The complexity of modern IT environments and the volume of data they produce have increased by orders of magnitude. According to predictions from UBS, the data universe will grow by more than a factor of 10 — reaching 660 zettabytes — from 2020 to 2030. This explosive growth exceeds the abilities of legacy event-management tools and human operators. AIOps augments human activities within IT operations using AI, data, and machine learning.

IT operations teams in the financial services industry face serious challenges. Customers expect a seamless experience across a complex landscape including online platforms, mobile devices, and ATMs. Competition is fierce. Technology evolution continually disrupts the marketplace. These factors create obstacles for the teams tasked with ensuring near-perfect service availability while continuing to innovate.

The ability to quickly and accurately identify the root cause of IT incidents is paramount. According to EMA Research, more than 80% of IT professionals said a solution that could generate an accurate summary of alerts and incidents, including the likely root cause, would be transformational or high value. Respondents noted that such a solution would reduce mean time to resolution (MTTR) by 10 to 30 minutes.

ITOps stands at a crossroads: Teams need help managing high volumes of alerts and coordinating between different tools and teams. They must balance the agility offered by cloud technologies and the stability provided by on-premises solutions. Success relies heavily on adaptability and clarity, requiring flexibility, with synchronized technology stacks for seamless IT operations. AIOps, a term coined by Gartner, provides a straightforward way to improve IT operations.

Imagine you’re in the middle of a critical project, and suddenly, your system crashes. Or perhaps it’s the middle of the night, and your server goes down, affecting countless users. While you can’t avoid all IT incidents, how you handle them can significantly reduce their impact. You know that proper IT incident management is critical — and that incidents can become costly.

As I’m training for a marathon, I’ve learned that establishing a strong aerobic foundation is the most important first step. Aerobic strength is crucial for improving the body’s efficiency in using oxygen, strengthening the heart and lungs, and increasing stamina and recovery ability.

Unplanned downtime can cost large enterprises almost $1.5 million per hour, according to a recent survey by Enterprise Management Associates. AIOps offers a solution. With an effective AIOps platform in place, you can decrease the frequency and cost of outages by 30% and reduce their duration to under an hour. AIOps platforms apply AI and machine learning to complex IT data to enhance and automate IT operations.

The common thread among teams responsible for maintaining IT services is their reliance on a deep understanding of the IT environment. Teams need access to all types of critical data to keep systems running. While it seems straightforward, ITOps teams face many challenges in locating, accessing, and synthesizing enough data to fully understand an incident’s cause and establish a remediation plan.

The digital landscape is evolving constantly — as is its complexity. Organizations need more efficient and effective ways to sort through high volumes of IT noise to identify the root cause of incidents. In a recent webinar with BigPanda CIO Jason Walker and Waste Management Principal Architect Udo Strick, Joe Connelly — director of monitoring, observability, and service reliability at Chipotle Mexican Grill — shared his perspective on.

IT teams need immediate and automatic access to machine data and institutional knowledge to move faster and make the right decisions. And they need context to identify incidents and understand how to resolve them. AIOps enables this by transforming noisy and fragmented operations data into actionable insights. This is the foundation of full-context operations. Full-context operations combines observability and other machine-generated data with historical, expert, and institutional knowledge.