Podcasts about Barc

"Right now, AI seems like the wild, wild West – and I would recommend to show a little conservative decision making before you run off and do wild things with AI."

Toda la actualidad deportiva, con Rocío Martínez y Edu Pidal. La selección prepara el Mundial en Las Rozas y jugará el próximo jueves su primer amistoso, Enrique Riquelme anuncia a Raúl como su director deportivo si gana las elecciones a la presidencia del Real Madrid, el Barça sigue apretando por el fichaje de Julián Alvarez, y lío en el Sevilla con la oferta de Sergio Ramos para comprar el club.

Podcast: Tech TransformedGuest: Mihir Nanavati, GM and Product Executive in MarTech and AdTechHost: Doug Laney, Research & Advisory Fellow at BARC and Author of Infonomics & Data JuiceAI might have overtaken the industry with processing data, automating workflows, and creating content. The next big thing could be a major one, says Mihir Nanavati, GM and Product Executive in MarTech and AdTech, “AI is moving from managing data to making decisions with it.”In the recent episode of the Tech Transformed podcast, host Doug Laney, Research & Advisory Fellow at BARC and Author of Infonomics & Data Juice, sat down with Nanavati to talk about a larger transformation in data and decision-making systems driven by AI.They particularly focus on the integration of agentic AI in marketing and customer data platforms. They explore the challenges of fragmentation in ad tech, the importance of connecting customer data to revenue outcomes, and the transformative role of AI in decision-making processes. Mihir shares insights on how companies can leverage AI to enhance their marketing strategies and the future of first-party data."This is not a cost exercise, it's about how much more you can get done and how many more ideas you can execute," said Nanavati.For years, enterprises went through waves of technological change, including cloud infrastructure, mobile platforms, and customer data platforms (CDPs). Each development helped enterprises collect, store, and manage larger amounts of data. However, Nanavati asserts that humans making most decisions will never change. Now, AI agents are introducing a new model.How AI has Moved from Data Navigation to Making DecisionsIn the past, customer data initiatives aimed to create a unified view of customers. Enterprises built warehouses, ETL pipelines, and data platforms that were designed to be reliable. However, Nanavati suggests that AI agents are changing these expectations. "Machines can reason, and that is fundamentally different."Rather than simply serving as another analytical feature in existing systems, AI agents are increasingly acting as decision-makers. They weigh trade-offs, learn from results, and execute plans based on specific goals.This change has significant implications for customer data platforms. CDPs are not just repositories for customer information now. Instead, they are becoming layers that enable intelligent actions."The role of customer data platforms is evolving into ‘how do you make meaning of this?'" While, decisions about which customer segment to target, which message to send, or which offer to present may increasingly be guided by AI-driven systems.What's the Fragmentation Problem in Modern AdTechWhile AI agents create new opportunities, Nanavati pointed out a persistent issue in the AdTech and MarTech ecosystem – fragmentation. Brands today tend to lean towards deploying multiple advertising and customer engagement platforms. These include social platforms, retail media networks, email tools, and specialised ad technologies. Each system may optimise effectively within its own space, but often fails to connect at the customer level.Nanavati calls it a "paradox of choice." "Each system is optimising locally for its own clicks and conversions, but none of that is coordinated at the consumer level."The result is a customer experience that many consumers notice, alluding to repeated retargeting for products they have already bought, irrelevant recommendations, or disconnected interactions across channels.As enterprises adopt AI agents, fragmented data environments may become an even bigger problem. AI systems can process information quickly, but they still rely heavily on context. "AI doesn't need perfect data in many cases, but it needs context."What's Next for Enterprise Tech?As AI adoption continues, Nanavati believes that successful enterprises will be recognised not by how many experiments they run, but by how fast they learn and use the results."Learn very rapidly. Then scale what you've learned." For leaders, this may require a stronger commitment than just isolated pilot programs or limited rollouts. It may also need organisational changes that place AI decision-making and customer context at the centre of growth strategies.For companies navigating the intersection of AI agents, CDPs, and customer data, the question may no longer be whether AI can automate processes. The ultimate question is about who is calling the shots.Key TakeawaysAI is fundamentally changing how decisions are made in marketing.The shift from third-party to first-party data is crucial for businesses.Fragmentation in ad tech leads to a paradox of choice for brands.Connecting customer data to revenue outcomes is essential for success.AI can help marketers make better decisions without needing perfect data.Customer data platforms are evolving to support real-time decision-making.Companies can run significantly more marketing experiments with AI.Leaders must personally drive change in their Enterprises.Successful AI implementation requires a focus on revenue outcomes.First-party data collection is becoming more sophisticated and essential.Chapters00:00 Navigating the Shift in Data and AI03:03 The Evolution of Decision-Making in Marketing05:55 Challenges of Fragmentation in Ad Tech09:00 Connecting Customer Data to Revenue Outcomes11:56 The Role of AI in Customer Data Platforms14:55 Real-World Applications of Agentic AI18:05 Blueconic's Approach to Customer Growth21:14 The Future of First-Party Data24:02 Building Habits for Successful AI ImplementationListen to the full episode of Tech Transformed for a deeper discussion on AI agents, customer data platforms (CDPs), first-party data strategies and the future of AdTech. Subscribe for upcoming episodes and join the conversation across our social channels.BlueConic LinkedIn: @BlueConicEM360Tech YouTube: @enterprisemanagement360EM360Tech LinkedIn: @EM360TechEM360Tech X: @EM360TechFor more information, please visit em360tech.com and blueconic.com.

Toda la actualidad deportiva, con Rocío Martínez y Edu Pidal. El Rayo se prepara para jugar la primera final europea de su historia, repasamos las novedades en las elecciones a la presidencia del Real Madrid, la última hora del Barça que despide a Alexia Putellas, y hablamos con Monchi sobre su llegada a la dirección deportiva del Espanyol.

Toda la actualidad deportiva, con Rocío Martínez y Edu Pidal. Repasamos la última hora del Barça en clave fichajes para el próximo verano y de las elecciones del Real Madrid para las que Enrique Riquelme ultima su candidatura para hacer frente a Florentino Pérez. Además, hablamos con Damià Vidagany, director de fútbol del Aston Villa, en la previa de la final de la Europa League; con Jose Alberto, técnico del Racing recién ascendido a Primera División; y con un aficionado del Rayo Vallecano víctima de una estafa con la compra de unas entradas para final de la Conference League.

