How AI can uncover hidden value in your data

At Data Center Frontier Trends Summit, our CEO Chris Downie shared a bold new playbook for data center operators.    With AI reshaping industries and driving unprecedented demand, Chris highlighted the pressing challenges ahead — from soaring power needs and land scarcity to supply chain bottlenecks and capital allocation.    “The machines are rewriting reality in real time,” he told the audience. “This new cycle invalidates past experience — business as usual won’t apply.”    Read more via RCR Wireless News: https://lnkd.in/dT3hD8sP #DCFtrends #AI #DataCenters

Big Data is no longer just about scale — it’s about speed, context, and meaningful outcomes. I recently read this insightful article on The Future of Big Data: Trends Shaping Tomorrow’s Digital Landscape: https://lnkd.in/dwWQw4ry What struck me most is how GenAI and ML will reshape real-time decisioning. Imagine turning massive data streams into personalized, contextual experiences — powering smarter recommendations, seamless journeys, and adaptive systems. #BigData #GenAI #DataEngineering #DigitalTransformation

Why does data feel so siloed? In most organizations, data starts in isolated pockets. A department collects information to improve its products or operations, but this data stays locked away. While valuable, this siloed approach misses the exponentially greater value that comes from aggregating large data sets across the entire enterprise. This is the power of Big Data. Every enterprise uses data, but the most successful ones leverage Big Data for a competitive advantage: * Media and Consumer Products use it to build predictive models for new products and services. * Manufacturing deploys it for predictive maintenance, anticipating failures before they happen. * Retail businesses use it to improve customer experiences and manage complex supply chains. * Financial organizations analyze it to find patterns that indicate potential fraud. But Big Data's most critical role today is supporting AI initiatives. Artificial intelligence and machine learning require vast sets of rich data to train and validate models. A lack of sufficient data is one of the most common reasons AI initiatives fail. To truly achieve AI goals, organizations must develop an enterprise-wide approach to collecting, managing, and delivering integrated data. Of course, collecting all this data isn't easy. Big Data is often characterized by the 3 Vs: * Volume: The sheer scale of data is massive. Big Data solutions must collect, aggregate, and deliver massive volumes of data that can reach hundreds of petabytes. * Velocity: Data-driven decisions require the latest information. Velocity is the speed at which data is received and refreshed, demanding solutions that can handle continuous streams of new data. * Variety: Data comes in many forms—from structured databases to unstructured sources like video, images, and sensor data—each presenting its own unique challenges. More recently, the data community has added more "Vs" like Veracity, Value, and Visibility to highlight the ongoing challenges of storing, managing, and serving this crucial resource. How are you moving beyond data silos to unlock the full potential of Big Data and AI? #SAFe6 #Bigdata #AI4All learn more here: https://lnkd.in/eJHawGSg

Beyond the Hype: Taming Big Data Chaos with Web Intelligence Is your organization drowning in data but starving for insights? You're not alone. The promise of Big Data is immense, but so are its challenges. We collect terabytes of information, yet turning it into a competitive advantage remains elusive. Here’s why: ⬇️ The Big Data Hurdles: ➡️ Volume & Velocity: The web generates data at an unprecedented scale and speed. Traditional tools can't keep up. ➡️ Variety & Veracity: Data comes in endless formats (structured, unstructured, text, video) from countless sources. How do you ensure its quality and trustworthiness? ➡️ The Insight Gap: Having data is one thing; understanding the why behind the trends is another. Raw data lacks context. This is where Web Intelligence becomes non-negotiable. Web Intelligence isn't just about collecting more data; it's about collecting the right data and meaning. It's the advanced discipline of using AI, machine learning, and sophisticated scraping to: ✅ Filter the signal from the noise. ✅ Transform unstructured web data into structured, actionable intelligence. ✅ Uncover hidden patterns, market shifts, and consumer sentiment in real-time. It’s the difference between knowing how many people mentioned your brand and understanding why they feel that way. It’s moving from reactive reporting to proactive strategy. The bottom line: Big Data gives you the "what." Web Intelligence gives you the "so what?" and the "now what." Are you focusing on both? What's the biggest data challenge your team is facing right now? Volume, variety, or deriving insight? Share in the comments! 👇 #WebIntelligence #BigData #DataScience #ArtificialIntelligence #MachineLearning #DataAnalytics #BusinessIntelligence #Tech #Innovation #DataDriven #DigitalTransformation #AI #DataEngineering #IoT #LinkedInTech

