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BigPanda Updates Root Cause Change Feature with New GenAI Capabilities

BigPanda launched its updated Root Cause Change (RCC) feature with new GenAI capabilities to automate incident analysis – identifying root cause faster and with higher accuracy than humanly possible.

Organizations are grappling with a surge of incidents tied to frequent infrastructure/software changes, with 85% of issues originating from these shifts and traditional IT tools can't keep up. RCC will now leverage explainable AI/ML to correlate incident alerts with infrastructure change data and improve incident root cause identification, reducing response times by 50%. With BigPanda's updated RCC feature, you're able to:

Reveal change data linked to IT incidents in real-time: Correlate multi-source alerts with change data to identify the probable change that caused an incident.

Leverage multi-source change aggregation: Connect to all change feeds and tools (such as ServiceNow, JIRA, Jenkins and CloudTrail), and aggregate their data.

Utilize real-time IT Incident correlation: Correlates multi-source alerts with change data to identify incident-impacting changes across hybrid cloud environments.

Apply pragmatic, explainable AI: BigPanda's pragmatic AI provides explanations why suspected changes were linked with an incident in easy-to-understand language.

Collaborate with change tools and teams: If a potential change is matched, users can explain their choice and propose next steps, which will be communicated directly to the tool that initiated the modification.

Report analytics: A new integration with Unified Analytics enhances efficiency in measuring, improving, and operationalizing root cause change investigation across all applications and services.

Customers share their POV on working with BigPanda:

"With BigPanda, our IT noise is not only reduced, but we are able to identify root cause in real-time- who the responsible team is, who owns the service that's alerting, etc. which is significantly reducing our MTTR. One of the biggest drivers that we have right now is auto-remediation."
Priscilliano Flores, Staff Software Systems Engineer at Sony Interactive Entertainment

"Previously, we had upwards of 2,500 alerts in a day. With BigPanda we achieved a 94% reduction in alert noise, which allowed us to consolidate those 2,500 alerts into 150 actionable events."
Sanjay Chandra, CIO at TiVo

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BigPanda Updates Root Cause Change Feature with New GenAI Capabilities

BigPanda launched its updated Root Cause Change (RCC) feature with new GenAI capabilities to automate incident analysis – identifying root cause faster and with higher accuracy than humanly possible.

Organizations are grappling with a surge of incidents tied to frequent infrastructure/software changes, with 85% of issues originating from these shifts and traditional IT tools can't keep up. RCC will now leverage explainable AI/ML to correlate incident alerts with infrastructure change data and improve incident root cause identification, reducing response times by 50%. With BigPanda's updated RCC feature, you're able to:

Reveal change data linked to IT incidents in real-time: Correlate multi-source alerts with change data to identify the probable change that caused an incident.

Leverage multi-source change aggregation: Connect to all change feeds and tools (such as ServiceNow, JIRA, Jenkins and CloudTrail), and aggregate their data.

Utilize real-time IT Incident correlation: Correlates multi-source alerts with change data to identify incident-impacting changes across hybrid cloud environments.

Apply pragmatic, explainable AI: BigPanda's pragmatic AI provides explanations why suspected changes were linked with an incident in easy-to-understand language.

Collaborate with change tools and teams: If a potential change is matched, users can explain their choice and propose next steps, which will be communicated directly to the tool that initiated the modification.

Report analytics: A new integration with Unified Analytics enhances efficiency in measuring, improving, and operationalizing root cause change investigation across all applications and services.

Customers share their POV on working with BigPanda:

"With BigPanda, our IT noise is not only reduced, but we are able to identify root cause in real-time- who the responsible team is, who owns the service that's alerting, etc. which is significantly reducing our MTTR. One of the biggest drivers that we have right now is auto-remediation."
Priscilliano Flores, Staff Software Systems Engineer at Sony Interactive Entertainment

"Previously, we had upwards of 2,500 alerts in a day. With BigPanda we achieved a 94% reduction in alert noise, which allowed us to consolidate those 2,500 alerts into 150 actionable events."
Sanjay Chandra, CIO at TiVo

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Payment system failures are putting $44.4 billion in US retail and hospitality sales at risk each year, underscoring how quickly disruption can derail day-to-day trading, according to research conducted by Dynatrace ... The findings show that payment failures are no longer isolated incidents, but part of a recurring operational challenge that disrupts service, damages customer trust, and negatively impacts revenue ...

For years, the success of DevOps has been measured by how much manual work teams can automate ... I believe that in 2026, the definition of DevOps success is going to expand significantly. The era of automation is giving way to the era of intelligent delivery, in which AI doesn't just accelerate pipelines, it understands them. With open observability connecting signals end-to-end across those tools, teams can build closed-loop systems that don't just move faster, but learn, adapt, and take action autonomously with confidence ...

The conversation around AI in the enterprise has officially shifted from "if" to "how fast." But according to the State of Network Operations 2026 report from Broadcom, most organizations are unknowingly building their AI strategies on sand. The data is clear: CIOs and network teams are putting the cart before the horse. AI cannot improve what the network cannot see, predict issues without historical context, automate processes that aren't standardized, or recommend fixes when the underlying telemetry is incomplete. If AI is the brain, then network observability is the nervous system that makes intelligent action possible ...

SolarWinds data shows that one in three DBAs are contemplating leaving their positions — a striking indicator of workforce pressure in this role. This is likely due to the technical and interpersonal frustrations plaguing today's DBAs. Hybrid IT environments provide widespread organizational benefits but also present growing complexity. Simultaneously, AI presents a paradox of benefits and pain points ...

Over the last year, we've seen enterprises stop treating AI as “special projects.” It is no longer confined to pilots or side experiments. AI is now embedded in production, shaping decisions, powering new business models, and changing how employees and customers experience work every day. So, the debate of "should we adopt AI" is settled. The real question is how quickly and how deeply it can be applied ...

In MEAN TIME TO INSIGHT Episode 20, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA presents his 2026 NetOps predictions ... 

Today, technology buyers don't suffer from a lack of information but an abundance of it. They need a trusted partner to help them navigate this information environment ...

My latest title for O'Reilly, The Rise of Logical Data Management, was an eye-opener for me. I'd never heard of "logical data management," even though it's been around for several years, but it makes some extraordinary promises, like the ability to manage data without having to first move it into a consolidated repository, which changes everything. Now, with the demands of AI and other modern use cases, logical data management is on the rise, so it's "new" to many. Here, I'd like to introduce you to it and explain how it works ...

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