Dr Anil Kakodkar is one of the senior-most living architects of India's atomic energy programme and a Padma Vibhushan awardee. He joined the Bhabha Atomic Research Centre in 1964. He served as Director of BARC from 1996 to 2000 and as Chairman of the Atomic Energy Commission and Secretary, Department of Atomic Energy, from 2000 to 2009.He was among the small group of scientists at Pokhran for India's first nuclear test — Smiling Buddha — on 18 May 1974, and played a central role a quarter-century later in the five Pokhran-II nuclear tests in May 1998 that established India as a declared nuclear weapons state.As a working engineer through the long sanctions era, he designed and built the Dhruva research reactor entirely indigenously, led the development of pressurised heavy water reactor (PHWR) systems that today form the backbone of India's civilian fleet, and rehabilitated Units 1 and 2 of the Madras Atomic Power Station after the 1989 failure of their moderator inlet manifolds — both reactors had been on the verge of being written off. He conceptualised the Advanced Heavy Water Reactor (AHWR), a 300 MW thorium-fuelled design that remains central to India's three-stage nuclear power programme.His team at BARC designed the miniaturised 83 MW pressurised light water reactor that powers INS Arihant, completing India's nuclear triad. Between 2005 and 2008, he was the technical anchor of the Indian negotiating team — alongside Manmohan Singh, Pranab Mukherjee, Shivshankar Menon and Shyam Saran — that delivered the 123 Agreement with the United States, the India-IAEA safeguards agreement, and the September 2008 Nuclear Suppliers Group waiver that ended three decades of India's nuclear isolation.A lifelong champion of thorium as the foundation of India's long-term energy sovereignty — India holds roughly a quarter of the world's known thorium reserves — he has continued to argue, well into his eighties, that abandoning the thorium path would be a serious strategic error. Beyond nuclear, he has chaired the Board of Governors of IIT Bombay, led high-level committees on Indian Railways safety and Maharashtra higher education, helped establish NISER and the Homi Bhabha National Institute, and currently chairs Maharashtra Knowledge Corporation Limited.

Podcast: Tech TransformedGuests: Maxim Fateev, Co-Founder and CTO, Temporal Technologies and Cornelia Davis, Developer Advocate, Temporal TechnologiesHost: Kevin Petrie, VP of Research at BARCArtificial Intelligence (AI) models have been breaking ground in the last three years. In the race to boost capabilities month by month among platforms like OpenAI, Anthropic, and Google's Gemini models. However, for many enterprises, the main challenge is not creating AI prototypes; it's ensuring they can reliably support real business processes.In a recent episode of the Tech Transformed podcast, Kevin Petrie, VP of Research at BARC, hosted a discussion with Maxim Fateev, Co-Founder and CTO, Temporal Technologies and Cornelia Davis, Developer Advocate, Temporal Technologies. They talked about why enterprises find it hard to transition AI from experimentation to production and how infrastructure must change to support autonomous systems.Why AI Demos Break in the Real WorldAccording to Davis, many organisations make a common mistake: they focus on the "happy path" during experiments and overlook real-world operational challenges. “We have always ignored the non-functional requirements until we go to prod at our peril,” Davis said. “A lot of our experimentation is so focused on the models that we forget about the non-functional requirements.”This means developers often prioritise model performance but neglect reliability, scaling, and system resilience. Agent frameworks used in experiments—usually lightweight Python or TypeScript libraries—add to the issue.“What you're really building is a highly distributed system that's calling Large Language Models (LLMs) that will be rate-limited… networks are going to go down,” Davis explained. “When we move into prod, we haven't considered scale or instability.”As enterprises expand AI into their workflows, these overlooked details become imperative. A single outage, rate limit, or infrastructure failure can disrupt a complicated workflow that involves multiple AI steps.Also Watch: Developer Productivity 5X to 10X: Is Durable Execution the Answer to AI Orchestration Challenges?What Risks are Surfacing Since the Rise of Agentic Systems?The transition from simple AI workflows to autonomous agents adds a new layer of complexity. Traditional AI applications have predictable flows—such as summarising documents, tagging data, or creating recommendations. In contrast, agentic systems choose tools and decide on actions dynamically.“When we move from non-agentic to agentic, we introduce unpredictability,” Davis said. “The tools and the order they run in are unpredictable. Whether we go through the agentic loop once or a hundred times is unpredictable.”Such unpredictability creates new governance and compliance challenges, especially in regulated industries. “Enterprises are still responsible for predictable outcomes,” Davis noted. “We need stronger audit trails to understand why the agent made the decisions it did.”For enterprises, this means AI systems must ensure traceability, accountability, and compliance, even when decision paths differ from one interaction to another.Why is Durable Execution the New Foundation for Enterprise AIFateev argues that to manage such newly surfacing risks, enterprises need a new architectural layer focused on reliability. His concept, “Durable Execution,” aims to ensure that complex workflows keep running even when infrastructure fails.“You write code as if failures don't exist,” Fateev explained. “If a process crashes, we recover all the state and continue executing.” In practical terms, Durable Execution allows long-running AI workflows to survive interruptions—from network outages to system crashes—without losing progress or data.This is essential as agents start interacting with real systems and taking real actions. “The moment agents start acting on the external world—changing files, submitting orders—you absolutely don't want those things to get lost,” Fateev said.The Temporal co-founder further emphasised that enterprise AI will not completely replace traditional software systems.“You will always have deterministic code,” he said. “You can't imagine banks dynamically deciding what a money transfer means.”Instead, the future architecture will combine deterministic software with agents that interact through controlled tools and reliable communication layers.Also Watch: How Do You Make AI Agents Reliable at Scale?Key TakeawaysAI projects fail in production when non-functional requirements are ignoredAgentic systems bring unpredictability, making governance, traceability, and auditability essential.Lightweight experimentation frameworks aren't suited for enterprise workloads.Durable execution enables reliable AI workflows, ensuring processes continue despite infrastructure failures.Enterprise AI will blend deterministic software with agents.Chapters00:00 Introduction to AI's Impact on Business03:53 Challenges in Integrating AI into Business Workflows13:00 Understanding Non-Functional Requirements in AI19:14 The Role of Orchestration in AI Systems24:26 Exploring Durable Execution in AI Workflows30:28 Future Architectures for Autonomous AI Systems36:05 Key Takeaways for Executives in AI ImplementationFor more information, please visit em360tech.com and temporal.io.To learn more about Temporal and Durable Execution, follow:Temporal LinkedIn: Temporal TechnologiesTemporal X: @TemporalioTemporal YouTube: @TemporalioEM360Tech YouTube: @enterprisemanagement360EM360Tech LinkedIn: @EM360TechEM360Tech X: @EM360Tech#DurableExecution #EnterpriseAI #AIToProduction #AIOrchestration #TemporalTech #AutonomousAgents #SystemReliability #LLMs #TechTransformed #AIWorkflows

El incidente entre ambos jugadores, que acabó con el uruguayo en el hospital, deja muy tocado al equipo de Arbeloa a escasos días de disputar un Clásico en el Camp Nou en el que el Barça puede ganar LaLiga.

