At Dynatrace Perform 2022, the AIOps track will explore how to use AIOps to manage the complexity of multicloud environments.
|This article includes key takeaways on AIOps strategy:
Today’s cloud complexity wall has become overwhelming for IT departments.
With the proliferation of cloud services, including containers and microservices, IT teams have a distributed, dynamic and ephemeral landscape to manage. As a result, they need data, intelligence, and automation to manage dynamic, multicloud environments. The goal is to identify issues before they affect system performance or user experience.
But that’s a tall order when organizations have assembled a web of interconnected and sometimes ephemeral IT systems. It can be difficult to peer into these systems, to successfully identify the root cause of performance and security problems, and to automate the resolution.
AIOps strategy at the core of multicloud observability and management
Today, operations and development teams need AI to get to the heart of problems and proactively address them. With real-time data that pinpoints the precise source of issues, teams don’t have to wade through a sea of alerts that create data overload.
Causal AI is also more precise and efficient. With real-time causal AI, organizations can identify the root cause of issues without having to train their data models up front. That’s critical to circumvent the time-consuming process of training algorithms to understand system behavior. Causal AI is particularly important in dynamic cloud environments where components are constantly changing.
Dynatrace’s approach to AIOps puts deterministic AI at the core of observability, delivering a platform that automatically identifies root causes. Rather than having to learn or feed data to it, this integrated approach to AIOps enables teams to automate remediation.
Exploring keys to a better AIOps strategy at Perform 2022
At Dynatrace Perform 2022, the conference theme is “Empowering game changers.” We will feature several breakout tracks to help organizations meet the challenges of today’s complex, multicloud environments.
In our AIOps track, we explore how teams can use an AIOps platform to drive intelligence and automation throughout the enterprise. This approach with AI at its core accelerates software innovation, drives efficiency, and achieves better business outcomes. We’ll explore the following aspects of successful AIOps approaches:
- Integrated AI. An AI engine needs to be integrated into the cloud observability platform, not bolted on. Native AI is the only way to achieve automation across the SDLC for faster, more efficient, and proactive approaches.
- Explainable AI. Algorithms often suffer from the “black-box problem” in which the assumptions built into algorithms are opaque or unclear. With AI-enabled observability, teams can leverage transparent and explainable intelligence to build trust, eliminate silos, and drive better collaboration across the business.
- Real-time AI. Traditional machine learning approaches involve time-consuming training of algorithms. In contrast, AI-enabled observability provides real-time, continuous insights and root-cause analysis. This immediacy helps avoid costly, ineffective responses to issues in production or during software development.
- Causal AI. Data collected via traditional cloud monitoring tools provide correlation-based insights, which require interpretation that often amounts to guessing. Deterministic AI that is integrated into observability solutions provides causation-driven analysis of issues to deliver answers instead of simple correlations.
Dynatrace customers enlist an automated and intelligent AIOps strategy
Here are some ways that Dynatrace helps its customers achieve their goals with an automatic and intelligent AIOps strategy.
Dynatrace helps keep the semiconductor supply chain up and running
Today, the supply chain has become a bellwether for the state of the global economy.
As consumers seek goods that are out of stock, suppliers have struggled to overcome bottlenecks that have defined the supply chain for the better part of 18 months.
One key example of supply chain dysfunction is the semiconductor shortage. These computer chips power products from cars and fridges to renewable energy sources and electronics.
A major chip manufacturer purchased a series of fabrication plants to boost chip production and address shortages in the auto industry. This company did not have a common platform to gain insight into performance and security issues in their distributed “fabs”. This left them with an inefficient patchwork of cloud monitoring tools.
The manufacturer is now standardizing on the Dynatrace platform so engineers can speak a common language and view system events from a single source of truth. With Dynatrace, the chip manufacturer is keeping its supply chain up and running with better monitoring insights and automated remediation capabilities.
By adopting Dynatrace, the chip manufacturer also laid the foundation for its future AIOps strategy. IT teams have begun to tier incidents that they can address with automation and those that require human intervention. With Dynatrace AIOps, the chip manufacturer can take a more strategic approach.
Delivering improved health experiences with Dynatrace AIOps
Vitality is a U.K.-based health and life insurance provider whose mission is to promote a healthy lifestyle among its members. Their platform rewards members who pursue a healthy lifestyle. For example, members can earn points for walking 10,000 steps, eating healthily, and other lifestyle choices. They can exchange these points for movie tickets, gift cards, and other perks.
To ensure customer satisfaction and a seamless end-user experience, Vitality enlists Dynatrace AIOps to identify and proactively address customer usability issues. If a benefits system slows to a crawl or is in danger of failing, Dynatrace immediately pinpoints the root cause and can automatically fix the problem before users are affected.