Enterprise AIOps

The traditional methods of IT Operations, which are still in practice at many organizations are evaluating different means to drive the behaviors and mindsets of people towards adoption of better practices to gain efficiencies and enhanced delivery of services. As the world moved fast towards digitization (accelerated by Covid in the past few years), most of the businesses have IT as the main enabler. Thus, any impact to the availability of the IT systems has a direct impact to revenue, brand value and ability to maintain its advantage in the extremely competitive market. Hence, the stability of IT systems is now a board level topic and more organizations are putting the necessary focus/investments to do what's necessary to maintain stability.

We have always seen that IT Operations teams have been solving a lot of issues manually viz. monitoring events, creating incidents, correlation across multiple systems and data points and resolving them to the best of their ability and of course as fast as possible. With the IT landscape getting more complex by the day with multi-cloud environments, on/off premise workloads, micro-service architectures with cloud native applications, the traditional methodologies start to look archaic and not relevant to the changing times. There is a need to provide some relief to our IT Operations colleagues.

What is the way forward?

AIOps or Artificial Intelligence for IT Operations is a field that has been gaining massive importance over the last few years.?AIOps uses AI and ML to automate IT operations, right from event correlation across multiple data sources and root cause analysis to remediation and proactive monitoring. In complex IT landscapes detecting anomalies becomes very time consuming resulting in poor customer experience. Using machine learning for large data sets, its easier to detect such anomalies and fix the issues much faster and accurately leading to reduced outage durations. As the data for analysis accumulates, you're able to do better predictive analysis, which is the next step in maturing of AIOps. You're able to analyse historical data, predict failures and fix them proactively making it a 'non-issue'; the nirvana state for IT Engineers :). This has a direct impact on improved stability of the IT systems and enhanced customer service. Since, you're using a lot of automation to make this happen, it's easier to scale the IT landscape as business grows leading to better time to market, increased revenue and predominantly customer satisfaction.

How to make this happen?

As always, this is a combination of people, processes and tools and I would add an additional one - 'mindset'. Baselining the existing organization certainly helps to know where you stand. Need for the right skills is a no brainer to drive this forward across the various stakeholder teams. Define a roadmap in collaboration with the teams who are an integral part of this journey - very essential so that all are marching in the same direction. For sure you need a couple of building blocks to get towards full AIOps.

1. The data sources or data generators, which provide actionable insights, statistical analysis, anomaly detection, etc.
2. Your system of records, single source of truth for post incident analysis, trends, etc.
3. Using machine learning on the data in the systems, analyse/interpret, use supervised/unsupervised learning algorithms, neural networks for pattern identification leading towards predictive analytics
4. Tool(s) to do the necessary automations as one size doesn't fit all and my opinion has been to not put all eggs in one basket. Use auto-remediations/self healing to get the real impact of AIOps apart from point solutions for individual automations.
5. Orchestration to tie this all together and end-to-end governance ensuring the sanctity of the ecosystem is maintained

For those who want to be ambitious - add a layer of Conversational AI to top it up and take it to the next level - sky is the limit !!

Which are the right tools/systems?

There is no one right answer as the needs for different organizations are different due to the IT landscapes they operate in, the requirements from their customers AND there are many tools in the market with new capabilities popping up each day. The wise thing to do is to evaluate the investments made within organizations, to ensure that they're leveraged before any new investments are brought in but also map it against the AIOps roadmap that you have planned for. A thorough study of existing tools and their product roadmaps is important to take an informed decision to keep or replace. Most of the times there is no need for a rip and replace in brown field IT landscapes especially with the investments made, however, the will to take the right step forward is always the differentiator amongst organizations.

Why all this fuss?

That's a great question to ask. If you're looking for the below mentioned benefits, then there's no looking back

• IT stability - high availability brings in customer satisfaction, trust that you can run your business, thus improving revenue and maintaining high brand image
• Faster recovery in case of outages, issues will always happen; those who come back up fast are the ones who stay in the longer run
• Reduce complexity and bring in efficiencies within systems and processes
• Enable employees to focus on value added tasks by moving from hard work to smart work
• Better customer experience by driving predictability in business
• Green IT by lowering data center emissions
• Cost efficiencies

Sounds interesting isn't it, so why wait. Let's together explore the world of AIOps !!