Gartner Report Reveals Why Your APM Strategy Needs AIOps

December 05 2018

Access Gartner’s exclusive research report to find out how AIOps—when used together with APM—plays a pivotal role in driving better business outcomes.

Is application performance monitoring (APM) without artificial intelligence a waste of resources?

It turns out, the answer may be yes. Gartner’s newly released report, Artificial Intelligence for IT Operations Delivers Improved Business Outcomes, reveals that using artificial intelligence for IT operations (AIOps) in tandem with APM might be the key to optimizing business performance.

So why exactly do AIOps and APM make such a powerful pair and, perhaps more importantly, how can you start applying an AIOps mindset to APM in your own organization?

AIOps and APM: Great Alone, Better Together

Application performance monitoring (APM) is the key to proactively diagnosing and fixing performance issues, but a new study from Gartner reveals the many incremental benefits IT teams can derive from leveraging AIOps in conjunction with APM. Adding artificial intelligence into the mix gives IT and business leaders visibility into the right data at the right time to make decisions that maximize business impact. The power of AI in relation to APM is that most APM environments generate massive quantities of data that humans can’t possibly parse and derive meaning from fast enough to make it useful. Through machine learning, we can ingest that data, and over time, develop intelligence around what matters within an application ecosystem. As Gartner reveals, “AIOps with APM can deliver the actionable insight needed to achieve business outcomes such as improved revenue, cost and risk.”

Consider the process of assessing customer satisfaction based on customer sentiment data and related service desk data. Without using both AIOps and APM, infrastructure and operations (I&O) leaders might come to the conclusion that customers are delighted based on fast page load times. But by using AI to also ingest and analyze data from order management and customer service applications, I&O leaders can find correlations between IT metrics and business data such as revenue or customer retention. This level of insight offered by AIOps allows business leaders to make informed decisions and prioritize actions that will quickly improve customer satisfaction and, ultimately, the bottom line.

Applying AIOps to APM

Here are three ways I&O leaders can leverage AIOps together with APM to achieve incremental benefits—the step-by-step technical strategies for which can be found in Gartner’s new report:

1. Map application performance metrics to business objectives by using AIOps to detect unsuspected dependencies.

AIOps can be used to help measure IT’s activities in terms of benefits to the business—such as an increase in orders or improved customer satisfaction. To do this, I&O leaders should start by collaborating with key business stakeholders to identify the mission-critical priorities of the business relative to applications. Next, acquire the data supporting the measurement of these selected objectives by capturing the flow of business transactions such as orders, registrations and renewals. After inspecting their payloads, you can then use AIOps algorithms to detect patterns or clusters in the combined business and IT data, infer relationships, and determine causality.

2. Expand the ability to support prediction by using AIOps to forecast high probability future problems.

“AIOps provides insight into future events using its ability to extrapolate what is likely to happen next, enabling I&O leaders to take action in order to prevent impact,” Gartner states. As such, I&O leaders should take advantage of the many ways machine learning algorithms can provide value: predicting trends, detecting anomalies, determining causality and classifying data. Use AIOps algorithms to predict future values of time-series data such as end-user response time, engage in root-cause analysis of predicted issues to determine the true fault, and take preventative measures to prevent the impact of predicted problems.

3. Improve business outcomes by applying AIOps to customer and transaction data.

The pattern recognition, advanced analytics and machine learning capabilities of an AIOps solution can extend APM’s historical insight into application availability and performance to provide business impact. By using AIOps’ machine learning capabilities—including anomaly detection, classification, clustering and extrapolation—you can analyze behavior (e.g., customer actions during the order process) and relate that behavior to events afflicting the underlying IT infrastructure. Use the clustering and extrapolation algorithms contained within AIOps to detect unexpected patterns or groupings in your data and predict future outcomes. From there, you can correlate IT problems with changes in business metrics and establish how changes in application performance and availability impact customer sentiment.

Augmenting APM with Artificial Intelligence

The verdict is in and the evidence is compelling: AIOps is the key to maximizing the business impact of your APM investment.

Using AIOps together with APM can help I&O leaders more effectively align IT and business objectives, expand the ability to support prediction, and improve business performance. Leveraging AIOps can take your APM strategy to the next level, giving IT and business leaders the deep insight they need to make decisions that increase revenue, reduce costs, and lower risk.

Application performance management is already a critical tool that belongs in every IT leader’s toolbox, and AIOps is a game-changing technology set to transform APM and IT operations in a major way. As one analyst recently wrote for Forbes, “AIOps is gearing up to be the next big thing in IT management…When the power of AI is applied to operations, it will redefine the way infrastructure is managed.” In today’s competitive business landscape, companies need an edge to survive and thrive—and it seems APM with AIOps might just be the golden ticket.

Access the Full Research

For more exclusive insights into Gartner’s research on why—and how—you should apply AIOps to APM, download the report Artificial Intelligence for IT Operations Delivers Improved Business Outcomes.

Gartner, Artificial Intelligence for IT Operations Delivers Improved Business Outcomes, Charley Rich, 12 June 2018

Marco Coulter
As the Technical Evangelist for Analytics and Business Intelligence at AppDynamics, Marco Coulter is passionate about the experience humans have when interacting with technology. A former startup CTO, Marco has progressed from operator to leadership roles at CSC, CA Technologies, and more recently 451 Research, where he led the Business Intelligence team. He earned the nickname "the tech-whisperer" for his skills in translating business drivers for a technical audience and technical concepts for business leaders. When taking the rare break from technology, Marco can be found harvesting fresh vegetables from his garden.

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