Cisco Data Fabric: Transforming Machine Data into AI-Ready Intelligence

As organizations continue to scale their AI initiatives, infrastructure demands are evolving faster than ever. Traditional systems can’t always keep up with the performance, agility, and resilience required to fully unlock AI’s potential. This recent article from CIO Online explores how the edge is being redefined to set new standards for AI infrastructure—an area where Jeskell helps clients prepare today for tomorrow’s demands. 📖 Read the full article here: https://lnkd.in/ezP7ExeA

Many organizations are investing in AIOps, but network limitations are holding them back. Learn how modernizing your infrastructure with acceleration and intelligent data filtering can prevent slowdowns, reduce costs, and increase the value of AI: https://rvbd.ly/45D9ZDG

🚨 Most enterprises are drowning in machine data but starving for AI insights. I just saw Cisco drop something that could change everything at Splunk .conf 2025. Their new Data Fabric isn't just another platform—it's solving the biggest AI adoption problem most companies face: Turning messy machine data into AI-ready intelligence. Here's why this matters: ✅ 80% of enterprise data sits in silos, unusable for AI ✅ Companies spend millions on AI tools but can't feed them quality data ✅ Machine data from sensors, apps, and factories holds massive untapped value Cisco's solution tackles this head-on: 🔹 Time Series Foundation Model for instant AI readiness 🔹 Cross-domain real-time search at extreme scale 🔹 Intelligent edge data management that reduces costs 🔹 AI Canvas for building custom models without complexity The game-changer? It transforms raw sensor readings, factory metrics, and app data into actionable insights that power agentic workflows. This isn't just about better data management. It's about making enterprise AI adoption faster, more secure, and dramatically more accessible. The companies that figure out how to turn their machine data into AI gold will dominate their industries. How is your organization currently handling machine data for AI initiatives?

AI innovation today is defined not just by the models we train but by where those models live and run! Placing inference at the edge brings computation closer to data, enabling faster insights, better privacy, and more efficient use of resources. This marks the foundation of the next era of AI. Making this shift requires a structured approach. The journey begins with 4 steps to prioritize proximity at the edge! 🔸 Audit existing infrastructure to pinpoint performance gaps. 🔸 Map out edge requirements based on application and industry. 🔸 Build a dynamic multicloud, multi-provider strategy. 🔸 Connect through neutral, interconnected partner ecosystems for flexibility. Still, the real challenge lies in managing data itself. Data placement decisions shape cost structures and compliance outcomes. Moving vast datasets to centralized locations increases expenses and delays, while regulations in over 140 countries dictate where sensitive information can, and cannot, be stored. So how to navigate it? By embracing proximity-first infrastructure, businesses align performance with compliance and cost-efficiency! 💡 #Advantech #WeEnable #AI #infrastructure #standards

The U.S. government’s AI action plan highlights AI as a national priority, emphasizing that data infrastructure is as critical as algorithms. But this priority is not unique, We live in a complex, competitive world where other countries also seek Sovereign AI - infrastructure built and operated within jurisdictional boundaries, leveraging pre-trained LLM. Scale, sustainability, data governance and security are essential outcomes. Hitachi Vantara’s Hitachi iQ addresses the complexity, security, and risks of building robust data pipelines for AI, ensuring business success across data preparation, model training, and inference. The VSP One Platform helps governments unify data across the traditional data center, public and priviledged clouds. Hitachi iQ reduces sprawl, enhances governance, and mitigates risks like biased outputs or vulnerabilities. With 76% of large organizations relying on AI, governance and data quality challenges are urgent. Hitachi iQ’s innovative AI infrastructure solutions enable seamless data management, zero-trust security, and auditable retention, ensuring compliance and trust. By simplifying the complexities of AI pipelines and aligning with regulatory needs, Hitachi Vantara empowers enterprises to lead in the AI era. #AI #DataInfrastructure #SovereignAI #HitachiVantara #Hitachi_iQ https://lnkd.in/g8-w8eyJ

