The global AIOps market is expected to surpass $172 billion by 2032, an indication of increasing demand and the ready availability of enterprise-grade systems and platforms. However, as enterprises in West Africa embrace cloud computing, many are warming up to embark on their AIOps journeys. Irrespective of the sector or industry, big data analytics, AI, and ML technologies for IT operations, therefore, represent huge value in the automation and streamlining of management services and workflows. This illustrates the level and nature of demand increasing and enterprise-grade systems and platforms becoming available across the board, with the global AIOps market likely to surpass $172 billion by 2032.
The acceleration of AI across the IT infrastructure is powered by the need to simplify processes—doing more with less—and a desire to stay focused on core business transformation efforts, not to mention its potential for improved security in IT. However, to integrate AIOps successfully, organizations need to understand proper requirements and meet challenges. All of this begins with data.
For AIOps to work, an enterprise needs data—particularly the data it generated via business operation. This ranges from data on organizational network traffic, processing use, uptime and downtime, application logs and errors, to security data of authentication attempts and firewall notifications.
The magic word here is observation.
Operational observability has become a more and more vital part of IT. This urgency increases with increasing complexity in software and systems, and organisations are using microservices increasingly. Also, distributed architecture—for example, because an organisation uses multiple cloud environments—pushes up the need of an organisation to ‘build in’ more observation and monitoring possibilities.
Today, many enterprise IT platforms are already fitted with, or at least can link to, observation features. Once you have clearly defined the operational health of your organisation then you can start to factor in AI and ML technologies into projects.
What prevents successful AIOps integration?
One of the huge challenges organisations are going to face is that AIOps binds itself to the availability of enough data for processing. When there is a shortage of it, the effectiveness of even the most powerful AIOps tools is blunted. There were already data siloes, potentially pricey and time-consuming, but now they are capable of entirely knocking off any submitted plans on AIOps.
Other clues can slow down the integration process, however. Enterprises typically have sufficient data, but often it is not of the highest quality. Some common problems include infrequent or inconsistent reporting frequencies and inconsistent naming policies. Data also often has no relative value—often referred to as ‘noisy data’—which will further compromise data sets. As such, the phrase ‘garbage in, garbage out’ is very appropriate to describe the need for ensuring quality data.
This perhaps might be the most important thing: for organisations to really understand the business problems they are trying to solve using AIOps. By working from top-down with use cases, and from bottom-up with improvements in processes, organisations stand a chance of maximizing their algorithms and AI-enabled features for impact.
Use cases and working with the cloud
Abnormalities are thus detected with AIOps in voluminous data, able to trace back roots, for example, to excessive CPU use in a cloud infrastructure. In the second place, AIOps enables automation in incident management by automatically triggering incident response procedures for appropriate placement. Security can be enhanced by analysis of security logs and events, honeypot/trap sensor alerts, or suspicious network traffic patterns that raise red flags for instant action.
An excellent use case for AIOps, particularly within a market like West Africa, where companies are quickly embracing cloud technologies in the same pursuits of achieving greater business efficiencies, is data-driven cloud resource and spend decision-making. With the need to be ‘always on’ and to have guaranteed application performance, organisations can end up overprovisioning and spending money on resources they don’t need. AI empowers organizations to know precisely where, when, and how resources are being deployed and how cost can safely be balanced against performance.
Efficiency and agility will be hallmarks of companies’ competitiveness in the markets in which they operate in the future. Whereas AI would show up in a number of different ways in West Africa—most development coming from the strategies of private enterprise to public and non-profit organizations—the journey really begins with incremental introductions for many. AIOps cannot be integrated overnight. However, by working with trusted vendors and outlining key integration areas, companies can start experiencing what AI can do for their business.