Toda la actualidad deportiva, con Rocío Martínez y Edu Pidal. El PSG jugará su segunda final de Champions consecutiva tras eliminar al Bayern. Los de Luis Enrique se citan con el Arsenal el próximo 30 de mayo en Budapest. Además, analizamos la crisis desatada en el vestuario del Real Madrid, las horas posteriores a la eliminación en Champions del Atlético de Madrid, y la preparación del Barça para un Clásico del que puede salir campeón de Liga.

Me siento incapaz de descifrar la frase "en el fútbol hay demasiado hombre". La pronunció la ministra de Educación tras ser interpelada por si el deporte rey es uno de los escollos de su cartera. Seguiremos rascando, por si nos regala su señoría más hebritas de las que tirar y de paso, algún argumento.Demasiados puntos de diferencia entre los 2 gallos de la liga, eso sí, el Barça se vio obligado a retrasar alirón como poco, hasta el Clásico. El Madrid optó por susto y no muerte: el pasillo no… pero quizá fiesta 'titulera' sí sentados en primera línea del Camp Nou. Y es que los culés tienen demasiados motivos para empezar a descorchar: Hansi Flick, que se encontró el primer día con caras estupefactas (¿qué hace este alemán aquí?). La Masía, con esa máquina expendedora de talentosos (para la zaga, para ser cerebrito, para ser arietes con colmillos afilados). Los directivos, esos que intentan bajar la media de calidad de su equipo y casi nunca lo consiguen. Y, por supuesto, los aficionados, que siguen siendo el mejor patrimonio de nuestra liga, no hay encorbatado o política que merme sus capacidades.Demasiados sueños heredados para mañana en Londres. A todos los hombres y mujeres rojiblancas, el guionista planetario les debe una recompensa de Champions. Una final con final feliz estaría muy bien. Y para ello deseamos que aterricen en la casa del Arsenal con su óptima versión y los propósitos claros: mejor decidir que pastorear, mejor proponer que agachar, mejor atacar que obligarse a defender.¿Demasiados espectadores viendo el vuelo del Barça femenino? ¿60 mil seguidores jaleando a mujeres futbolistas que quieren y pueden reinar otra vez en el continente? Los hombres de can Barça no atinan con lo de Europa. Ellas, con maravillosa recurrencia. En fin. Demasiados "demasiados". Es lunes, perdón.

El análisis de la eliminación del Atlético de Madrid en semifinales de Champions y la última hora de Real Madrid y Barça con el Caso Mbappé y la preparación de un Clásico en el que el Barça puede ganar matemáticamente LaLiga.

Me siento incapaz de descifrar la frase "en el fútbol hay demasiado hombre". La pronunció la ministra de Educación tras ser interpelada por si el deporte rey es uno de los escollos de su cartera. Seguiremos rascando, por si nos regala su señoría más hebritas de las que tirar y de paso, algún argumento.Demasiados puntos de diferencia entre los 2 gallos de la liga, eso sí, el Barça se vio obligado a retrasar alirón como poco, hasta el Clásico. El Madrid optó por susto y no muerte: el pasillo no… pero quizá fiesta 'titulera' sí sentados en primera línea del Camp Nou. Y es que los culés tienen demasiados motivos para empezar a descorchar: Hansi Flick, que se encontró el primer día con caras estupefactas (¿qué hace este alemán aquí?). La Masía, con esa máquina expendedora de talentosos (para la zaga, para ser cerebrito, para ser arietes con colmillos afilados). Los directivos, esos que intentan bajar la media de calidad de su equipo y casi nunca lo consiguen. Y, por supuesto, los aficionados, que siguen siendo el mejor patrimonio de nuestra liga, no hay encorbatado o política que merme sus capacidades.Demasiados sueños heredados para mañana en Londres. A todos los hombres y mujeres rojiblancas, el guionista planetario les debe una recompensa de Champions. Una final con final feliz estaría muy bien. Y para ello deseamos que aterricen en la casa del Arsenal con su óptima versión y los propósitos claros: mejor decidir que pastorear, mejor proponer que agachar, mejor atacar que obligarse a defender.¿Demasiados espectadores viendo el vuelo del Barça femenino? ¿60 mil seguidores jaleando a mujeres futbolistas que quieren y pueden reinar otra vez en el continente? Los hombres de can Barça no atinan con lo de Europa. Ellas, con maravillosa recurrencia. En fin. Demasiados "demasiados". Es lunes, perdón.Conviértete en un supporter de este podcast: https://www.spreaker.com/podcast/mas-noticias--4412383/support.ESCUCHAR RADIO 

Most finance leaders have seen a BARC report before, downloaded it, skimmed the vendor rankings, and moved on. But BARC reports offer far more than a simple vendor comparison. They provide a strategic framework for evaluating your entire finance technology landscape. In this episode of CPM Customer Success, we break down two key pieces of research: the BARC Score for Financial Performance Management 2026 and the Financial Consolidation & Group Accounting Survey 26.  We explore what BARC is, how these reports are structured, and how finance leaders can use them to make smarter, more confident technology decisions.

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

Il weekend di Serie A, ultimissime su Inter-Como di Coppa Italia, Ausilio su Bastoni, il calciomercato e non solo.

El 25 de marzo de 1981 era liberado el futbolista del Barça, Enrique Castro 'Quini', después de pasar 25 días secuestrado por tres secuestradores no profesionales. Movistar + estrena una serie que narra estos hechos y que han venido a presentar en Más de uno, su director, Nacho García Velilla y el actor protagonista, Agustín Otón.

El 25 de marzo de 1981 era liberado el futbolista del Barça, Enrique Castro 'Quini', después de pasar 25 días secuestrado por tres secuestradores no profesionales. Movistar + estrena una serie que narra estos hechos y que han venido a presentar en Más de uno, su director, Nacho García Velilla y el actor protagonista, Agustín Otón.Conviértete en un supporter de este podcast: https://www.spreaker.com/podcast/mas-noticias--4412383/support.

