Crystal Ball AIOPs: How Historical Data Helps Companies Predict the Future

How did we get here? Just a few decades ago, AI was mostly the stuff of sci-fi movies. Today, artificial intelligence for IT operations (AIOps) is emerging as the key to unlocking IT management by using AI to predict the future… basically. As Alan Turing said, “We are always faced with the need to improve and innovate.” But to build automated processes that can effectively predict patterns and trends, companies need to expand their data fabric to include not just real-time data, but also trustworthy historical insights.

Historical Data in Predictive Analytics
In our last blog post in this series, we talked about real-time network insights as the backbone of effective AIOps. Real-time data provides immediate visibility for identifying and addressing performance and cybersecurity issues as they arise. In contrast, historical data offers the context needed to understand trends and patterns for predictive analytics and automation.

Historical insights, including past performance metrics, logs, and incidents—as well as real-time and historical data from processes such as deep packet inspection (DPI)—can provide a comprehensive view of system behavior over time. Collecting and integrating historical data is crucial for building accurate predictive models and strengthening an organization’s security posture. This data can be stored in a central repository, such as a data lake, and easily accessed, processed, and analyzed alongside real-time data streams or sent directly to AIOps platforms.

Some reasons why historical data is so important:

• Enhanced insights: Historical data complements real-time data by providing a deep understanding of trends and patterns in network and application performance.
• Comprehensive analysis: Header data, payload data, and metadata from DPI, along with historical insights from other observability data—such as metrics, logs, traces, and events—provide highly granular views of system behavior and can detect cybersecurity threats.
• Proactive management: Predictive models built on historical data enable IT teams to proactively address emerging and potential issues, reducing risks and preventing downtime before it affects operations.

So, the integration of historical and real-time data sets the stage for proactive troubleshooting and predicting likely events. And this goes hand in hand with the Holy Grail of AIOps: automation.

Transforming AIOps with AI-Driven Automation

Think of automation as the catalyst that makes companies more efficient and responsive. Automation enhances AIOps capabilities by streamlining processes and enabling faster responses to incidents, including security events. Predictive analytics, a product of machine learning (ML), provides valuable insights that drive automated workflows. For instance, neural networks can learn from historical data to identify complex patterns and make accurate predictions about future events. When a potential issue or security threat is detected, automated systems can trigger predefined responses, such as reallocating resources, restarting services, or alerting relevant personnel. By automating tasks and responses, organizations can minimize the impact of incidents and ensure swift resolution.

The benefits of long-term automation in AIOps are significant. Automated responses not only reduce downtime but also improve efficiency by freeing up IT teams to focus on more complex tasks. This results in a more resilient IT environment with minimal disruption to business operations, which improves employee productivity, protects user experiences and can expedite positive business outcomes.

But for all those things to be possible, the data must be of the highest quality.

NETSCOUT Makes the Crystal Ball Crystal Clear

By providing deep visibility into network performance, application behavior, and user activity, NETSCOUT makes data both actionable and future-enabled, unlocking predictive analytics and AI-driven automation while enhancing security and threat detection.

As Alan Turing famously said, “The best way to predict the future is to invent it.” So, the team at NETSCOUT went ahead and did just that!

NETSCOUT’s groundbreaking Omnis AI Insights solution—featuring our Omnis AI Streamer and Omnis AI Sensor—helps AIOps professionals build more resilient, efficient, secure, automated, and forward-thinking IT operations, all powered by the highest-quality curated data available.

• DPI and observability: NETSCOUT provides granular, low-noise telemetry insights, including operational and conversational data, creating a comprehensive view of IT environments regardless of scale.
• Relevance: The precision of NETSCOUT’s data is vital for developing robust ML algorithms that use historical data to make accurate performance forecasts, classify information, cluster data points, and reduce dimensionality.
• Accuracy: By collecting data at its source and curating it to ensure the highest quality, NETSCOUT minimizes false positives, resulting in reliable ML forecasts and effective preemptive actions through clean, relevant data, advanced feature engineering, and precise model tuning beyond human capability at scale.
• Automation: NETSCOUT’s data value is trusted by AIOps professionals building predictive analytics and automating AI-driven processes. This data supports ML models that can automate IT responses, thereby improving operational efficiency, reducing threats, and increasing uptime.
• Partners: NETSCOUT's partner network consists of globally renowned top-tier organizations known for their expertise and experience in analyzing and visualizing large machine-generated datasets in real time and enabling AI-powered service and cybersecurity processes.

Now, in a very real way, NETSCOUT is not only helping AIOps professionals predict the future; we’re actively helping them shape it.

Learn more about our Omnis AI Insights solution for AIOps and how NETSCOUT data is transforming artificial intelligence.