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With companies adopting cloud computing, thriving device diversity, and increasingly vital data analytics, IT environments are becoming more complex. Organizations often prioritize agility, security, and adaptability to sustain effective operations in this evolving technological landscape.
When systems become more complex, so do their volumes of monitoring and observability data. Yet, this data isn’t useful without the ability to extract timely, actionable insights. As the data volume swells, finding these insights becomes more challenging as teams need to:
Artificial intelligence (AI) and machine learning (ML) capabilities can help analyze this massive data. Embracing this approach enhances the value of monitoring your systems while accelerating your time to insight—the time necessary to determine what action to take on information. Additionally, generative AI aids in communicating these findings, making it straightforward for IT teams and other stakeholders to understand and act on these complex data insights.
Let’s explore how to progress from basic system monitoring to deriving and communicating actionable insights via AI.
There was a time when system monitoring meant detecting an anomaly—an event that didn’t fit the usual pattern, such as a sudden stop or an unusual spike in traffic. Initially, IT teams would poke around at this juncture to figure out what went wrong. However, system monitoring evolved as AI provided sophisticated analytics. Analytics became more personalized, producing outputs based on individuals’ preferences and behaviors. This information allowed IT and marketing teams to gauge how customers interact with the application.
Then came predictive analytics based on historical data, allowing IT teams to anticipate potential issues. Armed with this information, the teams knew when to boot up some extra resources for an expected increase in traffic or plan to replace a drive next month based on its expected failure.
Today, generative and analytical AI help system monitoring reach new heights. Instead of the IT team or a manager poring over data to detect patterns, these AI helpers pick up on the patterns and communicate their findings in a more intuitive and accessible manner. AI tools can create new information, like dashboards, reports, or synthetic data.
These tools use generative AI to explain their analysis and insights in human-like language, making this information accessible to all departments. While less technical managers find it more straightforward to understand the insights, IT professionals enjoy quickly getting the necessary information to act immediately.
Modern AI tools quickly organize and analyze data while ensuring it’s factual and valid. Then, they pass the summary or relevant information to human team members. AI can help create easy-to-read reports, complete with visuals to highlight insights.
Users promptly receive the information required for them to take action, focusing on implementing solutions to fix issues or build capacity where needed. Additionally, they don’t need to wait for data analysts to provide this data. AI can support data democratization so all coworkers can quickly analyze massive data sets to glean relevant insights.
AI and ML are revolutionizing observability by managing the grunt work of aggregating data and producing insights.
AI is adept at pattern recognition. It can easily collect and analyze vast amounts of data and learn from historical material. AI can dig deep to trace one data point and then expand to consider the broader picture. When your team looks at all that data, they may not realize that performance drops when some unusual combination of circumstances happens, but a good AI tool finds the pattern, informs the team, and prompts them to take action.
AI also excels at anomaly detection. As AI digs deep into the data across complex systems and microservices, it picks up on the system and user behavior patterns. When it notices events that don’t fit the usual routine, the AI can alert team members while investigating the anomaly’s cause and recommending actions that could help.
Furthermore, AI supports predictive analytics. As AI learns the behavior of individual components, individual users, customer segments, and the system, it begins to predict future behavior with increasing accuracy. For example, this approach allows AI to predict when there may be a spike in server traffic, prompting the team to add more resources. It can also forecast what else may interest customers based on previous views or purchases. Consequently, it better targets marketing initiatives to boost sales.
Together, AI capabilities enable IT teams to identify issues before they escalate, proactively optimize system performance and resource use, and personalize user experiences. These abilities help teams save time while providing smooth customer experiences.
Anomaly detection uses historical data to get a baseline of what’s permitted and then flags any data points outside the normal range as anomalies. This problem is a natural fit for AI. To showcase this, let’s review two anomaly detection algorithms, Robust Principal Component Analysis (RPCA) and Matrix Sketching, to see how they apply to different scenarios.
RPCA breaks down data matrices into two fundamental components: a low-rank matrix and a sparse matrix. By separating the data into the low-rank matrix, which holds the main features of the dataset, and the sparse matrix, which holds anomalies or noise, RPCA can enhance the accuracy of anomaly detection tasks. It can even handle seasonality in a data set, making it better suited for time series data—especially when a high level of accuracy is required.
On the other hand, Matrix Sketching offers a more streamlined approach to anomaly detection. It condenses multiple attributes into a singular model, reducing the computational burden associated with treating each attribute independently. This approach is better suited to areas that require a more scalable and resource-efficient anomaly detection solution, such as server or cloud infrastructure monitoring.