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

Witam Państwa, nazywam się Jarosław Drożdż, pracuję w Centralnym Szpitalu Klinicznym Uniwersytetu Medycznego w Łodzi, skąd nagrywam podcast Kardio Know-How. W tym odcinku omawiam efekty połączenia leków hamujących krzepnięcie krwi.Po zawale ściany przedniej skrzeplina w lewej komorze występuje obecnie u 9–12% chorych (a w NMR nawet u ponad 16%), podczas gdy w erze przedtrombolitycznej sięgała 60%. Rezonans magnetyczny wykrywa skrzepliny nawet czterokrotnie częściej niż echo, a tylko około 1/3 zmian widoczna jest w badaniu echokardiograficznym. Link do pełnej wersji artykułu state of the art z roku 2022: https://www.jacc.org/doi/10.1016/j.jacc.2022.01.011Ticagrelor zmniejsza częstość skrzeplin względem klopidogrelu, dlatego pozostaje preferowanym składnikiem DAPT. W badaniu APERITIF opublikowanym 25 lutego 2026 w JAMA Cardiology oceniono dodanie naczyniowej dawki riwaroksabanu (2 × 2,5 mg) do DAPT po zawale ściany przedniej.https://jamanetwork.com/journals/jamacardiology/fullarticle/2845590W nowej analizie (ok. 560 pacjentów) redukcja skrzeplin była niewielka i statystycznie nieistotna (poniżej 14% vs >16%), przy większej liczbie głównie drobnych krwawień (BARC 1). Mimo częstego występowania skrzeplin zdarzenia zatorowe były rzadkie (ok. 1–1,5%), a śmiertelność poniżej 1%. W praktyce oznacza to, że rutynowe NMR ani dodawanie riwaroksabanu do DAPT z ticagrelorem tuż po zawale nie wydają się uzasadnione, a kluczowe pozostaje stosowanie skutecznej DAPT.Szczegółowy TRANSKRYPT do odcinka.Podcast jest przeznaczony wyłącznie dla osób z profesjonalnym wykształceniem medycznym.

"You need to have both the bottom-up experimentation to learn what's possible, and the top-down business view from executive level -- what do we want to do now and in the future?"

"Balanced Scorecards für AI müssen nicht konzernweit sein. Warum? Weil jede Abteilung mit AI eine andere Art von Wert erzeugen möchte und wird."

"It's a special crowd. Whoever makes the way to Davos, has some reason to be there."

For years, enterprises have discussed data democratisation as if it were an inevitable end goal. An assumption was made that turning on dashboards and training the business would lead to insight following naturally. But according to Barry McCardel, Co-Founder and CEO of Hex Technologies, the reality has been much more complicated.In the recent episode of the Don't Panic, It's Just Data podcast, McCardel joined host Kevin Petrie, VP Research and Head of Data Management at BARC, to talk about why access alone has never been enough. He also discussed how artificial intelligence (AI) is forcing the analytics community to rethink the purpose of data. The conversation dives into a familiar issue: how can organisations empower non-technical users without compromising data trust or overwhelming the technical teams responsible for it?“We've spent a decade pretending the problem was solved by self-service,” McCardel says. “But what we actually did was move complexity around instead of removing it.”As AI becomes part of analytics platforms, that complexity is finally being addressed. This includes long-standing beliefs about roles, ownership, and teamwork.Addressing the Myth of Data DemocratisationTracing many of the analytics issues faced by organisations in the present day, McCardel alludes to the early self-service BI, which promised that business users could explore data on their own. This was supposed to allow analysts and engineers to focus on more important tasks. In reality, the outcome often included duplicated logic, inconsistent metrics, and a widening trust gap between teams.“Access without context is chaos,” McCardel tells Petrie. “If everyone can answer questions, but everyone answers them differently, you haven't democratized anything; you've just created noise.”This issue has grown more urgent as organisations expand. Different roles—data engineers, analysts, data scientists, and business stakeholders—approach data with distinct goals and skills. Traditional tools forced everyone into the same interfaces, often designed for one group while ignoring the needs of the others.Petrie notes that many companies responded by adding layers of control, but this approach had drawbacks. Stricter guidelines slowed insight generation and pushed business users back into reliance on centralised teams.McCardel argues that the main problem isn't a lack of governance or tools but a lack of shared understanding. “We've treated analytics like a handoff,” he explains. “The data team builds it, the business consumes it. That model doesn't work when questions are fluid, and decisions are continuous.”He believes AI is revealing the limits of that model and providing a path forward.Also Watch: “Data Teams Suffer from Fragmentation” | Charles Schaefer @ Big Data LDN 2025AI is the Bridge, Not the ShortcutWhile much of the industry conversation about AI in analytics focuses on automation and natural language querying, the CEO of Hex is cautious about viewing AI as a quick fix. “If AI just gives you faster wrong answers, that's not progress,” he points out.Instead, he presents AI as...

Welcome to the third inning of the modern AI era and welcome to this week's theCUBE Research Insights, powered by ETR. In this special Breaking Analysis we assess the shift from the shock of “what is this Gen AI thing?” to “how do we make it work for us?” And how can we get agents to reliably take action to deliver the productivity gains the tech industry has promised. To do so, we're pleased to host our fifth annual data predictions power panel with collaborators from the Cube Collective, members of the Data Gang and some of the industry's leading data analysts. With us today are five industry experts focused on data and related topics. Sanjeev Mohan of Sanjmo, Tony Bear of DB Insight, Dave Menninger of ISG Research, Kevin Petrie of BARC, and Andrew Brust of Blue Badge Insights.

El director de Radioestadio subraya la fama de 'delfín' con la que llega Arbeloa a un vestuario en el que reina un ambiente catastrófico. Todo ello en contraste de la buena imagen que está dando el Barça.

El director de Radioestadio subraya la fama de 'delfín' con la que llega Arbeloa a un vestuario en el que reina un ambiente catastrófico. Todo ello en contraste de la buena imagen que está dando el Barça.Conviértete en un supporter de este podcast: https://www.spreaker.com/podcast/mas-noticias--4412383/support.

