In the recent past, every enterprise has had to deal with an outage, leading to war rooms where ITOps teams are put on the spot. While they take on the burden of ensuring 100% uptime, it is often the tools they employ which don’t live up to their promises.
Especially in the wake of the pandemic, with working norms being redefined, ITOps teams have been under even greater pressure to deliver. While they strive to be efficient and rely on cutting-edge technology, uptime is often elusive. The cost of downtime can vary between enterprises but an effective solution/toolset to ensure resilience is similar across teams.
Fig 1: Rough depiction of Enterprise ITOps Toolset Architecture
What Stays and What Goes?
One conundrum that ITOps teams face is deciding which toolsets to procure anew, and which ones to replace. While TCO and ROI of these toolsets play an important role in the decision matrix, every ITOps team must take the following set of issues into account:
1. With too many tools working in silos, how to consolidate similar functionality tools?
2. When toolset features overlap with others, how to zero in on one?
3. With too many alerts due to varied toolsets, current correlation mechanism is manual and cumbersome.
4. Root cause analysis takes a long time and is often inaccurate.
5. Capacity issues are frequent and cause outages. How to do effective sizing?
6. The ITSM tool records incidents and their fixes, but how sure are we that things will work as they must now?
Finding Solutions
Enterprises are re-architecting their monitoring toolset platforms. Most teams feel that vendor locking is a massive roadblock and are instead on the lookout for toolsets that can be plugged into the enterprise monitoring platform, tried, tested, and replaced. They look for a wide range of technical features including support for open telemetry, open API, ease of customizations, open data ingestion options, ease of data extractions, and more.
This has also led to the rise of pay per use licensing. Today, enterprises are enforcing upon tech vendors plug and play options. But the question of what to do with the collected data remains.
New innovations in AI/ML statistical analysis are helping ITOps teams process stored data, and provide insights that were previously overlooked by teams. AIOps or Artificial Intelligence for IT Operations is focused on solving issues like alert correlation, alert flooding, and false positives, by deep diving into cause, rather than symptoms, or suggestive fixes for issues. A common demand from ITOps teams is that they want solutions to be predictive rather than prescriptive.
The expectation from any AI/ML platform that is tested is that they should be predictive, suggest fixes for future issues and provide plug and play solutions in the enterprise monitoring platform.
HEAL AIOps
The unique capability of “Recognize and respond to smaller issues before they escalate” is HEAL’s USP. HEAL’s patented AI/ML algorithms help in observability, AI-assisted analytics and automated healing capabilities for Enterprise Business Applications. HEAL’s ML platform can fill in the gaps identified by any Enterprise Monitoring Platform. HEAL provides the capability of Agent & Agentless mechanisms to monitor business applications and give insights.
Fig 2: HEAL’s ML Capabilities