AIOps startup BigPanda announced Jan. 12 that it has raised $190 million in a new round of funding, increasing the company’s valuation to $1.2 billion and giving it unicorn status.
AIOps is a rapidly growing technology category in which artificial intelligence (AI) is used to help optimize and manage IT operations and systems. Headquartered in San Francisco, BigPanda has seen rapid adoption and growth since its founding in 2012, particularly over the last few years as demand and adoption of AIOps have accelerated.
"The AIOps market is moving from an early adopter technology to a mainstream solution on the roadmap of every CIO," Assaf Resnick, co-founder and CEO of BigPanda, told ITPro Today. "This is being driven by the fact that digital services are no longer optional – they are mandatory for businesses to survive."
BigPanda's Path to AIOps
When Resnick started BigPanda, the company was not an AIOps vendor – it was a marketing technology company. Resnick recounted that while building out the original vision, the company developed a sophisticated cloud environment to run its services.
"As we started to scale, we ran into operational issues, so we solved them," he said.
Resnick said that his "lightbulb moment" came when he and his co-founders realized that if BigPanda, at its size, struggled with the complexity of infrastructure and the amount of data that comes from monitoring tools, other companies were likely dealing with the same or larger issues to deal. That’s when they pivoted the business to AIOps, with the company shifting its focus to solving IT operations problems with AI and automation.
"We didn’t just evolve our business model – we ditched the marketing technology product and created the BigPanda AIOps platform out of a need we directly experienced," Resnick said.
Market Demand for AIOps and BigPanda's Platform
There are a number of market dynamics driving demand for AIOps as well as BigPanda's platform.
According to BigPanda’s 2021 AIOps Benchmark Report, published on Jan. 6, the majority of organizations are planning to use or are already using some type of AIOps platform. Among the key reasons why are the challenges of DevOps and multi-cloud architectures. Seventy percent of respondents said it takes 3 hours or longer to resolve major incidents.
"DevOps was supposed to simplify ITOps, and some believe it should dramatically reduce major incidents and change-related outages," Resnick said. "But the research showed that supporting reliable hybrid and multi-cloud architectures remains challenging, especially for large organizations."
Each day, ITOps and site reliability engineering (SRE) teams face a tsunami of data associated with their digital services, Resnick said. In fact, he noted that most organizations have anywhere from 50 to 100 monitoring tools that spew out data about specific applications, systems, cloud and on-premises infrastructure. Most organizations also have tools to track specific types of changes and map topologies. In Resnick's view, none of those individual tools provides a complete picture, and it is no longer possible for humans to filter through all of their data and piece together the story of an incident.
"Dozens of people who could be doing high-value work are, instead, on multi-hour bridge calls, pointing fingers and trying to avoid blame," Resnick said. "For these reasons, most CIOs and their teams are evaluating and adopting AIOps."