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.”
The Latest
I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...
Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...
For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...
Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...
Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...
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 ...