Wednesday, 17 February 2021 12:35

AIOps predictions for 2021


The year 2020 will remain a landmark for accelerated digital transformation, but there is a need for more structure in the post-pandemic world.

The current digital landscape is diverse, distributed, and very dynamic, attributes of innovation and of chaos at the same time. Organizations strive with monitoring and have significant security concerns as they use more and more off-premise and SaaS solutions while dealing with a high volume of data. The solution that emerged so far is called AIOps since 2017 when Gartner separated it from ITOps and DevOps to mark the difference between using AI for IT operations.

AIOps — The Premise and the Promise

Right now, the AIOps market is booming, growing from $2.55 billion in 2018 to an estimated $11.02 billion by 2023, at a staggering CAGR of 34% per year, according to Markets and Market. This rate is powered by CIOs' rising interest in this technology, which promises to improve the performance of IT systems, perform root cause analysis on identified problems, and prevent outages by taking a predict and prevent approach. Here are the top 10 aspects that will make AIOps platforms a top market in 2021.

Top 10 AIOps Predictions 2021

1. Remote work boosts the need for AIOps platforms

Most organizations restructured their offices in a digital setup, and even when the pandemic will be over, some roles will remain fully remote. The advantage of most AIOps tools is that they are location agnostic. It gets the data streams; the AI and machine learning algorithms perform data analysis and output the necessary measures which impact productivity and security. This is necessary because when work was done in an office setup, any problem could have been handled on the spot by the IT team. In a distributed and heterogeneous environment, infrastructure monitoring requires more robust and automated tools.

2. More data types and data search tools

Modern IT systems rely on a wide variety of data types, including logs, transactions, and unstructured data such as CCTV recordings or client feedback. We can expect that the AIOps platforms will analyze these data sources together, looking for correlations leading to better event management. Future development will also strive to perform noise reduction between these numerous data sources to speed up problem-solving.

As most unstructured data sets are not fit for conventional search with SQL, AIOps technology requires dedicated tools, such as Elasticsearch, a distributed analytics and search engine well-fitted for text and other unstructured data.

3. More data generators

All user and IoT sensors are data generators, and AIOps experts know that troubleshooting future systems will require distributed power and better algorithms.  The multiple sources are impossible to analyze individually due to the volume and update speed. With the power of machine learning, this is reduced to checking a dashboard for escalated problems.

4. Better security

One of the major concerns of IT systems, security – with its very special problem: security alert triage, needs constant infrastructure monitoring. AI algorithms create models of historical data patterns and compare them with real-time records. The goal is to perform anomaly detection, a process that identifies threats by putting them in the context of regular operations. True AIOps can do even more and make decisions instead of the IT team in real-time by blocking attacks on the spot. The incident response time is the most critical variable when hackers are attacking a network.

5. Enhance cross-departments collaboration

There are countless business benefits in cross-department collaboration, but sometimes this is made difficult by siloed data or not having permissions to access it. Putting an AIOps system in place solves this problem by gathering data from all departments, performing noise reduction, and using predictive analysis. This way, every department has better uptime, and any IT problem identified in one area can be either stopped or patched so that it does not spread.

6. Decrease setup time

Currently, the installation and setup time for an AIOps platform is long, and Ops teams feel like the benefits don't outweigh the trouble. Still, as SaaS platforms become more widely available and AIOps technology is becoming mainstream, the tools will be more user-friendly and readily available. This will also mean less costly alternatives and broader adoption rates of the technology.

7. More AI-educated staff members

As AIOps platforms will become part of the regular business environment, more employees will be required to use these tools at a higher level. We can expect a rising demand on the job market for people who understand an AI-powered platform's requirements. We can also expect a surge in demand for senior analysts with good know-how of Big Data and machine learning. Cross-functional teams will also be more present, instead of just DevOps teams, since new challenges require insights from the engineers and the data owners and other departments.

8. AIOps will handle compliance and governance

International rules and regulations related to data handling and data protection (including, but not limited to GDPR) will become stricter. Organizations will be required to conduct digital business by having a clear overview of all data, where it is stored, and how it is processed. Infrastructure monitoring will require more than metrics and logs; it will also have a conduct and compliance component.

9. Observability will become a core feature of digital experiences

Until now, AI systems were more like operational black boxes. Companies are looking for an overview of all their moving parts: the digital infrastructure, productivity, client experience, and more. This enhances operations management and potentially identifies new market opportunities because it highlights how the business is performing.

10. Actionable insights

The investment in AIOps is justified by having access to actionable insights at the push of a button. No longer does the IT team need to look for the needle in the haystack. The tools do that automatically and either provide a recommendation or implement it without even asking, as part of the protocol it received during the training phase.

The AIOps market is still in its infancy, but all the necessary growth premises are already here. If we had to choose one reason for which AIOps is part of the future, we could say continuous and automated feedback loop.