Intelligent decisions, intelligent systems: The ins and outs of AIOps
As enterprises in West Africa embrace cloud computing, many are warming up to embark on their AIOps journeys as well. The use of big data analytics, AI and ML technologies for IT operations to automate and streamline service management and workflows represents significant value regardless of sector or industry. The global AIOps market is forecast to be worth more than $172 billion by 2032, which demonstrates the growing level of demand and the availability of enterprise-grade systems and platforms.
Leveraging AI across IT infrastructure is a result of the need to simplify processes, doing more with less, as well as the desire to focus on core business transformation efforts. Not to mention its potential for enhanced IT security. However, organisations need to have a proper understanding of the requirements of AIOps and the challenges they need to overcome for a successful integration.
It all begins with data
For AIOps to work, an enterprise needs data, specifically, the data it has generated via business operations. This includes everything from an organisation’s network traffic, processing use, uptime and downtime, application logs and errors, to security data such as authentication attempts and firewall notifications.
The keyword here is observation. Operational observability has become a critical component of IT. This importance grows as software and systems become more complex and organisations make increasing use of microservices. A distributed architecture, such as the use of multiple cloud environments, also increases an organisation’s need to enhance its observation and monitoring capabilities.
Today, many enterprise IT platforms come equipped with, or at least connect to, observation features. Once your organisation’s operational health is clearly defined, you can start to incorporate AI and ML technologies into projects.
What holds back a successful AIOps integration?
AIOps is highly dependent on having enough data to process, which is one of the biggest challenges organisations face. This shortage can blunt the effectiveness of even the most powerful AIOps tools. Data siloes are already a potentially expensive and time-consuming problem for companies, and now they can completely derail any AIOps plans.
However, other challenges can slow down the integration process. While enterprises may have an adequate amount of data, that data may not be of the highest quality. Common problems can include insufficient or inconsistent reporting frequencies and inconsistent naming policies. Additionally, data may have no relative value (often referred to as ‘noisy data’), which compromises data sets. The phrase ‘garbage in, garbage out' is a very appropriate way to describe the importance of quality data.
Perhaps most important of all, organisations need to know what business problems they are trying to solve using AIOps. By thinking about use cases from the top down, while improving processes from the bottom up, organisations can maximise the impact of their algorithms and AI-enabled features.
Use cases and working with the cloud
Analysing large volumes of data, AIOps can identify abnormalities, such as excessive CPU usage in cloud infrastructure, and trace them back to their origins. AIOps can also automate incident management processes, such as automatically triggering incident response protocols and routing them to the appropriate teams. It can also be used to enhance security by analysing security logs and events, or unusual network traffic patterns, which can then be flagged for immediate attention.
A good use case for AIOps, especially in a market like West Africa where companies are adopting cloud technologies in the pursuit of achieving greater business efficiency, is data-driven cloud resource and spend decision-making. Organisations that want to be ‘always on’ and have guaranteed application performance can end up overprovisioning and spending money on resources they don’t need. With the help of AI, organisations can identify how and when resources are used, as well as safely balance cost and performance.
Going forward, efficiency and agility will define companies’ competitiveness and success in the markets they have a presence in. While AI will manifest in several different ways in West Africa, from the strategies of private enterprises to the efforts of public and non-profit organisations, for many the journey starts with incremental introductions. AIOps cannot be integrated overnight. But, by working with trusted vendors and outlying key integration areas, companies can start to experience what AI can do for their business.
Guest article by Oluwafiropo Tobi Ogundare, Territory Sales Lead for West Africa and Mauritius at Red Hat