Prepare for AIOps by preparing your data

AIOps solutions have become critical for businesses looking to improve service reliability. See why effective orchestration, correct implementation and above all, good data are the keys to success.

 The artificial intelligence for IT operations (AIOps) market will grow from $13.5 billion in 2020 to more than $40 billion in 2026, according to Mordor Intelligence. This massive growth reveals the increasing importance of continuous availability, or ensuring a business’s critical apps and services are always on and performing well.

Achieving high availability has become an absolute business imperative — just ask Slack or Facebook what happens when services go down. Downtime can cause enterprises to lose revenue (sometimes to the tune of millions of dollars per hour), halt internal operations and compromise customer loyalty.

But while maintaining availability is critical, avoiding service outages in our complex and distributed IT ecosystems is also very difficult. That is, it’s difficult without the right tools.

This is where the strategic use of AIOps can help. A next-generation AIOps solution can help DevOps and site reliability engineering (SRE) teams  improve service reliability by detecting potential issues early in the incident lifecycle, before they impact the business. And a well-implemented tool can streamline the incident response by identifying who should respond, giving that team context to determine the right course of action and recognizing patterns to ensure those issues don’t happen again.

By guaranteeing less downtime and more business continuity, AIOps is rapidly becoming the solution modern businesses can’t afford to live without. But there’s a rather large caveat: Successful outcomes are contingent on good data.

The difference between a successful AIOps outcome and a failed one lies in the tool’s setup and implementation. Some people expect that they can buy an AIOps solution, throw data at the technology and it magically works. The reality is: Tech teams need to orchestrate AIOps solutions — and all AI-driven technology, for that matter — to yield successful business outcomes.

The best outcomes typically occur when an AIOps provider helps the customer create an AIOps strategy before implementing the tool. What’s the problem? What’s the budget? How can the technology solve the issue at hand?

In many cases, AIOps vendors help clients orchestrate the technology to solve their particular problems. They may help tech teams understand the difference between good and bad data, choose the correct data and set expectations.

If implemented correctly, AIOps can help DevOps and SRE teams resolve incidents confidently, saving time for more high-value tasks. If implementation falters, well, people will find truth behind the old adage of “garbage in, garbage out.”

Successful AI-driven outcomes are often associated with enterprise-wide, multi-billion dollar projects and big data. The reality is that most modern businesses produce plenty of data to reap the benefits of AIOps adoption. And the companies themselves don’t have to be particularly large either. As long as the AIOps tool has access to quality data, the amount of data required is very low.

For example, one of the most active AIOps customers I’ve worked with also has one of the smallest tech teams. To be clear, this client has applied modern DevOps practices to eliminate toil by automating every manual process possible and has thus maintained a svelte IT department. But as a result, the fully implemented AIOps solution does a lot of heavy lifting behind the scenes, with astounding success.

Google’s SRE Handbook describes how to improve data quality and which data is most important to monitoring.