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Tech Talks Daily

Neil C. Hughes
Tech Talks Daily
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  • Marketing Intelligence After Cookies: How Funnel Turns Data Into Decisions
    Marketing teams used to have a simple enough job: follow the click, count the conversions, and shift the budget accordingly. But that world is gone. GDPR, iOS restrictions, and browser-level changes have left most attribution models broken or unreliable. So what now? In this episode, I sat down with Fredrik Skansen, CEO of Funnel, to unpack how marketing intelligence actually works in a world where data is partial, journeys are fragmented, and the old models don’t hold. Since founding Funnel in 2014, Fredrik has grown the company into a platform that supports over 2,600 brands and handles reporting on more than 80 billion dollars in annual digital spend. That scale gives him a front-row seat to the questions every CMO and CFO are asking right now. Fredrik explains why last-click attribution didn’t just become inaccurate. It became misleading. With tracking capabilities stripped down and user signals disappearing, the industry has had to move toward modeled attribution and real-time optimisation. That only works if your data is clean, aligned, and ready for analysis. Funnel’s platform helps structure campaigns upfront, pull data into a unified model, apply intelligence, push learnings back into the platforms, and produce reporting that makes sense to the wider business. This isn’t about dashboards. It’s about decisions. We also talk about budget mix. Performance channels may feel safe, but Fredrik points out they are also getting more expensive. When teams bring brand and mid-funnel activity back into the measurement framework, the picture often changes. He shares how Swedish retailer Gina Tricot grew from 100 million to 300 million dollars in three years, in part by shifting spend to brand and driving demand earlier in the customer journey. That move only felt safe because the data supported it. AI adds another layer. With tools like Perplexity reshaping search behavior and the web shifting from links to answers, click-throughs are drying up. But it’s not the end of visibility. Content still matters. So does structure. The difference is that now your reader might be an AI model, not a human. That requires a rethink in how brands approach discoverability, authority, and engagement. What makes Funnel interesting is that it doesn’t stop at analytics. The platform feeds insight back into action, reducing waste and creating tighter loops between teams. It also works for agencies, which is why groups like Havas use it across 40 offices through a global agreement. If you're tired of attribution theatre and want to understand what marketing measurement looks like when it’s built for reality, this episode gives you a clear, usable view. Listen in, then tell me which decision you're still guessing on. Because marketing can be measured. Just not the way it used to be. ********* Visit the Sponsor of Tech Talks Network: Land your first job  in tech in 6 months as a Software QA Engineering Bootcamp with Careerist https://crst.co/OGCLA    
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  • Why FeatureOps Might Be the Future of Software Delivery
    I invited Egil Østhus to unpack a simple idea that tends to get lost in release day pressure. DevOps gets code to production quickly, but users experience features, not pipelines. Egil is the founder of Unleash, an open source feature management platform with close to 30 million downloads, and he argues that the next step is FeatureOps. It is a mindset and a set of practices that separate deployment from release, so teams can place code in production, light it up for a small cohort, learn, and only then scale out with confidence. Here is the thing. Controlled rollouts, clear telemetry, and fast rollback reduce risk without slowing teams down. Egil explains how FeatureOps connects engineering effort to business outcomes through gradual exposure, full stack experimentation, and what he calls surgical rollback. Instead of ripping out an entire release when one part misbehaves, teams can disable the offending capability and keep the rest of the value in place. It sounds straightforward because it is, and that is the point. Less drama, more learning, better results. We also talk about culture. When releases repeatedly disappoint, trust between product and engineering frays. Egil shares examples where Unleash helped a hardware and software company move from blame to shared ownership by making rollout plans visible and collaborative. Another client, an ERP vendor, discovered that early feedback from a small group of users allowed them to ship a leaner version that met the need without months of extra scope. That is how FeatureOps saves money and tempers expectations while still delighting customers. AI enters the story too. Code is shipping faster, but reliability can wobble when autogenerated changes move through pipelines. Egil sees feature management as a practical control plane for this new reality. Feature flags provide a real time safety net and, if needed, a kill switch for AI powered functionality. Teams can keep experimenting while protecting users and brand equity. If you want to move beyond release day roulette, this episode offers a practical playbook. We cover privacy first design, open source flexibility, and why metadata from FeatureOps will help leaders study how their organizations truly build. To learn more, visit getunleash.io or search for Unleash in your favorite tool, then tell me how you plan to measure your next rollout’s impact. ********* Visit the Sponsor of Tech Talks Network: Land your first job  in tech in 6 months as a Software QA Engineering Bootcamp with Careerist https://crst.co/OGCLA      
