Splunk announced the limited availability of Splunk Industrial Asset Intelligence (IAI), its first Internet of Things (IoT) solution.
Splunk IAI helps organizations in manufacturing, oil and gas, transportation, energy and utilities monitor and analyze industrial IoT data in real time to create a simple view of complex industrial systems while helping to minimize asset downtime.
Splunk IAI offers a packaged set of capabilities that helps customers pivot their operational strategy from reactive to proactive.
“Real-time analytics is an absolute must for manufacturers today, but organizations are struggling to bridge the gap between legacy systems, industrial assets and sensor data,” said Ammar Maraqa, SVP, Business Operations and Strategy and GM of IoT Markets, Splunk. “Splunk IAI provides a single solution that helps ensure industrial systems are running at full capacity, enabling organizations to significantly save resources and money on unplanned downtime.”
Built on top of Splunk Enterprise, Splunk IAI enables capture and correlation of data from Industrial Control Systems (ICS), sensors, SCADA systems and applications, making it easy to monitor and diagnose equipment and operational issues in real time. This data-driven approach to industrial operations enables customers to respond to issues faster without affecting production, where unplanned downtime can equate to millions of dollars in lost revenue.
A limited availability release of Splunk IAI will be introduced on April 23, 2018 at a leading industrial conference, with general availability for all Splunk customers this fall.
The Latest
For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...
New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...
Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...
In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ...
In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...
When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...
Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...
Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...
As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...
For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...