Understanding Telemetry Data
Telemetry data refers to the continuous collection, transmission, and analysis of data from networks, including remote systems. This data encompasses metrics, events, logs, and traces (MELT), and packet data, forming a foundation for observability. By capturing insights from both system health and network activity, telemetry empowers organizations to monitor performance, optimize operations, detect security threats, and ensure digital resilience—the ability to withstand and recover from disruptions. Telemetry is essential for identifying and responding swiftly to anomalies, strengthening security posture, and minimizing downtime, especially when done in real time.
How Telemetry Works
Telemetry involves gathering data from various systems, including network traffic, application performance, and user interactions. Ideally, this data is processed and analyzed in real time, providing rapid insights to help identify and address issues as they occur. The data is also normalized and curated to deliver both real-time and historical insights, supporting operational resilience, performance optimization, and security management.
Types of Telemetry Data
Telemetry data is categorized into five key components, four of which are collectively known as MELT, and packets:
- Metrics: Quantitative measurements, such as system response times, CPU usage, and bandwidth consumption. Metrics can be available in real time to ensure continuous performance tracking.
- Events: Specific occurrences, like system errors, security breaches, or configuration changes. Real-time event monitoring enables quick detection and remediation of critical incidents, reducing risks to the organization’s security posture.
- Logs: Detailed records of system activities, providing context for events and aiding in troubleshooting and auditing. Real-time log monitoring accelerates root-cause analysis and strengthens security posture.
- Traces: Records tracking the flow of transactions or requests across distributed systems, which help identify bottlenecks or failures. Real-time tracing offers visibility into transaction paths, ensuring prompt detection and resolution of issues.
Packets: Deep Packet Inspection (DPI) greatly improves telemetry for greater depth and precision by analyzing packet data in real time. DPI examines raw packet data to generate KPIs and actionable insights, providing a more comprehensive understanding of network traffic and root causes of potential security threats, and performance issues. This in-depth analysis enhances the insights gained from telemetry data, leading to more accurate decision-making, better system management, and a stronger security posture.
AIOps: Benefits of Leveraging Telemetry for Automated IT Operations
Artificial Intelligence for IT Operations (AIOps) refers to machine learning and artificial intelligence applications that automate and enhance IT operations. By analyzing vast amounts of telemetry data in real time, AIOps platforms can detect anomalies, predict system failures, and even automate responses to common issues, reducing manual intervention and improving overall system management.
Telemetry is the backbone of AIOps, feeding real-time packets, metrics, events, logs, and traces into AI models. These models can analyze patterns, detect performance issues, and identify potential security threats before they escalate into larger problems. Telemetry data, especially when enriched by DPI, provides a more granular view of system activities, allowing AIOps to deliver more accurate and actionable insights.
AIOps enables organizations to:
- Improve Operational Efficiency: Automate repetitive tasks and streamline troubleshooting by using real-time telemetry to quickly identify and resolve issues.
- Enhance Security Posture: Detect and respond to security incidents in real time by leveraging AI-driven insights from telemetry and DPI-processed data.
- Support Proactive System Management: Predict future failures or performance issues using historical telemetry data to take preemptive action and avoid disruptions.
NETSCOUT’s solutions provide the AI-ready, curated telemetry that AIOps platforms need to deliver these benefits. By combining real-time and historical telemetry with advanced analytics, organizations can move toward a more resilient IT infrastructure and services that support faster detection, response, and recovery.
Cybersecurity and Telemetry
Telemetry plays a crucial role in enhancing cybersecurity, providing visibility into potential threats, and helping organizations quickly respond to emerging risks. By leveraging real-time telemetry data and DPI, organizations can monitor network traffic, detect anomalies, and address security concerns proactively.
Key benefits of telemetry in cybersecurity include:
- Threat Detection: Continuous monitoring of real-time telemetry data enables the detection of suspicious behavior, helping prevent cyberattacks.
- Incident Response: Real-time telemetry allows rapid detection and response to security breaches, minimizing damage and ensuring operational continuity.
- Edge Computing Security: Telemetry provides visibility into edge environments and remote sites, ensuring that security measures extend to distributed devices. DPI enhances this by offering granular insights into traffic at the edge, protecting against vulnerabilities in decentralized architectures.
- Regulatory Compliance: Telemetry data supports auditing and compliance efforts by providing detailed logs of system activity, helping organizations meet stringent security and privacy regulations.
