Performance monitoring today epitomizes the “missing the forest in the trees” metaphor. A din of alerts assails operating managers struggling to extract the signal in the noise. The din devolves into deafening cacophony as data volumes from interconnected systems explode. Post-Covid, with accelerated digital transformation across most domains and industries, finding relationships affecting performance in a flood of events, telemetry data from multiple domains is a forbidding challenge. Automation of data capture, analysis, and remedial action is inescapable for monitoring and controlling performance.
Planning data curation for a big picture view
Artificial intelligence and machine learning are inseparable from performance monitoring and control in today’s environment. Patterns from data analytics should inform operational upgrades to achieve performance goals. Comprehensive planning to curate data to track, monitor, and control performance variables is of paramount importance. AI and ML are not magic wands—they founder unless error-free data for all determinants of performance is aggregated in real-time.
Controlling systems with ML-Analyzed data
AIOps use AI and ML algorithms to parse the data influencing performance and decipher trends and detect anomalies to uncover actionable insights for course corrections and automatically implement them.
The mathematical model is pivotal—it embeds the formulas capturing the interdependencies affecting expected outcomes, tracking shortfalls, and suggests courses for actions that will help realize the desired results and confirm when they have been achieved. The data and parameters can be adjusted during the training period of the mathematical algorithm to ensure that it learns to identify trends that point to actions. These trends typically fall into one of the following categories:
- Positive direction – data movement validates the results and actions desired for achieving the goals of the product or system, and these typically do not imply the need to take action.
- Neutral direction – actions that currently do not impact the system or product positively or negatively. However, the adjustment of these parameters can lead to either positive or negative trends, and AI/ML is designed to determine how best to achieve the end goal.
- Negative direction – data movement that indicates corrective action is needed, where AI/ML can explore how to recover from the negative state of affairs.
AIOps sets out to automate and optimize the use of physical resources within the bounds of their costs and availability to maximize positive outcomes such as service revenue. Teams can tweak the operating system benefitting from the calibration of parameters based on learning from data, achieving desired goals and shortening the time to success.
Visualize the outcomes of the data
Reports and dashboards quickly and concisely present the status of outcomes realized by the system, along with projected trends using the current parameters. The pictorial representation of the results helps ops leaders and decision-makers instantly recognize and understand the current status and look for opportunities to keep the systems operating at peak performance and achieve the program’s goals.
Success is here!
AIOps platforms are here and are driving results by tracking and monitoring operations data to identify and act automatically on issues causing lapses in performance or catastrophic results. Business performance is achieved on a foundation of data quality, formulas, and processes for automatics corrections. It takes meticulous planning to develop an AIOps platform for improving business performance.
