The AIOps market is flourishing and the new year is coming up, so let’s take a look at the top 3 trends to watch out for in 2022.
AIOps, or at least the term “AIOps,” is just four years old this year. Gartner coined the term in 2017 as a way to describe artificial intelligence (AI), machine learning (ML) and related technologies designed explicitly for IT Operations activities and tasks. By using big data, AIOps helps DevOps and SRE teams maintain system assurance by quickly identifying incidents and providing actionable insights into ways to fix these incidents.
You’ve come a long way, baby
The global AIOps market is flourishing, fueled by a growing digital economy with an insatiable appetite for bigger and better technologies and zero tolerance for downtime. Trying to keep pace with demand, IT systems have grown more interconnected and complex. And now these systems are frankly too unwieldy for human operators and legacy tools to monitor. Enter AIOps. The solution streamlines the monitoring of operational data from applications, cloud services, networks, infrastructures, and allows for automation. Humans can now focus on difficult problems that can not easily be automated.
Although no one even talked about AIOps until relatively recently, the market is projected to expand from $13.5 billion in 2020 to $40.9 billion by 2026 according to Mordor Intelligence.
Here are the trends that will propel the AIOps space into 2022 and beyond.
AIOps trend #1: enable remote and hybrid work
Pandemic-induced office closures and work-from-home policies increased digital traffic and complicated data collection for IT teams. Businesses supporting remote work sent employees home with new hardware and software, resulting in more data traffic. And IT teams, already contending with increased data production, also had to monitor streams of data with different properties, caused by information flowing in from various remote office locations.
An AIOps platform helps IT teams handle increased and increasingly dissimilar data caused by the globe’s permanent shift to remote and hybrid work models. AIOps deploys intelligent algorithms that ingest large volumes of data with varying properties and from disparate sources and automates its analysis. The AIOps solution looks at the aggregated data to detect patterns and predict problems before they raise their ugly heads and cause disruption to employee productivity. And, if there is a service-impacting incident, AIOps helps DevOps and SRE teams quickly identify the root cause and determine a fix to reduce mean time to remediation (MTTR).
AIOps trend #2: automating cybersecurity
By the time the year is through, cybercrime’s global damages will likely double from an annual $3 trillion in 2015 to a whopping $6 trillion in 2021. Unfortunately, cybercrime has grown into a booming business, and fighting cyberattacks will continue to be a top priority for modern enterprises. Security teams need to constantly monitor for threats, vulnerabilities and disruptions to their complex system infrastructures.
AIOps, traditionally used by IT operations teams, will also help enterprise security operations teams maintain constant vigilance of their systems. AIOps uses intelligent algorithms to model the systems’ standard behavior patterns and set baselines for system performance. These platforms unlock the ability to proactively detect a cyberattack by identifying deviations in real-time and determining if a performance issue is due to a cyberattack rather than another IT issue. In the case of an attack, the system can kick off a series of automated defensive tactics like shutting down a server, closing access to a storage system or blocking an IP address.
AIOps trend #3: decrease MTTR with observability
With high-profile service failures like Facebook’s costly October outage, service assurance issues are keeping business leaders up at night. And who can blame them with customers, corporate reputations and sales on the line? While technology can’t yet provide 100% protection against service failures, a platform that combines the alert data associated with AIOps and the telemetry data associated with observability can mitigate the damage. When issues occur and every moment counts, AIOps helps SRE and DevOps teams quickly detect the incident and provide actionable insights to help resolve it.
Observability data offers early indicators, deep diagnosis and fast detection, while AIOps data provides high-level, comprehensive views of whole tech stacks. A single unified cloud monitoring solution that links both kinds of data provides a complete picture of and deep insights into complex systems for IT teams. In one system of engagement, these teams can see into the entire tech stack and provide quick, accurate problem resolution for better, faster architectures.
You’ve heard it here first, folks: watch for AIOps to create clarity from chaos in 2022. This tech, although still relatively new on the scene, will be critical to tackling today’s most pressing digital business challenges. AIOps will provide reliable systems for remote employees to work, build defenses against cyber attacks and increase the uptime for applications and digital services.
About the author