What is Network Telemetry?
Network Telemetry focuses on capturing real-time data from traffic traversing the network. DPI adds granular insights into information like traffic flows, bandwidth usage, errors, protocols, and potential security threats, strengthening digital resilience by offering deep visibility into network performance and enhancing security posture.
Improving Business Outcomes Through Improved Telemetry
Businesses that integrate telemetry data of the highest quality into their data foundation can achieve better business outcomes through:
- Increased Operational Uptime: Real-time monitoring ensures that issues are quickly identified and resolved, reducing downtime and improving service availability.
- Proactive Threat Mitigation: Telemetry data, enriched by DPI, allows organizations to detect potential threats before they escalate, maintaining business continuity and protecting critical assets.
- Scalability: With real-time insights into system performance and user demand, businesses need to be able to scale their operations efficiently, whether they are expanding to the cloud or edge environments or deploying new services. NETSCOUT uniquely offers the scalability needed to handle the most complex networks in the world.
- Edge Computing for Business Efficiency: Digital resilience extends to edge computing environments, where telemetry and DPI provide granular insights into distributed systems, enabling rapid detection and resolution of issues on the network edge. This improves both security and operational performance at the edge.
Faster Decision-Making: Historical and real-time telemetry data enable better-informed business decisions by clearly showing system performance and customer behavior. Predictive analytics further enhance decision-making by identifying trends and potential disruptions.
Real-Time and Historical Data
The advantage of real-time telemetry over traditional data sources is maintaining operational continuity and enhancing an organization’s security posture. Immediate visibility into system activities allows for the quick detection of anomalies, system failures, and security threats, enabling faster response times and preventing incidents from escalating.
Real-time telemetry is essential for:
- Proactive Security: By analyzing real-time telemetry, organizations can detect security threats like unauthorized access or suspicious activity as they happen, preventing breaches and reinforcing their security posture.
- Operational Continuity: Real-time telemetry ensures that performance issues are quickly identified and resolved, maintaining seamless system operations.
While real-time telemetry provides instant insights, historical data also adds long-term value by offering a more comprehensive view of system trends and behaviors over time. Historical telemetry data, including curated data from sensors and DPI-analyzed data, offers valuable insights for enhancing network and application performance:
- Predictive Analytics: Historical data, enriched by DPI, helps identify patterns and forecast potential system failures or security vulnerabilities. These insights allow for proactive interventions, preventing disruptions and fortifying the organization’s security posture.
- Automation: Historical telemetry data supports automation by providing knowledge to AIOps platforms that automate responses to known issues, reducing manual intervention, and ensuring operational continuity.
AI-Ready Curated Data
For AI models to provide valuable insights, telemetry data must be normalized and curated from diverse sources, such as machine, conversational, and network data:
- Packet Data Processed by DPI: DPI analyzes packet contents in real time, offering AI models detailed insights into network behavior, anomalies, and security threats.
- Sensors at the Source: NETSCOUT’s Omnis AI Sensors capture raw telemetry data at the source, ensuring accuracy through real-time monitoring without data abstraction.
- Data Curation by Streamers: The Omnis AI Streamer curates data in real time, preparing it for AI analysis and streams this transformed telemetry to AIOps and data lake solutions to drive advanced analytics and intelligent automation, and a range of use cases.
NETSCOUT’s Solutions for Telemetry
NETSCOUT solutions process large volumes of the purest form of telemetry, DPI-processed packet data, to deliver actionable insights in real time and from historical trends. These insights help organizations optimize performance and enhance security, allowing businesses and AIOps to operate smarter and more efficiently. Omnis AI Insights is tailored to feeding AIOps platforms with actionable insights derived directly from packets to provide the most detailed data feed available.
Omnis AI Streamer and Omnis AI Sensor
- Omnis AI Streamer: Curates telemetry data received from the Omnis AI Sensor, preparing it for AI-driven analysis and action and streams it to AIOps and data lake platforms
- Omnis AI Sensor: Captures,processes, and analyzes packet-level data from your network through DPI in real time, providing detailed insights into network traffic and security.
NETSCOUT provides organizations with the tools to capture, curate, and analyze high-fidelity, granular telemetry data at scale, supporting various AI/ML models. Our approach addresses critical business and operational use cases and integrates seamlessly with data lakes, AIOps, and security platforms to deliver higher-quality behavioral classifications, predictive cybersecurity, and more reliable, automated business outcomes.