BigPanda doubles-down on core AIOps functions - Ease of use and powerful analytics enhance the core
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BigPanda has come into a lot of money this year. This Software-as-a-Service (SaaS) pureplay AIOps company, formed in 2012, raised $190 million in January in a series D round of funding. This reflects a very positive outlook in the AIOps market in general and in BigPanda in particular. But for me, an additional, smaller amount of $15 million raised as an extension to the January round in August this year is highly significant, as it came from two BigPanda customers, UBS and Wells Fargo. Not quite the “I was so impressed with the product that I bought the company” but nonetheless an important indicator of customer satisfaction.
Last week I got a briefing from Blair Sibille, Sales Engineer in pre-sales for BigPanda on developments in its solution and also to get an idea about the direction of the company following that large injection of capital.
While some of the new capital has been used to grow Big Panda’s global footprint with the recent opening of a sales office in London and by an expansion in its global partner program, it is clear that a large chunk is being used to, as Blair stated, “double-down on AIOps.” Back in 2020 I wrote a Bloor Spotlight entitled, “Event Correlation and Automation – The Valuable Heart of AIOps.” This is the core of what BigPanda delivers. The term AIOps has been somewhat subverted by many vendors in the last couple of years to encompass even the most simple algorithmic machine learning and automation in almost any part of the IT operations sphere. BigPanda has no intention of getting into monitoring or service management, and will remain focused on developing their core AIOps solution.
The BigPanda solution is SaaS based, and one of the key tenets of their strategy is to make it very easy to adopt and keep using. There has been a strong focus on making it very easy to use, with extensive out-of-the box integrations with just about any monitoring tool you can think of and a range of customisable dashboards designed to give both senior business management and IT operations staff views that are meaningful and impactful. Importantly they have put effort and money into enhancing the post go-live experience. In September they relaunched BigPanda University with a key objective of giving their customers the skills and confidence to quickly become self-sufficient and self-serving in implementing and managing AIOps However, if customers need extra help, consulting and technical architect support can be purchased on an ad-hoc basis or, alternatively, BigPanda offers a resident solution architect who becomes part of the customer’s IT operations team offering proactive coaching and technical guidance.
A lot of focus is being placed on enhancing the analytics capabilities of the platform with a new Unified Analytics module that around 40% of existing clients have already switched to. With the huge amounts of telemetry data being ingested in modern containerised, micro-services environments BigPanda decided to re-engineer the back-end of the application and rearchitect their data pipeline to turbo-charge their analytics capabilities.
Artificial Intelligence (AI) can be a contentious subject. BigPanda have always stressed the transparency of their AI and Machine Learning (ML) algorithms. Previously ,it used the term Open-Box Machine Learning. But this is now being dropped in favour of Pragmatic AI that reflects the collaborative nature of building and previewing new algorithms with its customers. It also reflects the growing use of AI in automating remediations as well as the use of heuristics to present suggestions to customers using the new Unified Analytics capabilities.
I have focused on just some of the newer developments in BigPanda, but it is clear that the funding it has received is being aggressively applied to keep its core strengths in event correlation, automation and ease of use ahead of the game. At the same time, new features like self-service on-boarding and others still in development, will make it easier to acquire, for customers in both greenfield sites and those with a wide range of legacy monitoring tools.