„Ich kann jetzt keine Datenkultur aufbauen, die der Unternehmenskultur entgegenwirkt.“

We live in a world where technology moves faster than most organisations can keep up. Every boardroom conversation, every team meeting, even casual watercooler chats now include discussions about AI. But here's the truth: AI isn't magic. Its promise is only as strong as the data that powers it. Without trust in your data, AI projects will be built on shaky ground.In this episode of Don't Panic, It's Just Data podcast, Amy Horowitz, Group Vice President of Solution Specialist Sales and Business Development at Informatica, joins moderator Kevin Petrie, VP of Research at BARC, to tackle one of the most pressing topics in enterprise technology today: the role of trusted data in driving responsible AI. Their discussion goes beyond buzzwords to focus on actionable insights for organisations aiming to scale AI with confidence.Why Responsible AI Begins with DataAmy opens the conversation with a simple but powerful observation: “No longer is it okay to just have okay data.” This sets the stage for understanding that AI's potential is only as strong as the data that feeds it. Responsible AI isn't just about implementing the latest algorithms; it's about embedding ethical and governance principles into every stage of AI development, starting with data quality.Kevin and Amy emphasise that organisations must look at data not as a byproduct, but as a foundational asset. Without reliable, well-governed data, even the most advanced AI initiatives risk delivering inaccurate, biased, or ineffective outcomes.Defining Responsible AI and Data GovernanceResponsible AI is more than compliance or policy checkboxes. As Amy explains, it is a framework of principles that guide the design, development, deployment, and use of AI. At its core, it is about building trust, ensuring AI systems empower organisations and stakeholders while minimising unintended consequences. Responsible data governance is the practical arm of responsible AI. It involves establishing policies, controls, and processes to ensure that data is accurate, complete, consistent, and auditable.Prioritise Data for Responsible AIThe takeaway from this episode is clear and that is responsible AI starts with responsible data. For organisations looking to harness AI effectively:Invest in data quality and governance — it is the foundation of all AI initiatives.Embed ethical and legal principles in every stage of AI development.Enable collaboration across teams to ensure transparency, accountability, and usability.Start small, prove value, and scale — responsible AI is built step by step.Amy Horowitz's insight resonates beyond the tech team: “Everyone's ready for AI — except their data.” It's a reminder that AI success begins not with the algorithms, but with the trustworthiness and governance of the data powering them.For more insights, visit Informatica.TakeawaysAI is only as good as its data inputs.Data quality has become the number one obstacle to AI success. Organisations must start small and find use cases for data governance.Hallucinations in AI models highlight the need for vigilant

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...

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Now that companies have begun leaping into AI applications and adopting agentic automation, new architectural challenges are bound to emerge. With every new technology comes high responsibility, consequences and challenges. To help face and overcome some of these challenges, Temporal introduced the concept of “durable execution.” This concept has quickly become an integral part of building AI systems that are not just scalable but also reliable, observable and manageable.In this episode of the Tech Transformed podcast, host Kevin Petrie, VP of Research at BARC, sits down with Samar Abbas, Co-founder and CEO of Temporal Technologies. They talk about durable execution and its critical role in driving AI innovation within enterprises. They discuss Abbas's extensive background in software resilience, the development of application architectures, and the importance of managing state and reliability in AI workflows. The conversation also touches on the collaboration between developers, data teams, and data scientists, emphasising how durable execution can enhance productivity and governance in AI initiatives.Also Watch: Developer Productivity 5X to 10X: Is Durable Execution the Answer to AI Orchestration Challenges?Chatbots to Autonomous Agents“AI agents are going to get more and more mission critical, more and more longer lived, and more asynchronous," Abbas tells Petrie. “They'll require more human interaction, and you need a very stable foundation to build these kinds of application architectures.”AI not just fuels chatbots today. Enterprises are increasingly experimenting with agentic workflows—autonomous AI agents that carry out complex background tasks independently. For example, agents can assign, solve, and submit software issues using GitHub pull requests. Such a setup isn't just a distant vision; the Temporal co-founder pointed to OpenAI's Codex as a real-world case. With this approach, AI becomes a system that can handle hundreds of tasks at once, potentially achieving "100x orders of magnitude velocity," as Abbas described.However, there are some architectural difficulties to stay mindful of. The AI agents are non-deterministic by nature and often depend on large language models (LLMs) like OpenAI's GPT, Anthropic's Claude, or Google's Gemini. They reason based on probabilities, and they improvise. They often make decisions that are hard to trace or manage.AI workflows as simple codeThis is where Temporal comes in. It becomes the executioner that keeps the system cohesive and in alignment. “What we are trying to solve with Temporal and durable execution more generally is that we tackle challenging distributed systems problems," said Abbas.Rather than developers stressing over queues, retries, or building their own reliability layers, Temporal allows them to write their AI workflows as simple code. Temporal takes care of everything else—reliable state management, retrying failed tasks, orchestrating asynchronous services, and ensuring uptime regardless of what fails below the surface.As agent-based architectures become more common, the demand for this kind of system-level orchestration will only increase.Listen to the full conversation on the Tech...

Organizations are increasingly exploring new technologies to improve their operations, but adoption comes with real challenges. In the latest episode of Don't Panic, It's Just Data, host Trisha Pillay speaks with Kevin Petrie, VP of Research at BARC, about the practical realities of integrating these emerging technologies into business operations, particularly when it comes to data stewardship, strategy, and operational oversight.Real-World Applications and GovernancePetrie begins by noting that these technologies are already delivering real value today, particularly in areas like software development and customer service. The key, he emphasizes, is starting with strong oversight. “Governance starts with data,” he says, pointing out that reliable, well-managed data is the foundation for successful adoption.Human oversight is equally important; automation alone cannot replace careful monitoring and decision-making. Effective governance also needs to extend beyond structured data to include unstructured information such as text, images, and other content types. As organizations adopt new models, they must be aware of the risks these systems introduce and put controls in place to mitigate them.BARC DATA festival onlineBuilding on these themes, BARC will host the DATA festival online on October 21, a virtual gathering designed to help leaders turn insight into action. The free event will bring together data professionals, decision-makers, and industry experts to share real-world use cases, operational frameworks, and lessons learned from practical implementations. The festival provides a clear roadmap for organizations seeking to make technology adoption both effective and sustainable.TakeawaysEmerging technologies are already delivering measurable value in business operations.Governance begins with reliable data and extends to all types of information, including unstructured formats.Human oversight is essential to maintain accountability and manage risks.Practical, real-world use cases illustrate successful adoption strategies.Cost efficiency, culture, and cross-functional programs are critical for sustainable implementation.Responsible, accountable practices are a must-have for long-term success.Chapters00:01: Introduction to AI Adoption01:00: Real-World AI Applications03:03: Balancing Automation and Human Oversight05:19: Governance and Data Strategy07:02: BARC DATA festival online preview09:54: Audience and Takeaways12:13: Rapid Fire Questions14:02: Closing ThoughtsAbout BARCBARC is a leading analyst firm for data and analytics and enterprise software, recognized for providing trusted, unbiased insights. Its expert analysts deliver in-depth research, advisory services, and industry events to help organisations make informed decisions about technology, data strategy, and analytics.With over 25 years of experience in data strategy, governance, architecture, and software selection, BARC empowers clients to become truly data-driven organizations. Its research highlights market trends, evaluates software and vendors rigorously, and provides actionable guidance to help enterprises innovate with data, analytics, and AI.