I just came across a fascinating piece on TE Connectivity's data strategy, and it got my inner geek buzzing. 🤓 For years, we've treated our corporate data like a dragon's hoard: jealously guarded in separate caves (silos), polished to perfection (governed), but rarely ever spent. We were obsessed with the 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 that held the treasure, not with the epic quests it could fund. TE Connectivity's CDAIO, Elena Alikhachkina, talks about flipping this script with their "One Data" initiative. They're moving from a system-centric to a 𝐜𝐨𝐧𝐬𝐮𝐦𝐩𝐭𝐢𝐨𝐧-𝐜𝐞𝐧𝐭𝐫𝐢𝐜 model. It sounds simple, but it's a seismic shift in mindset. It’s like finally deciding to melt down the gold coins to forge Excalibur. ⚔️ The goal isn't just to 𝐡𝐚𝐯𝐞 data; it's to make it ridiculously easy for people to 𝐮𝐬𝐞 it for BI, data science, and yes, the big AI magic show. This resonates so much with what we see every day in the Office of the CFO. Finance teams are often the keepers of the most critical data "keys," but they're stuck trying to unlock value with a dozen different, ill-fitting keys from various systems. The real win isn't just a single source of truth, but a single, trusted 𝐯𝐞𝐧𝐝𝐢𝐧𝐠 𝐦𝐚𝐜𝐡𝐢𝐧𝐞 of truth, where anyone with the right permissions can get the insights they need to make smarter decisions, faster. The article highlights how the Chief Data Officer role is evolving from a data police officer to a value-creation champion. [1] This is spot on. The best data leaders aren't just building fortresses; they're building superhighways and equipping everyone with a self-driving car. It’s a powerful reminder that a successful AI strategy isn't born from algorithms; it's born from a data culture that prioritizes access and usability over arcane control. You can't build the future if your most valuable resource is locked in a digital dungeon. Time to set the data free! #DataStrategy #AI #DigitalTransformation #OneData #DataCulture #Leadership #Innovation #CCHTagetik #FutureOfFinance #CDO #Analytics #BusinessIntelligence #DataGovernance #AIinBusiness #ConsumptionCentric References: [1] How to Succeed at AI Strategy and Implementation: The 5 Questions Every Company Needs Answered: https://lnkd.in/deCb6_2d

Did you know? Nearly 40% of a data professional’s time is lost to fixing and cleaning data. (That’s almost half the workweek, according to the Anaconda report.) At Zyrix, we asked: What if AI could change this equation? With Agentic AI, organizations are already cutting this effort by up to 80% through autonomous detection, diagnosis, and resolution. Zyrix Data Copilot goes beyond rule-based cleanup, delivering real-time intelligence, adaptability, and trust in every dataset. We’re not just improving processes; we’re redefining the role of data teams as true drivers of enterprise value. Ready to experience the Agentic AI advantage? Read the blog to know more! https://lnkd.in/gJqfSMHi #AgenticAI #DataQuality #Analytics #Automation #Zyrix #FutureOfWork

Enterprises are betting big on AI and agents. But here’s the reality: without trusted, unified, real-time data, those projects don’t scale. The recent sponsored Harvard Business Review that I co-authored with Venkat Venkatraman lays out the challenge clearly: 👉 Advantage comes from data in motion, not data at rest. 👉 Competing requires ecosystem-wide orchestration, not just data locked inside one enterprise. 👉 Trust isn’t optional — it’s what allows autonomous systems to make safe, precise decisions. The future is human + AI working together, not AI alone. At Reltio, we’ve been working on these problems for more than a decade. And what we’ve learned is simple: more data is not the answer. Trusted, real-time, 360 data is. The Age of Intelligence demands new thinking. The rules for enterprise data are being rewritten today. The question is: are you ready? Check out the article here: https://lnkd.in/gQwRkz79  #DataStrategy #AgenticAI #EnterpriseAI #Reltio #HBR #AITransformation

AI is only just beginning to transform the enterprise — and most organizations' data infrastructure can't handle what's ahead, said SingleStore's CEO Raj Verma in this episode of Makers. By Heather Joslyn

AI is only just beginning to transform the enterprise — and most organizations' data infrastructure can't handle what's ahead, said SingleStore's CEO Raj Verma in this episode of Makers. By Heather Joslyn

This isn’t hype, it's reality. AI agents thrive on speed and autonomy, but without an AI-ready foundation, you're handing them a blueprint to magnify every data flaw. Key points that hit home: -       Data silos aren't just inconvenient; they’re a critical risk. Incomplete, decentralized data forces AI agents to act on partial truths, eroding trust at scale. -       By 2026, Gartner warns that 60% of AI projects will be abandoned if they lack that data foundation, yet more than 60% of data leaders admit their practices aren’t there yet. -       Achieving a truly agentic future isn’t about the agent, it’s about building the network around the data: governance, real-time context, semantic consistency. Tools only accelerate what already exists. Before you chase the next AI agent pilot, fix your data infrastructure first. Invest in clarity, quality, interoperability, and ownership. Only then will agents perform and you’ll preserve trust instead of eroding it. https://lnkd.in/dsDsjM-n