In anticipation of our New York City Summit Event on October 19th, we’re highlighting some of the great partners who will be in attendance in this Exhibitor Series. We’re excited to share our latest Q&A with Assaf Resnick, CEO of BigPanda.
(The image above shows how BigPanda’s powerful aggregation engine normalizes alerts from AppDynamics and all of your other monitoring systems into a single unified data model.)
AppD: For visitors who don’t know BigPanda ahead of the Summit, what do you do?
AR: BigPanda is a machine learning platform for automating IT Service Operations at large enterprises. Our customers include Intel, Workday, Autodesk and Cisco. Service Operations teams – sometimes referred to as NOCs, DevOps or SREs – are the folks within IT charged with keeping mission-critical software, services and infrastructure running. BigPanda automates the ability of these teams to keep the business running in the face of an IT stack that’s exploded in scale, fragmentation, and complexity over the last 15 years.
Data center transformation in recent years has been driven by, among others, cloud, microservices, CI/CD, and DevOps. This transformation has allowed IT to become an enabler of agility and speed, which is great. But for the folks in the NOC, it’s left them with a flood of machine data coming out of the data center that they struggle to manage in order to keep critical business services running. This tsunami of monitoring data is heterogeneous, unstructured, constantly changing and moving faster than ever before. Legacy platforms that used to help make sense of this data – like IBM Netcool or HP OMI – weren’t built for today’s modern, dynamic IT environments.
That’s why BigPanda employs machine learning to separate the signal from this tsunami of IT noise. Our platform automates incident correlation – meaning we do the heavy lifting of parsing all this complex event data, normalizing and correlating it, and turning it into insight for the NOC.
AppD: In what ways do you believe the ITOM and ITSM markets have evolved over the past few years?
AR: BigPanda exists because of the earthquake that has occurred along these two tectonic plates called ITOM and ITSM. These used to fit together neatly, but no longer do. The tool fragmentation in ITOM has bumped up against the inherent scalability limitations of ITSM. BigPanda is the layer in the middle that cushions IT organizations against these tremors.
The traditional ITOM stack was fairly monolithic and slow moving. It was sourced largely from a single legacy vendor like BMC or HP. Code and infrastructure changes occurred in an orderly and infrequent fashion. That synced well with ITSM tools that were built to handle a finite amount of incidents, and to manage the human workflows of resolving those incidents.
However, today’s typical ITOM stack consists of multiple global clouds, thousands of VMs and containers, and dozens of best-of-breed technology vendors. Code and infrastructure changes no longer occur once or twice per quarter, but several times a day. The more things move; the more things break. As a result, the volume of alerts and incidents coming out of the data center has grown by orders of magnitude. This tsunami of machine data coming out of the ITOM stack has overwhelmed ITSM tools, which are designed to manage manual processes – not machine data at scale. BigPanda’s platform intelligently correlates all this machine data, turning it back into insights and workflows at that human scale.
AppD: How have the challenges faced by IT Operations changed as a result?
AR: The job of IT Service Operations has become a lot harder. There are a lot more moving parts to keep track of. Problems used to occur in slow, predictable cycles. Today’s IT incidents are explosive and fast moving.
The pace of IT service delivery has radically increased. With the rise of digital services, the pressure is on to meet customer SLAs within strictly defined parameters. For the NOC operator, life has become an unmitigated nightmare. For IT executives, the situation is just as challenging. Their current response to the growing flood of machine data is to throw more people at the problem, which has led to bloated headcount, out of control costs, and increased business risk.
AppD: In what ways can BigPanda address the new challenges?
AR: We help our enterprise customers automate their way out of this mess. Our data scientists have applied machine learning to connect the dots between all these monitoring, change management and incident management tools. BigPanda helps customers understand in real time the health of their critical business services and how to resolve issues quickly. This improves MTTR, service availability and most importantly, customer experience.
Some the largest enterprises with the most complex IT environments in the world depend on BigPanda to power their Service Operations. For example, Cisco uses BigPanda to improve the accuracy of root cause analysis and productivity of their tier 1 NOC engineers. In fact, Cisco was able to reduce monthly incident ticket volume by 98 percent using BigPanda!
AppD: Can you elaborate on “Algorithmic Service Operations” and what that means?
AR: Sure. We exist to automate the mission-critical functions of the Service Operations team. Now automation in Service Ops has been around for a long time. But the previous generation of solutions, such as IBM Netcool and HP OMI, were all rules-based. Deploying such solutions require an army of professional services and 18 months to write a large set of rules to automate manual, predictable actions. In today’s dynamic environment, this approach just won’t work any longer.
Enterprises instead need to take an “algorithmic” approach, which means applying machine learning to automatically interpret your ever-changing dynamic applications, microservices, cloud infrastructure, et cetera. We get customers up and running in a handful of weeks instead of the months it used to take. Moreover, our platform learns and adapts to our customers changing IT stack.
AppD: How does the partnership of BigPanda with AppDynamics help IT Operations teams?
BP: We share a common mission to increase visibility and availability of modern enterprise services. BigPanda has helped a lot of AppDynamics customers such as Shutterfly, Cisco and Caesars Interactive to automate their Service Ops.
AppDynamics provides critical information about application performance. We feature open integration with the automated Alert & Respond capabilities in AppDynamics – from metric thresholds defined in Dynamic Baselines to Health Rules and Policy definitions. Any predefined action automated in a runbook can trigger an alert in BigPanda. It’s seamless and fully configurable.
These insights can be further enriched by events and information from other layers in the IT stack. We help our mutual customers understand event data coming from AppDynamics in the context of a broader set of ITOM data — log monitoring, system monitoring, network monitoring — in order to determine the overall health of a business service or the root cause of an outage incident. We give AppDynamics users a holistic view into what’s going on across their IT stack, how it’s affecting the business, and what they should do about it.
AppD: Why should delegates come and visit the BigPanda booth?
AR: If conference delegates have the kind of problems we’ve outlined, they should come and see us. We’re happy to help AppDynamics’ large enterprise customers achieve their individual strategic objectives around modernizing apps and infrastructure. With BigPanda, they can automate and scale their Service Operations to better support and de-risk these modernization initiatives.
Register here to book your free place at the NYC Summit on October 19th and meet the BigPanda team there.
Assaf Resnick is the founder & CEO of BigPanda. He began his career at Moody’s Investors Services before becoming a principal at Sequoia Capital. At Sequoia he focused on public and early stage technology, internet, energy and mobile ventures. Assaf received a B.S. in Business Administration from the Haas School at University of California at Berkeley.