With an erratic and fast business environment, finance teams are facing high pressure to process reports. The main challenge lies in how mid-market firms achieve digital transformation, not by abandoning familiar tools but by making them more effective. In this episode of the Don't Panic It's Just Data podcast, host Kevin Petrie, Vice President, Research and Head of Data Management Practice, BARC, speaks with Maeghan Carriere, Divisional Vice President, Software Sales NA, insightsoftware; and Nate Cook, Director of Product Marketing, insightsoftware. They discuss the importance of automation, the role of CFOs as strategic leaders, and, most importantly, how mid-market companies can leverage tools like Spreadsheet Server for digital transformation. The conversation also spotlights the need for finance professionals to upskill in AI and data analysis to remain competitive.All speakers agree that the key is in equipping finance professionals with direct, real-time access to their data in the environment they know and trust – Excel.CFOs as Strategic LeadersCook believes that the role of the Chief Financial Officer (CFO) and their team has changed massively. "We really expect them to look back only as it helps them find a path forward.”This new role requires finance teams to become effective data teams but how can they do that? A recent Gartner study found that 75 per cent of CFOs "said they own or co-own enterprise data and analytics at their organisations." Unfortunately, these teams "sometimes struggle to have access to the data that they need." The time wasted on tedious data entry and report generation is time lost for important analysis and strategic thinking."The short answer is Spreadsheet Server," Cook told Petrie, explaining that better decisions rely on better data, and Spreadsheet Server provides the latter.Also Watch: Struggling with ERP Data? How to Get Real-Time Reporting in ExcelExcel with AI Upskills Finance TeamsAutomating reporting doesn't just save time; it allows finance professionals to develop skills in areas like Excel, data analysis, and AI. Carriere points out that AI is an important tool that every professional needs to learn to use effectively."Once you understand how data flows and connects in real time, you're better positioned to use AI tools because you can quickly tell if the results make sense," she explains.The key message for IT decision-makers is clear, as per insightsoftware's divisional VP, "Stop fighting Excel and make it more powerful instead." By embracing the tool that finance teams prefer, organisations can achieve quicker results, faster adoption, and ultimately free their financial experts from manual tasks. Cook also notes, Spreadsheet Server is "one way to help remove a lot of that toil and refocus the time that your folks are spending on the more strategic parts of analysis and decision-making that can help drive your organisation forward."TakeawaysFinance teams are facing scaling pressures and resource constraints.The need for speed in decision-making is critical for finance leaders.Automation can save finance teams significant time and

Send us a textGary brings you more bagpipes delights from the Bothy.PlaylistDan Houghton with Drink, Debauchery and 200 Euros, Teigis agus Dealg Innte, Willie Murray's Reel and The Fourth Floor from Bagpipe Personality Disorder. Greater Glasgow Glasgow Police Pipe Band with Lightly Swims the Swan, Miss Victoria Ross, Lady Margaret Stewart, Port a Beul, Lord McConnell of Lough Erne and Lochend from Ceolry. Mick O'Brien with Statia Donnelly's, If I Will I Can and Patsy Geary's from May Morning Due Gary West with Mrs Stewart of Grandtully, Glen Tilt Lodge, The Devil in the Kitchen, Captain Horne, Smith of Chilliechassie, Uist Reel and Smelling Fresh from the Raasay Restitution 2025, EYP Recording. MacKenzie Caledonian Pipe Band with Straloch Turkeys, Adrian's Obsession, The High Drive from A Big Step Forward Baghad de Vannes with Barc'hadur from Liviou  Donald MacPherson with The Lochaber Gathering, Arniston Castle and Mrs MacPherson of Inveran from Donald MacPherson Plays Ceol Beag.  Peatbog Faeries with The Naughty Step from Dust       Support the show

Itt most egy részletet hallhattok az Európa Leak 10.09-én megjelent adásából, amelyben Sevilla 4-1-es Barcelona elleni győzelmét elemezzük.A mikrofonokat és podcast keverőnket a Relacart és az AV365.hu biztosította. A teljes műsor témái:

LE PSG LAMINE LE BARÇA ! by Paris United

AI-Powered Canvases: The Future of Visual Collaboration and InnovationAs hybrid and remote work become the standard, organizations are rethinking how teams brainstorm, align, and innovate. Traditional whiteboards and digital tools often fall short in keeping pace with today's complex business challenges. This is where AI-powered canvases are transforming visual collaboration.In this episode of Tech Transformed, Kevin Petrie, VP of Research at BARC, joins Elaina O'Mahoney, Chief Product Officer at Mural, to explore how AI collaboration tools are reshaping teamwork in off-site locations. From customer journey mapping to process design, AI-powered canvases give teams the ability to visualize ideas, surface insights faster, and make better decisions—while keeping human creativity at the centre.AI-Powered Canvases, Visuals, and CollaborationA central theme in the conversation is the distinction between automation and augmentation. While AI can recommend activities, map processes, and identify participation patterns, decision-making remains a human responsibility.As O'Mahoney explains:“In the Mural canvas experience, we're looking to draw out the ability of a skilled facilitator and give it to participants without them having to learn that skill over the years.”This balance ensures that while AI-powered canvases streamline collaboration, teams still rely on human judgment, creativity, and contextual knowledge. One of the most powerful contributions is in AI-driven visuals, which can translate raw data or unstructured input into clear diagrams, journey maps, or process flows. These visuals not only accelerate understanding but also help teams spot gaps and opportunities more effectively.For example:In customer journey mapping, AI can quickly generate visual flows that highlight pain points and opportunities that would take much longer to uncover manually.In manufacturing, AI-powered canvases can create dynamic visuals of workflows, showing how new technologies might disrupt established processes.The Role of Visual Tools in Hybrid WorkIn blended work environments, teams often lack the in-person cues that guide effective collaboration. Visual canvases bring those cues into the digital workspace, showing where ideas are concentrated, highlighting gaps in participation, and enabling alignment across dispersed teams. By combining intuitive design with AI-driven support, platforms like Mural help organisations adapt to the demands of hybrid work while keeping human creativity at the centre.TakeawaysAI is reshaping visual collaboration in distributed teams.Visual elements enhance understanding and decision-making.AI can augment workflows but requires human oversight.There is no universal playbook for AI integration in businesses.Hybrid work necessitates effective digital collaboration tools.AI can help visualize complex customer experiences.Human intuition and creativity remain essential in AI applications.Training and guidance are crucial for effective AI use.Collaboration tools must adapt to diverse work environments.AI should be seen as a partner in the creative process.Chapters00:00 The Evolution of Visual Collaboration05:15 Augmenting vs Automating: The Role of AI10:36...

