Data Sheet

Elik Eizenberg, CTO and Co-Founder

Using BigPanda's Features forRoot Cause Analysis

OverviewThe ever-growing complexity, scale and pace-of-change of IT environments puts a huge burden on IT Ops,

NOC, and DevOps teams, who are tasked with keeping these environments up and running. One of the

biggest challenges is Root Cause Analysis (RCA). When something breaks, we need to determine what

broke it, and we need to do it fast.

Unfortunately, there is no silver-bullet for root cause analysis: there is simply no one technique that teams

can master to address all types of incidents and outages. Modern IT environments are just too chaotic to

allow for it. IT professionals should be wary when vendors pitch a single, miraculous technique for RCA.

BigPanda’s philosophy is different. We encourage operations teams to embrace the chaos of modern IT

environments, and design an RCA strategy that relies on a range of techniques. Each technique will be

2 | BigPanda’s Top-5 Root Cause Analysis Features

useful in different contexts, and sometimes multiple techniques will be needed. This datatsheet outlines

five BigPanda features that assist in Root Cause Analyis.

Incident Timeline

Root Cause Changes

IT incidents usually materialize in the form of

many symptoms across your monitoring

systems. There’s often a very distinct order of

events. For example, load issues on a database

are followed by transaction timeouts in your logs,

which are then followed by application-level

latency alerts.

BigPanda’s Incident Timeline was designed to

help you easily make sense of this “cascading”

effect, see how an incident evolved over time,

and determine the cause-and-effect relationships

between alerts.

Around 85% of incidents are caused by changes.

In other words, in the vast majority of cases, the

root-cause of an incident is a change in the IT

environment.

BigPanda aggregates change data from all your

change feeds and tools, including CI/CD, Change

Management and Auditing. It then uses Open-Box

Machine Learning technology to identify the

changes that likely caused the incident.

Note that correlation does not imply causation,

which is why we’ve developed a proprietary Causal

Inference algorithm that can distinguish between

the two cases.

3 | BigPanda’s Top-5 Root Cause Analysis Features

2. Root Cause Changes

1. Incident Timeline

Dynamic Smart TitlesBigPanda’s Open-Box Machine Learning engine

automatically correlates related alerts into high-level

incidents. It then verbalizes the relationship

between these alerts, to help identify the common

denominator.

For example, if ten VMs experience load issues

because of a problem with one underlying physical

server, BigPanda will automatically display the

problematic physical server's name as the title of the

incident. Or if twenty devices report connectivity

issues due to one faulty upstream network switch,

BigPanda will display the switch's name as the title.

What makes this feature even more powerful is that the incident title is updated in real-time when new

alerts are added to the incident. This ensures operators have ongoing situational awareness.

Incident TopologyFew things are as difficult as navigating the dependencies

of a modern application, which is comprised of

microservices, databases, queues, storage, cloud and

on-prem servers, network devices, etc.

BigPanda makes it easy to understand these

dependencies in the context of a specific incident.

The Topology tab visualizes all the affected components

and their respective relationships.

While other vendors provide topology views, BigPanda

is unique in that it pieces together the topology by

merging information from configuration management

platforms, orchestration tools, APMs and CMDBs into a real-time topology mesh. This gives you unparalleled

visibility across the stack, encompassing applications architectures, cloud environments and physical networks.

4 | BigPanda’s Top-5 Root Cause Analysis Features

4. Incident Topology

3. Dynamic Smart Titles

5 | BigPanda’s Top-5 Root Cause Analysis Features

SummaryResolving incidents quickly and reducing MTTR requires that you detect incidents as close as possible to

real-time, investigate them and isolate their root-cause quickly, and then kick off remediation/orchestration

actions to resolve them. BigPanda provides a suite of root cause analysis features that allow IT Ops, NOC

and DevOps teams to develop an RCA strategy based on their needs and the different contexts in which

they operate.

Deep Links5. Deep LinksAs previously mentioned, there is no silver bullet for

Root Cause Analysis. While BigPanda offers users

several powerful RCA features, we also acknowledge

that there’s a vast number of RCA techniques offered

by other vendors. Therefore BigPanda enables

operators to dive quickly into any other tool that

might provide additional RCA insight.

The Deep Links feature turns BigPanda into an

intelligent gateway for operational context. The

relevant dashboard in a monitoring tool, a related

search in a log management tool, or an

appropriate runbook article in a knowledge base -

all of these are always just a click away in BigPanda.

www.bigpanda.io (650) 562-6555 info@bigpanda.io

About BigPandaBigPanda helps IT Ops, NOC and DevOps teams detect, investigate, and resolve IT incidents and outages,

faster and more easily than ever before.

Powered by Open Box Machine Learning, BigPanda captures alerts, changes and topology data from all

your disparate tools and uses machine learning to reduce IT noise, detect incidents and outages, and

surface their probable root cause, in real time.

Customers such as Intel, TiVO, Turner Broadcasting and Workday rely on BigPanda to reduce their operating

costs, improve service availability and performance, and de-risk and accelerate their digital transformation

initiatives.