RPCA and Matrix Sketching both showcase the improvements available by using AI algorithms to identify anomalies. These algorithms can help you mitigate risks that stem from anomalous behavior and proactively respond to potential issues.
Time to insight is the time from gathering data to gaining actionable insight. In software systems, it’s the time between monitoring the application or infrastructure’s performance, health, and behavior and the system administrator gaining meaningful insight they can use to address issues.
Essentially, the time to insight metric measures the team’s ability to detect and address issues promptly. It’s vital to their efforts to minimize downtime, improve the system’s reliability, and enhance overall operational efficiency.
AI tools help boost time to insight, quickly getting critical information to the decision-makers. The tools dig through massive amounts of information, recognize patterns, and glean insights. They enable organizations to respond swiftly to changes in system behavior, user feedback, and emerging security threats.
The following examples illustrate how AI can accelerate time to insight in real-world situations.
Say one of your application’s critical components suddenly experiences increased error rates. As AI performs real-time monitoring, it can detect this spike, identify the affected component, assess a possible root cause, and suggest a targeted fix.
This quick analysis enables the IT team to take swift action, minimizing downtime and ensuring customers experience uninterrupted service.
Let’s say some areas of your software have low engagement while others experience bottlenecks. You can use AI to detect this behavior and promptly send you feedback.
With this data, you can dedicate more resources to the popular features while deprioritizing those with low engagement. You can also add more features similar to the popular ones while employing AI insights to improve the areas with negative feedback.
Your user satisfaction and loyalty increase as your software improves based on actual user data. Consequently, you’ll retain your existing users while growing your user base further.
With the surging frequency of cybersecurity attacks, IT teams should employ every means to detect unusual patterns and suspicious activities swiftly. Fortunately, AI excels at this pattern recognition.
Suppose your AI tool alerts you to unexpected access patterns or a sudden surge in failed login attempts. In that case, your security team can quickly implement security patches, update configurations, or isolate the affected systems to prevent breaches before they escalate.
If your AI-based monitoring system detects a gradual increase in CPU or memory usage, it alerts and provides early insights into potential performance issues.
The AI or your team can optimize resource allocation, scale your infrastructure, and identify and fix inefficient code. These actions ensure your system can perform optimally, providing the best user experience without resource wastage.
Releasing a new feature involves gauging its effectiveness. If it’s not meeting expectations or causing unexpected issues, you can bring in AI to assist with detecting issues and making adjustments.
Your team can quickly make adjustments or perform rollbacks, preventing any negative impact on user experiences.
AI excels at detecting trends so you understand resource trends and usage patterns. These insights boost your capacity planning as you better understand what to expect.
For example, if you’re expecting a sudden surge in traffic, your team can scale resources to handle this increased demand, ensuring a seamless user experience even during a peak period.
AI can monitor and alert you to sudden system outages or degradation, investigate possible root causes, and suggest possible fixes.
Using AI can help your team expedite incident responses. It can help you quickly identify affected components, communicate with stakeholders, and implement mitigation strategies, minimizing the impact on users and business operations.
When you’re subject to strict regulatory requirements, faster insights into your system activities and data access help you maintain compliance.
In the case of an audit, AI can help by rapidly retrieving relevant information so you can respond quickly to compliance queries and reduce the risk of penalties.
As much as machine learning and other types of AI have transformed software delivery and system maintenance, generative AI has transformed how these tools communicate insights.
Generative AI enables AI systems to understand queries and generate reports, summaries, and alerts in natural language. This approach makes the information straightforward for non-technical stakeholders to seek and understand.
This enhanced communication enables cross-team collaboration as various business units access the same information. Consequently, this data democratization enables faster decision-making. The human-like language and easy-to-understand reports also increase transparency in IT operations.
AI relieves your team from the old ways of monitoring systems and analyzing data. It quickly performs the work of digging through all this information and gleaning insights, providing your team with the necessary information to take action and freeing them to work on new features and projects that excite them.
These AI tools accelerate your team’s time to insight while clearly communicating these insights through generative AI. Even as modern IT environments become more complex, AI adeptly navigates these environments to ensure functionality, make improvements, and, ultimately, maintain your competitive advantage.
With AI permeating every industry, it’s time for IT leaders and professionals to embrace AI advancements in their monitoring strategies. It’s a strategic move toward building more agile, proactive, and informed IT operations. Sign up for a free 30-day trial of Site24x7 to experience AI-powered insights for yourself.
Write for Site24x7 is a special writing program that supports writers who create content for Site24x7 “Learn” portal. Get paid for your writing.
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