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  • From Bots To Agents: Building Trustworthy Autonomy With Hakkōda, an IBM Company
    I invited Atalia Horenshtien to unpack a topic many leaders are wrestling with right now. Everyone is talking about AI agents, yet most teams are still living with rule based bots, brittle scripts, and a fair bit of anxiety about handing decisions to software. Atalia has lived through the full arc, from early machine learning and automated pipelines to today’s agent frameworks inside large enterprises. She is an AI and data strategist, a former data scientist and software engineer, and has just joined Hakoda, an IBM company, to help global brands move from experiments to outcomes. The timing matters. She starts on the 18th, and this conversation captures how she thinks about responsible progress at exactly the moment she steps into that new role. Here’s the thing. Words like autonomy sound glamorous until an agent faces a messy real world task. Atalia draws a clear line between scripted bots and agents with goals, memory, and the ability to learn from feedback. Her advice is refreshingly grounded. Start internal where you can observe behavior. Put human in the loop review where it counts. Use role based access rather than feeding an LLM everything you own. Build an observability layer so you can see what the model did, why it did it, and what it cost. We also get into measurements that matter. Time saved, cycle time reduction, adoption, before and after comparisons, and a sober look at LLM costs against any reduction in FTE hours. She shares how custom cost tracking for agents prevents surprises, and why version one should ship even if it is imperfect. Culture shows up as a recurring theme. Leaders need to talk openly about reskilling, coach managers through change, and invite teams to be co creators. Her story about Hakoda’s internal AI Lab is a good example. What began as an engineer’s idea for ETL schema matching grew into agent powered tools that won a CIO 100 award and now help deliver faster, better outcomes for clients. There are lighter moments too. Atalia explains how she taught an ex NFL player the basics of time series forecasting using football tactics. Then she takes us behind the scenes with McLaren Racing, where data and strategy collide on the F1 circuit, and admits she has become a committed fan because of that work. If you want a practical playbook for moving from shiny demos to dependable agents, this episode will help you think clearly about scope, safeguards, and speed. Connect with Atalia on LinkedIn, explore Hakoda’s work at hakoda.io, and then tell me how you plan to measure your first agent’s value. ********* Visit the Sponsor of Tech Talks Network: Land your first job  in tech in 6 months as a Software QA Engineering Bootcamp with Careerist https://crst.co/OGCLA  
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  • 3418: Scaling IoT Security with Real Time Visibility at Wireless Logic
    Here’s the thing. Connecting thousands of devices is the easy part. Keeping them resilient and secure as you grow is where the real work lives. In this episode, I sit down with Iain Davidson, Senior Product Manager at Wireless Logic, to unpack what happens when connectivity, security, and operations meet in the real world. Wireless Logic connects a new IoT device every 18 seconds, with more than 18 million active subscriptions across 165 countries and partnerships with over 750 mobile networks. That reach brings hard lessons about where projects stall, where breaches begin, and how to build systems that can take a hit without taking your business offline. Iain lays out a simple idea that more teams need to hear. Resilience and security have to scale at the same pace as your device rollouts. He explains why fallback connectivity, private networking, and an IoT-optimised mobile core such as Conexa set the ground rules, but the real differentiator is visibility. If you cannot see what your fleet is doing in near real time, you are guessing. We talk through Wireless Logic’s agentless anomaly and threat detection that runs in the mobile core, creating behavioural baselines and flagging malware events, backdoors, and suspicious traffic before small issues become outages. It is an early warning layer for fleets that often live beyond the traditional IT perimeter. We also get honest about risk. Iain shares why one in three breaches now involve an IoT device and why detection can still take months. Ransomware demands grab headlines, but the quiet damage shows up in recovery costs, truck rolls, and trust lost with customers. Then there is compliance. With new rules tightening in Europe and beyond, scaling without protection does not only invite attackers. It can keep you out of the market. Iain’s message is clear. Bake security in from day one through defend, detect, react practices, supply chain checks, secure boot and firmware integrity, OTA updates, and the discipline to rehearse incident playbooks so people know what to do when alarms sound. What if you already shipped devices without all of that in place? We cover that too. From migrating SIMs into secure private networks to quarantining