte hosts. Sign up for the WSJ's free What's News newsletter. Learn more about your ad choices. Visit megaphone.fm/adchoices

Follow me @samirkaji for my thoughts on the venture market, with a focus on the continued evolution of the VC landscape.Today we're thrilled to be joined by Glenn Solomon, managing partner at Notable Capital. Along with Granite Asia, Notable Capital was one of two groups to emerge from GGV Capital, which recently split into two groups with Notable based in Silicon Valley, New York, and covering companies in the U. S., Israel, Europe, and Latin America.Glenn brings nearly 30 years of venture experience to the table, and it was great to draw from his insights in investing, building firms, and working with high performing teams. About Glenn Solomon:Glenn Solomon is the Managing Partner at Notable Capital. He focuses on investing in early to growth-stage companies across different sectors, including cloud infrastructure and business applications. He also serves on the boards of several companies, such as HashiCorp, Opendoor.com, and Orca Security.Before joining Notable, Glenn was a General Partner at Partech International from 1997 to 2006, where he worked on technology investments. Earlier in his career, he was an associate at SPO Partners from 1993 to 1995 and started as a financial analyst at Goldman Sachs from 1991 to 1993.Glenn Solomon earned his MBA and BA from Stanford University.In this episode, we discuss:(01:42) Glenn's journey from playing tennis at Stanford to discovering a passion for technology and investing(02:44) A pivotal moment when encountering the internet for the first time, which sparked a deeper interest in technology(04:06) The transition from Partech International to joining Granite Global Ventures in the mid-2000s(05:03) The appeal of GGV's global perspective and innovative approach in venture capital(07:48) The early strategy at GGV, focusing on differentiation in the venture space(09:01) The necessity of adapting to the evolving nature of the industry(10:29) The rebranding to Notable Capital and the strategic decisions following the split from GGV's Asia team(12:39) The guiding principles at Notable Capital, emphasizing the importance of speed and maintaining a sector-focused strategy(15:19) An example of a recent deal showcasing how the firm's flat structure empowers all team members to contribute significantly(17:33) Staying focused on specific sectors and building a strong support platform for portfolio companies(23:25) Engaging with CSOs and CDOs to maintain an edge in cybersecurity and data sectors.(27:00) Discusses the importance of resourcefulness in venture capital and how they assess this quality during interviews.(36:31) Advice on being a successful VC, stressing the critical role of building strong, lasting relationships(39:30) Success in venture capital fundamentally relies on working with exceptional peopleI'd love to know what you took away from this conversation with Glenn. Follow me @SamirKaji and give me your insights and questions with the hashtag #ventureunlocked. If you'd like to be considered as a guest or have someone you'd like to hear from (GP or LP), drop me a direct message on Twitter.Podcast Production support provided by Agent Bee This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit ventureunlocked.substack.com