INETCO Systems Limited announced the completion of a real-time alert feed to IBM’s Tivoli Netcool/OMNIbus management systems.
This data forwarding capability will grant OMNIbus users “one-stop” access to transaction intelligence that will help them quickly isolate enterprise service issues within physical, virtual and Cloud-based production environments.
A version of INETCO Insight which includes data forwarding capabilities is available for assisted evaluation and purchase immediately.
“63% of user organizations in TRAC's recent survey identified time spent correlating performance data as a key challenge,” said Bojan Simic, President & Principal Analyst at TRAC. “INETCO Insight’s proven ability to correlate data collected from different sources, coupled with their ability to feed detailed transaction intelligence to common IT management systems such as HP Operations Manager, Splunk or IBM OMNIbus, positions the product well to overcome this challenge. By accessing this data through a single platform, users can now take advantage of synergies between different types of data, cross-correlate application/transaction events with infrastructure events, and quickly determine how service levels are impacted during outages or brownouts.”
By feeding real-time transaction intelligence into their existing IT management systems, OMNIbus users can now track and set customized alerts based on every individual transaction, even those traveling through third party and virtual environments. This real-time transaction data and analytics can be used to speed up the identification of transaction failures, response time slowdowns and anomalies, such as unexpected response codes or TCP disconnects, anywhere within their production environment.
“Application Developers supporting custom applications have different monitoring needs than IT Operations teams responsible for the performance of all critical applications in production,” said Bijan Sanii, President and CEO of INETCO. “INETCO Insight supplies vital IT Operations Analytics (ITOA) that show how every service transaction is being delivered and how all parts of an enterprise system are performing. IBM customers can now leverage their existing OMNIbus investment across more user groups, more critical application platforms and more third party and cloud-based infrastructures - without deploying agents, code changes or extra traffic loads.”
The Latest
Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...
In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...
Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...
Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...
Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...
The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...
The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...
In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...
AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.
The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...