पीएम मोदी आज शाम देश को संबोधित करेंगे, कांग्रेस ने ट्रंप के बयानों पर तंज कसा, BARC ने नौसेना की न्यूक्लियर सबमरीन के लिए 200 MWe का नया रिएक्टर तैयार किया, नितिन गडकरी ने कहा कि E20 पेट्रोल से गाड़ियों की एवरेज पर असर नहीं होता, एकनाथ शिंदे का X अकाउंट हैक हुआ, पाकिस्तान के पीएम शहबाज़ शरीफ़ ने लंदन में कश्मीर मुद्दा उठाया, डोनाल्ड ट्रंप ने फिर कहा कि उन्होंने भारत-पाकिस्तान समेत सात युद्ध रोके, यूक्रेन के राष्ट्रपति ज़ेलेंस्की ने रूस पर 1500 ड्रोन हमले का आरोप लगाया और एशिया कप सुपर-4 में भारत-पाकिस्तान की भिड़ंत, सिर्फ़ 5 मिनट में सुनिए शाम 4 बजे तक की बड़ी ख़बरें

Welcome back to Meeting of the Minds, a special podcast episode series by EM360Tech, where we talk about the future of tech.In this Big Data special episode of the Meeting of the Minds, our expert panel – Ravit Jain, Podcast host, Christina Stathopoulos of Dare to Data and a data and AI evangelist, Wayne Eckerson, data strategy consultant and president of the Eckerson Group and Kevin Petrie VP of Research at BARC, come together again to discuss the key data and AI trends, particularly focusing on data ethics. They discuss ethical issues related to using AI, the need for data governance and guidelines, and the essential role of data quality in AI success. The speakers also look at how organisations can measure the value of AI through different KPIs, stressing the need for a balance between technical achievements and business results. Our data experts examine the changing role of AI across various sectors, with a focus on success metrics, the effects on productivity and employee stress, changes in education, and the possible positive and negative impacts of AI in everyday life. They highlight the need to balance productivity with quality and consider the ethics of autonomous AI systems.In the previous episode, new challenges and opportunities in data governance, regulatory frameworks, and the AI workforce were discussed. They looked at the important balance between innovation and ethical responsibility, looking at how companies are handling these issues.Tune in to get new understandings about the future of data and AI and how your enterprise can adapt to the upcoming changes and challenges. Hear how leaders in the field are preparing for a future that is already here.Also watch: Meeting of the Minds: State Of Cybersecurity in 2025TakeawaysGenerative AI is creating a supply shock in cognitive power.Companies are eager for data literacy and AI training.Data quality remains a critical issue for AI success.Regulatory frameworks like GDPR are shaping AI governance.The US prioritises innovation, sometimes at the expense of regulation.Generative AI introduces new risks that need to be managed.Data quality issues are often the root of implementation failures.AI's impact on jobs is leading to concerns about workforce automation.Organisations must adapt to the probabilistic nature of generative AI.The conversation around data quality is ongoing and evolving. AI literacy and data literacy are crucial for workforce success.Executives are more concerned about retraining than layoffs.Younger workers may struggle to evaluate AI-generated answers.Incremental changes in productivity are expected with AI.Job displacement may not be immediate, but could create future gaps.Human empathy and communication skills remain essential in many professions.AI will augment, not replace, skilled software developers.Global cooperation is needed to navigate...

Kassavaitinė 15min tinklalaidė apie futbolą „Skrieja kamuolys“ tradiciškai apžvelgia svarbiausius savaitės futbolo įvykius. Daugiausiai dėmesio vėl skyrėme Vilniaus „Žalgirio“ pokyčių užkulisiams, Marius Bagdonas atskleidė, kiek sostinėje uždirbo Vladimiras Čeburinas ir kiek Vilma Venslovaitienė. Pakalbėjome ir apie taškus ant „i“ Lietuvos čempionate sudėjusį Kauno derbį, A lygos žaidėjų pardavimus į Norvegijos ir Portugalijos lygas, Konferencijų lygoje žaisiančius lietuvius, rinktinės bėdas prieš akistatas su Malta ir Nyderlandais, skelbėme vieno bilietų konkurso nugalėtoją ir sugalvojome naują užduotį, o kitoje laidos dalyje apžvelgėme svarbiausius didžiųjų Europos lygų mačus. Studijoje – 15min sporto žurnalistai Aurimas Tamulionis ir Marius Bagdonas bei „Go3“ komentatorius Karolis Dudėnas. 00:00 Įžanga, naujas rėmėjas ir truputį krepšinio 01:56 VŽ pokyčių užkulisiai ir kiek uždirbo Čeburinas? 40:23 Mariaus pastabos savivaldybei ir naujas rėmėjas 44:33 Taškus ant „i“ sudėjęs Kauno derbis 48:05 A lygos žaidėjų pardavimai, „Sūduva“ 59:52 „Riterių“ ir „Dainavos“ šešiataškis 1:02:11 „Šiaulių“ fiesta Telšiuose 1:04:38 Šeši lietuviai Konferencijų lygoje 1:10:32 Šiaulių „Gintra“ lieka Europoje 1:11:19 Rinktinės bėdos prieš Maltą ir Nyderlandus 1:15:14 Konkursas bilietams laimėti 1:17:29 Lietuviai užsienyje ir netgi Maldyvuose 1:20:48 Anglų „Premier“ lyga: lyderių mūšis, VAR ir MU 1:47:23 Ispanų „La Liga“: „Rayo Vallecano“ šou prieš „Barcą“ 2:00:44 Vokiečių „Bundesliga“: jau nukirsta ETH galva 2:08:27 Italų „Serie A“: pamėgtas 1:0