Interesting insights from Octavian Tanase on Why #AISovereignty Will Redefine #Enterprise #Competitiveness? My take aways The winners of the next era won’t just consume #AI rather they’ll own it. That translates to building intelligent data platforms that are secure, compliant, and autonomous across #hybrid environments. As leaders, our challenge is to transform storage and infrastructure into sovereign #AIplatforms where trust, performance, and control converge. This is the inflection point. Are your organizations preparing for the era of #AI sovereignty? Are you building the infrastructure to own your #AI future? #AISovereignty #DataInfrastructure #EnterpriseAI #DigitalTransformation #DataGovernance #HybridCloud #AIInnovation #TechLeadership #CIOLeadership

As AI becomes the backbone of national strategy and business success, trusted data infrastructure is no longer optional—it’s mission critical. Solutions like Hitachi iQ and VSP One are paving the way for secure, governed, and scalable AI adoption. The future of AI leadership will belong to those who get data infrastructure right.

As AI becomes more embedded in business operations, monitoring tools need to get smarter and provide real-time insights into whether models are delivering results efficiently and securely. Legacy monitoring tools are transitioning to observabilty platforms where end to end ´real time’ health and behaviour of the entire system are ´understood’! All this thanks to machine learning and AIOps algorithms.

Inside the AI-optimized data center Why “AI-ready” ≠ “traditional” General-purpose DCs were built for mixed IT. AI DCs are architected for time/latency-critical, massively parallelworkloads (LLMs, RAG, agents): ultra-dense accelerators, lossless/low-latency fabrics, high-throughput storage, and liquid-centric thermal envelopes. What AI DCs enable (vs. legacy) Throughput & time-to-model: Faster training/deployment with fewer bottlenecks. Operational agility: Modular designs that keep pace with rapidly changing chips, power densities, and cooling. Cost posture: Potential shift toward OPEX models while retiring stranded legacy capacity. Sustainability potential: Deliberate siting and advanced cooling can curb energy/water use if designed well. Edge alignment: Local inference/analytics to cut latency and backhaul. Core building blocks Accelerators: GPUs plus TPUs/NPUs/DPUs; thousands of nodes orchestrated as one fabric. Networking: High-bandwidth, low-latency interconnects (Ethernet/InfiniBand + optical) between compute–storage–edge. Storage/memory: NVMe, parallel/distributed files & objects; HBM and high-throughput tiers tuned for training I/O. Cooling: Direct-to-chip plates and immersion (single-/two-phase) to manage rack-level heat; studies show 15–82%reductions in energy, GHG, and water vs. legacy approaches when properly engineered. Facilities: Power trains sized for dense clusters; controls that co-optimize workload placement, power, and thermals. Who’s building them Hyperscalers: Global footprint and scale, but may need retrofits to meet new densities/cooling. Neocloud/GPUaaS: Speed and performance, but exposed to GPU supply constraints and higher unit costs. Colo specialists: Capital-efficient access to Tier-1 facilities, with integration/standardization trade-offs across sites. Operator playbook (practical) Design for liquid first (warm-water, higher supply temps); air is auxiliary. Right-size the fabric: Balance GPU/TPU clusters with storage/network I/O to avoid idle silicon. Telemetry to policy: Let thermal, power, and congestion signals drive scheduler decisions. Power strategy: Time/location-matched clean power; plan grid upgrades in lockstep with phases. Sustainability by design: Track PUE/WUE and whole-life carbon (incl. MEP & refrigerants); standardize for reuse/refurb. Bottom line AI isn’t just new software—it mandates next-gen infrastructure. Winners will pair accelerator-dense compute with liquid cooling, fabric-aware storage/networking, and clean-power-aligned sites, delivered through modular designs that keep pace with the silicon roadmap. #AIInfrastructure #DataCenters #HPC #GPUs #TPUs #Networking #NVMe #HBM #LiquidCooling #ImmersionCooling #ThermalManagement #EdgeComputing #PUE #Sustainability #WholeLifeCarbon

Dark data may be costing you more than you think. 💰 This article shows how AI is helping businesses surface and structure "dark data", the neglected information hiding in logs, emails, and sensors, among other areas. If your organization is looking to get more value from your existing data stack, this is a good place to start. Read the article and message us to discuss how AI can help transform data waste into insight. https://zurl.co/99R54