suspect endpoints and turning on core-level detection without adding agents, there are practical ways to raise your posture without ripping and replacing hardware. Automation helps, especially at global scale, but people still make the judgment calls. Train your teams, run simulations, and give both humans and digital systems clear rules for when to block, when to escalate, and when to restore from backup. I left this conversation with a simple takeaway. Growth is only real if it is durable. If you are rolling out EV chargers, medical devices, cameras, industrial sensors, or anything that talks to the network, this episode gives you a working playbook for scaling with confidence. Connect with Iain on LinkedIn, explore the IoT security resources at WirelessLogic.com, or reach the team at [email protected]. ********* Visit the Sponsor of Tech Talks Network: Land your first job  in tech in 6 months as a Software QA Engineering Bootcamp with Careerist https://crst.co/OGCLA
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  • Can You Trust Your AI if You Can’t Trust Your Data? Reltio Weighs In
    We talk a lot about AI as if it can fix broken systems. But what happens when the underlying data is too messy, too slow, or too disconnected to support anything useful? That’s the problem Manish Sood, founder and CTO of Reltio, has spent the last decade working to solve. Reltio is not your average data company. It sits behind some of the world’s most recognisable brands, helping names like L’Oréal, Pfizer, HP, and CarMax modernise how they manage and activate data across the business. What they all share is a recognition that outdated systems and disconnected records don’t just slow down insights. They actively block innovation. Manish breaks this down with uncommon clarity. He calls it “data debt”—the invisible burden of stale, incomplete, or fragmented information that quietly kills speed and adds risk. It’s not just a technical problem. It’s a leadership challenge, especially as businesses adopt generative AI tools that rely on clean, contextual data to function reliably. We explore how real-time intelligence is changing the way companies operate across customer experience, fraud detection, and supply chain resilience. Manish shares examples from enterprise clients who have moved from legacy systems to unified platforms, and how that shift enabled smarter decision-making at scale. From personalised retail offers to proactive healthcare outreach, the stories point to one common truth: if the data isn’t trusted, the AI cannot be either. There’s also a new role emerging inside many companies—the data steward as AI enabler. These are the people ensuring that data isn’t just stored, but shaped. Human-guided, explainable, traceable. That clarity is key to responsible AI, especially in sectors where compliance and reputation are tightly linked. Manish also explains how Reltio’s platform helps businesses protect against AI vulnerabilities by enabling resilient data pipelines, consistent governance, and real-time monitoring. In a world where data is created and used simultaneously, batch syncing is not enough. Real-time pipelines give companies the confidence to experiment with AI without falling into chaos. If your business is chasing innovation without cleaning up its data layer first, this conversation is a wake-up call. Manish shows that the future of AI isn’t about who builds the best model. It’s about who feeds it the best foundation. ********* Visit the Sponsor of Tech Talks Network: Land your first job  in tech in 6 months as a Software QA Engineering Bootcamp with Careerist https://crst.co/OGCLA    
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About Tech Talks Daily

If every company is now a tech company and digital transformation is a journey rather than a destination, how do you keep up with the relentless pace of technological change? Every day, Tech Talks Daily brings you insights from the brightest minds in tech, business, and innovation, breaking down complex ideas into clear, actionable takeaways. Hosted by Neil C. Hughes, Tech Talks Daily explores how emerging technologies such as AI, cybersecurity, cloud computing, fintech, quantum computing, Web3, and more are shaping industries and solving real-world challenges in modern businesses. Through candid conversations with industry leaders, CEOs, Fortune 500 executives, startup founders, and even the occasional celebrity, Tech Talks Daily uncovers the trends driving digital transformation and the strategies behind successful tech adoption. But this isn't just about buzzwords. We go beyond the hype to demystify the biggest tech trends and determine their real-world impact. From cybersecurity and blockchain to AI sovereignty, robotics, and post-quantum cryptography, we explore the measurable difference these innovations can make. Whether improving security, enhancing customer experiences, or driving business growth, we also investigate the ROI of cutting-edge tech projects, asking the tough questions about what works, what doesn't, and how businesses can maximize their investments. Whether you're a business leader, IT professional, or simply curious about technology's role in our lives, you'll find engaging discussions that challenge perspectives, share diverse viewpoints, and spark new ideas. New episodes are released daily, 365 days a year, breaking down complex ideas into clear, actionable takeaways around technology and the future of business.
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