BigPanda launched Unified Analytics, a revamped feature that gives IT Ops teams new self-service analytic capabilities to create new and highly interactive dashboards and reports from complex IT Ops alert data.
BigPanda also delivers a complete library of ready-to-use operational and value dashboards that allow users to rapidly track and measure IT operations KPIs, metrics and value use cases that show the impact of IT Ops improvements to the business.
BigPanda’s Unified Analytics offers new, out-of-the-box, persona-based dashboards that help IT organizations translate IT Ops metrics into business impact. Leaders can now discover the impact of alert quality on IT Operations, track how consistently diverse teams are impacting incident response and better demonstrate — using metrics — improved IT Ops productivity.
“With so much IT Ops data created on a daily basis, teams need an easy way to harness it so they can see what’s working for the business and what’s not,” said Fred Koopmans, BigPanda’s chief product officer. “BigPanda was already the most robust AIOps platform on the market, and with our new Unified Analytics, organizations harness a virtuous cycle of data-driven analysis, decision making and measurement that has a massive impact on IT Operations that delivers real-life business outcomes.”
The Next Generation of IT Operations
With BigPanda’s Unified Analytics, IT Ops teams can ingest, normalize and tag all alert data to view within a single pane of glass. A purpose-built data model delivers calculated fields that remove the need for complex queries to measure how long it takes to assign, engage with, fix and resolve incidents. Users can create new and highly interactive dashboards with drag-and-drop functionality that make KPI measurement easier, allowing teams to ask the right questions that drive operational improvements.
Key benefits BigPanda’s Unified Analytics include:
- Organization-wide visibility. Use insights from data to improve operational and team performance thanks to comprehensive, ready-to-use IT Ops dashboards.
- Cost savings. Measure alert quality and connect the impact on noise reduction and operator productivity.
- Improved IT Ops efficiency. Operate at scale by reducing redundancies across both teams and tools.
BigPanda customers can try a beta version of Unified Analytics beginning in July, with general availability expected in August.
The Latest
In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...
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.