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ITRS Group Launches ITRS Insights

ITRS Group launched ITRS Insights, a new IT Operation Analytics (ITOA) application, built on its new Valo technology.

Both the application and technology address the challenge of running real-time analysis and search functions on time-series, semi-structured and even unstructured data simultaneously. Valo is an industry independent technology, and ITRS Insights brings these abilities to the growing ITOA space to help businesses extract maximum value from their increasingly plentiful and important operational data.

In data-intensive industries like financial services, unlocking this data can be crucial. Businesses can benefit from previously unavailable insights hidden in their data. These can add value and keep them ahead of the competition, or help them comply with ever-changing regulatory requirements. Insights is capable of delivering this analysis in real-time, across terabytes of data, without the need for batch processing, and can work in tandem with, or independently of, ITRS' existing application monitoring technology, Geneos.

ITRS CTO Justo Ruiz Ferrer explained: “Over the past three years we’ve built Valo from the ground up to crunch through big data, and Insights brings that power to ITOA. You’re now able to cross-analyse semi-structured, unstructured and time-series data in real-time. For example, an investment bank might want to look at their trade latency from data in log files, and system and application metrics to see why latency is unexpectedly high, and whether it’s usual compared to previous historic levels of business activity – that requires a combination of real-time analytics on semi-structured data, and machine learning and anomaly detection on structured historical data respectively. This is now possible with Valo and ITRS Insights.”

Insights is the first application built on ITRS’ new technology, Valo: a software platform with a Software Developer Kit (SDK) that provides the ability to store and perform real-time and historical analytics on masses of data. Valo is industry independent, and ITRS hopes to work both in-house and with partners to build a variety of applications for big-data, real-time analytic intensive use-cases, both in its core financial services space and beyond.

“Valo really is one-of-a-kind combination of the leading concepts in big data and analytics – but we don’t expect it to stay that way,” said Guy Warren, CEO, ITRS. “At the moment, it’s very difficult to handle both time-series and semi-structured data storage so that it can be queried and analysed in real-time. Those trying to do so are usually patching together some combination of standard tools such as Spark and ELK. Having all of that in one powerful, streamlined platform is too valuable to businesses for others not to follow Valo’s lead. The challenge for us is to keep investing in the product so that Valo stays ahead of the game.”

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ITRS Group Launches ITRS Insights

ITRS Group launched ITRS Insights, a new IT Operation Analytics (ITOA) application, built on its new Valo technology.

Both the application and technology address the challenge of running real-time analysis and search functions on time-series, semi-structured and even unstructured data simultaneously. Valo is an industry independent technology, and ITRS Insights brings these abilities to the growing ITOA space to help businesses extract maximum value from their increasingly plentiful and important operational data.

In data-intensive industries like financial services, unlocking this data can be crucial. Businesses can benefit from previously unavailable insights hidden in their data. These can add value and keep them ahead of the competition, or help them comply with ever-changing regulatory requirements. Insights is capable of delivering this analysis in real-time, across terabytes of data, without the need for batch processing, and can work in tandem with, or independently of, ITRS' existing application monitoring technology, Geneos.

ITRS CTO Justo Ruiz Ferrer explained: “Over the past three years we’ve built Valo from the ground up to crunch through big data, and Insights brings that power to ITOA. You’re now able to cross-analyse semi-structured, unstructured and time-series data in real-time. For example, an investment bank might want to look at their trade latency from data in log files, and system and application metrics to see why latency is unexpectedly high, and whether it’s usual compared to previous historic levels of business activity – that requires a combination of real-time analytics on semi-structured data, and machine learning and anomaly detection on structured historical data respectively. This is now possible with Valo and ITRS Insights.”

Insights is the first application built on ITRS’ new technology, Valo: a software platform with a Software Developer Kit (SDK) that provides the ability to store and perform real-time and historical analytics on masses of data. Valo is industry independent, and ITRS hopes to work both in-house and with partners to build a variety of applications for big-data, real-time analytic intensive use-cases, both in its core financial services space and beyond.

“Valo really is one-of-a-kind combination of the leading concepts in big data and analytics – but we don’t expect it to stay that way,” said Guy Warren, CEO, ITRS. “At the moment, it’s very difficult to handle both time-series and semi-structured data storage so that it can be queried and analysed in real-time. Those trying to do so are usually patching together some combination of standard tools such as Spark and ELK. Having all of that in one powerful, streamlined platform is too valuable to businesses for others not to follow Valo’s lead. The challenge for us is to keep investing in the product so that Valo stays ahead of the game.”

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Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

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