In this episode of the Don't Panic, It's Just Data podcast, Kevin Petrie, VP of Research at BARC and the podcast host, is joined by Dainius Jocas, Search Engineer at Vinted, and Radu Gheorghe, Software Engineer at Vespa.ai. They discuss how Vinted, an online marketplace for secondhand products, modernised its data architecture to address new AI search use cases and the challenges faced with Elasticsearch. From the switch to Vespa and the advantages of supporting multiple languages and complex queries, the podcast offers insights on the trade-offs organisations must think about when updating their search systems, especially regarding AI and machine learning applications.Vinted Elasticsearch ChallengesVinted's search architecture was built on Elasticsearch before they switched to Vespa. Elasticsearch is a functional system that presents a few major challenges. With over 20 supported languages, the company's "index per language" approach created significant sharding problems, leading to infrastructure imbalances and constant adjustments."The index for the French language, the biggest language that we support, was more than three times bigger than the second biggest language, which created imbalances in the Elasticsearch data nodes' load," Jocas explained.In addition to these technical obstacles, organisational issues arose as teams responsible for different parts of the search process found themselves "pointing fingers at each other at an increasing rate." The need for a more integrated, effective solution became clear.The Solution: A New Platform for a New EraThe search for a better solution led Vinted to Vespa. The initial adoption was a "one success story" when a machine learning engineer, working on recommendations, discovered that Vespa was ten times faster than Elasticsearch for their use case. This initial benchmark, run on a single decommissioned server, was a "true testament to how efficient Vespa is when it comes to serving requests,” Jocas told Petrie.Vespa helped Vinted solve their language problem by allowing it to set a language per document. Thus, it eliminates the need for separate indexes and the associated sharding headaches. As Jocas put it, "We got out of the sharding problem once and for all."TakeawaysVinted faced challenges with its initial Elasticsearch architecture.The need for better integration between matching and ranking was identified.Vespa outperformed Elasticsearch in handling image search and recommendations.Transitioning to Vespa involved significant learning and support from developers.Vespa allows for language-specific document handling, simplifying architecture.Organisations must evaluate the complexity and volume of their data before transitioning.Vespa is optimised for query performance, while Elasticsearch excels in data writing.The learning curve for Vespa can be steep, but support is available.It's important to focus on optimising new systems rather than emulating old ones.Partial updates in Vespa are more efficient than in Elasticsearch.Chapters00:00 Introduction to Vinted and...

In This Episode What if the secret to faster product launches and fewer team conflicts is better alignment between R&D and strategy? In this episode of Systems Simplified, host Adi Klevit interviews Laura Moran, an innovation leader turned consultant, about how she helps pet food companies streamline innovation. Laura shares how her early experiences at Mars and Barc shaped her ability to turn big-business frameworks into agile, usable systems for smaller brands. Laura describes the core components of her Pet Spark Navigator system—a structured process that aligns teams early, defines product expectations clearly, and embeds innovation capabilities within organizations. She explains how the system reduces rework, builds team confidence, and prevents costly mistakes down the line. Throughout the conversation, Adi and Laura dive into how smaller companies often resist systems out of fear they'll stifle creativity—when in reality, the right systems free teams to move faster with less risk. It's a practical, actionable look at transforming chaos into repeatable growth.  

A ratings monopoly in India has led to lack of technological variation, resulting in sluggish systems detached from market dynamics.  

A humorous account of three friends (and a dog) navigating the absurdities of a river trip gone hilariously wrong.

Blending financial planning directly into existing business intelligence (BI) platforms like Microsoft Power BI and Qlik are on the rise. But have you questioned why, and what drives this shift? In this episode of the Don't Panic It's Just Data podcast, Kevin Petrie, BARC analyst, sits down to chat with Thomas Gorr, Director of Product Management for xP&A and BI at insightsoftware, and Henri Rufin, Head of Responsible Data & Analytics at Radiall.The speakers stress how the integration of financial planning in BI platforms is driven by the need to move beyond traditional Excel-based planning. This is because it often leads to data silos and errors. Their conversation spotlights how integrated BI and planning solutions improve collaboration across departments. Going deeper, BI can also provide a unified view of data and help organizations be more agile and proactive in volatile markets.Gorr and Rufin explain how separating your financial data makes less sense, and why traditional tools like Excel are no longer significant.Watch this podcast to discover how embedding planning capabilities within BI platforms, such as Qlik and Power BI, offer a seamless experience, greater flexibility, and real-time collaboration.Learn how organisations are adapting to volatile market conditions through agile planning, and gain a glimpse into the evolving landscape of financial planning. Tune in to gain expert insights and practical strategies for a more collaborative and data-driven future!TakeawaysThe integration of planning into BI platforms is essential for collaboration.Excel is prone to errors and creates silos in data management.Organizations need to adapt to dynamic market conditions for effective planning.Stakeholder engagement is crucial for successful financial planning.Advanced planning solutions offer flexibility and real-time collaboration.Data governance is necessary to support planning processes.The total cost of ownership is lower with integrated planning solutions.BI platforms provide a unified experience for users.Future planning will focus on platform integration and advanced analytics.Companies must evolve their planning capabilities to remain competitive.Chapters00:00 Introduction to Financial Data Management03:04 The Shift Towards Integrated Planning08:30 Collaboration in Dynamic Markets11:05 The Role of Stakeholders in Planning15:10 Moving Beyond Excel19:19 Total Cost of Ownership in Planning Solutions25:06 Future of Integrated Financial PlanningAbout insightsoftwareinsightsoftware is a global provider of comprehensive solutions for the Office of the CFO. They believe actionable business strategies begin and end with financial data that's accessible and easy to understand. They offer solutions across financial planning and analysis (FP&A), accounting, and operations. This transforms how teams operate, empowering leaders to make timely and informed decisions.

Dr. Teri Rouse holds a doctorate in behaviour management. She is an international speaker, autism reading specialist and creator of the REAL Peaceful Parenting program. She is on a mission to help families and ed build stronger relationships through empathy, empowerment and emotional intelligence. Whether you are navigating tantrums or teacher burnout, Dr. Teri offers real world strategies that are practical, heartfelt and deeply human. What happens when a seasoned educator and special education expert faces behaviours in her own home that she can't explain, let alone manage? Join Dr. Teri Rouse, Women Changing the World Award winner, as she shares her journey with her step-son and how it led her to a doctorate in behaviour management.Dr. Rouse unpacks the emotional and professional challenges educators and parents face today.

The Moneywise Radio Show and Podcast Thursday, March 13th BE MONEYWISE. Moneywise Wealth Management I "The Moneywise Guys" podcast call: 661-847-1000 text in anytime: 661-396-1000 website: www.MoneywiseGuys.com facebook: Moneywise_Wealth_Management instagram: MoneywiseWealthManagement Guests: Shawn Kennemer, President of Bakersfield Arc & Erika Dixon, Sr. Vice President of Development website: www.bakersfieldarc.org/ phone: (661) 834-2272 facebook YouTube instagram    

Carsten Bange (Founder & CEO of BARC) joins me to chat about trends in data, analytics, and AI. BARC website: www.barc.com Carsten on LinkedIn: https://www.linkedin.com/in/carsten-bange/ BARC Data Culture Summit: https://barc.com/events/data-culture-summit%20/ BARC DATAfestival: https://barc.com/events/data-festival/