Prevent and resolve IT outages before they happen
Data volume and service complexity are only increasing as organizations transform to take advantage of cloud scalability and cost, refactor to use microservices architectures, embrace CI/CD change velocities, or simply extend and expand services into new regions with new features. These trends have made it impossible for humans to keep up at the pace and scale necessary, causing incidents to go unnoticed in the unmanageable volumes of data.
This has led many organizations to embrace AIOps so their IT teams can stop spending their time firefighting and focus instead on initiatives that drive innovation for the business.
“I remember when I brought BigPanda into my NOC…The NOC was dying before automation because the stuff we were doing, people don’t do anymore.
So being able to automate manual operations and workflow gave us the ability to do new, more exciting work.”
Senior Manager of Operations,
The breaking point for IT operations
The challenges for IT operations teams are becoming worse by the day. Data volume and service complexity are only increasing as the following five trends push IT Ops to the breaking point. Each trend enables organizations to innovate, increase velocity and grow market share, however, they create massive challenges from an operational standpoint.
In recent years, enterprises have embarked on complex, multi-year cloud adoption and migration journeys. During this time enterprises need to operate both traditional on-prem systems and modern cloud-based applications.
The combination of dynamic cloud-native stacks and traditional, slower-moving IT stacks, each with its own distinct tooling, creates siloed visibility, and it increases the volume, velocity and variety of data IT operations teams must handle.
Modernizing applications by breaking down large applications and services into smaller ones to capture benefits of the cloud allows organizations to scale each independently and introduce better, higher availability for each service.
However, this results in complex topologies which can’t be represented in traditional CMDBs limiting enrichment and correlation, and it results in fragmented visibility. This creates major challenges for operations teams.
Mergers and acquisitions (M&A)
Deal making is surging in most sectors of the economy. Because each acquisition brings new IT operations tools, new datasets, and new workflows and processes, IT operations teams struggle to reconcile and rationalize it all while continuing to serve their customers without service interruptions and outages.
As enterprises modernize applications and create complex cloud-native apps and services, development teams push code to production thousands of times a day, sometimes, tens of thousands of times a day.
Since each change has the potential to cause an unintended incident or outage, IT operations teams faced with an outage struggle to match it with the change that caused it.
The ability for distributed DevOps teams to innovate using their preferred tools is crucial to delivering new features and functionality on an ongoing basis. But handling the enormous volumes of data generated by these heterogeneous – and siloed – tool sets makes it hard for IT operations to prevent and resolve outages.
BigPanda leads the way with domain-agnostic AIOps
Read the 2021 Gartner Market Guide for AIOps platforms
How to succeed with AIOps
For enterprises to succeed with AIOps, their AIOps platforms must deliver the following set of capabilities.
Integrates with existing tools
Every enterprise uses and depends on several different tools that span observability and monitoring, change, topology, collaboration and remediation. In almost all cases, these tools reflect years of investment, development and customization. Often these tools are deeply embedded into critical IT Ops workflows and processes. Your chosen AIOps platform must not require a long and painful long rip-and-replace project. Instead, it must integrate with all of your existing tools. Ideally, it must also provide APIs to future-proof your future tool choices.
Data preparation and cleansing
“Garbage in, garbage out” is a well-known maxim in IT, and it applies to IT Operations as well. Force-feeding IT alerts to your AIOps platforms’ artificial intelligence and machine learning algorithms without adequate normalization, enrichment and tagging – aka data preparation and cleansing – results in low-quality results at best. At worst, it can result in AIOps failure, if your teams aren’t presented with any actionable insights. That’s why your AIOps platform must deliver built-in normalization, enrichment and tagging that can work at scale and be able to process millions of IT alerts every day.
Match changes to incidents (identify root cause changes)
As applications modernize and enterprises migrate to the cloud, developers are able to continually enhance their apps and services, and release new features and enhancements, creating thousands of daily changes, any one of which can cause an incident or outage. A key requirement for any AIOps platform is the ability to ingest change data and rapidly correlate it with alerts from observability tools for greater context about an incident.
Many enterprise AI-powered systems obscure the “why” behind the artificial intelligence and machine learning decisions. This is a recipe for reduced trust and adoption, and can severely limit the value enterprises gain from their AIOps investments. Your AIOps platform’s machine learning-generated logic must be expressed in clear, easy-to-understand language; your teams must be able to edit it and incorporate their institutional knowledge, and they must be able to preview the effects of any changes they make. Finally, your IT Ops teams must be able to manage this without relying on, or requiring, expensive and scarce data scientists or machine learning engineers.
AIOps platforms are the hub of IT operations data. In addition to data collected from observability/monitoring, change and topology tools, AIOps platforms capture data related to each stage of the incident management pipeline (e.g. enrichment and correlation rates, or root cause change matching rates), incident outcomes, team performance and efficiencies, operational workload. Your AIOps platform must be able to contextualize, extract and present this data natively or easily send its’ contextualized data to your preferred BI platform of choice. That’s the only way you can measure, track and improve your IT Ops KPIs and metrics.
Today, some enterprises have large, centralized IT Ops and NOC teams, whereas others have dozens or even hundreds of distributed DevOps and SRE teams. Enterprises also different in their level of IT Ops maturity, with some enterprises having “grown up” in the cloud, while others are mid-way or even just getting started, with their modernization initiatives. Finally there are several different – and equally important – stakeholders that can benefit from AIOps, from NOC Managers and L1 users to VPs of IT Ops to service owners to the heads of business units and CIOs. Your AIOps platform must be able to present data in easy-to-understand dashboards and reports that everyone can use to make decisions and take action.
“There is no future of IT operations that does not include AIOps. This is due to the rapid growth in data volumes and pace of change (exemplified by rate of application delivery and event-driven business models) that cannot wait on humans to derive insights.”
Market Guide for AIOps Platforms, Gartner Research, April 2021
BigPanda’s Event Correlation and Automation platform, powered by AIOps
Built from the ground-up for large scale and complex IT environments, BigPanda’s Event Correlation and Automation platform, powered by AIOps, helps organizations prevent and resolve IT outages.
Designed to go live and into production in just 10-12 weeks, BigPanda’s AIOps platform delivers three key capabilities: Event Correlation, Root Cause Analysis and Level-0 Automation.
Event Correlation uses explainable AI to correlate disparate streams of observability, monitoring, change and topology data into context-rich incidents in real time.
Root Cause Analysis uses explainable AI to surface probable root cause and root cause changes in real time, inside today’s complex and dynamic IT environments.
Level-0 Automation eliminates repetitive manual incident response tasks to accelerate incident response, remediation and resolution.
The result is a solution that processes diverse datasets, supports multiple use-cases and connects diverse teams like IT Ops, NOC, DevOps and SREs – all while enabling cost reduction, increased performance and availability, and accelerated digital transformation.
So, what are you waiting for?