Data aggregation: Benefits and how it works

Data aggregation includes systematically collecting, transforming, and summarizing raw data from multiple sources. A unified, consistent view helps IT teams analyze vast amounts of information, uncover patterns, and derive actionable insights for informed decision-making. In our case, it’s all about enhancing incident management.

An example of data aggregation: Suppose your organization has multiple data centers. You can collect logs from servers, network devices, and applications using a data-aggregation tool. Your data analysts can analyze the information to monitor system performance and detect anomalies. If the server in Cincinnati consistently shows high CPU usage, analyzing the aggregated data can help you identify patterns and resolve potential causes.

Data aggregation provides a comprehensive view of the IT infrastructure, helping teams address incidents more effectively and efficiently. In addition, it enables teams to:

• Increase visibility and situational awareness. Aggregating data enhances the visibility of the entire IT environment, including a holistic view of system performance, security status, and network health. Predictive analytics can identify potential issues before they escalate.
• Enhance incident detection and root cause analysis. Data aggregation improves incident detection by correlating seemingly unrelated events. Comprehensive datasets support deeper analysis to understand the full incident context and identify root causes.
• Streamline response and resolution. Consolidating all relevant data helps automate incident response. Easier access to critical information reduces time-consuming data collection tasks and speeds up issue resolution.
• Improve response, efficiency, and productivity. Aggregated data allows faster response to incidents and anomalies. This efficiency both boosts productivity and ensures smoother IT operations with minimal downtime.
• Proactive incident management and better decision-making: Using aggregated data analytics, ITOps can shift from a reactive to a proactive approach. Early detection of trends and potential issues enables preventive measures, reducing the likelihood of critical incidents and downtime. And data-driven insights enhance strategic decision-making to improve overall system stability.
• Optimize resource and operational costs. Reduce spending on unnecessary hardware, software, and other operational aspects. Aggregated data provides insight into to identify under- or over-utilized tools. Efficient resource and data management support the sustainability and scalability of your IT operations over time.

Step 1. Collect

Data aggregation begins by collecting data from various sources, such as databases, monitoring tools, sensors, logs, and APIs. The data provides valuable insight into the IT environment, including network performance, system status, and application behavior.

Each source contributes unique data points, offering a comprehensive view of overall ITOps. Collection tools ensure a continuous data flow for analysis in real-time or at scheduled intervals. Using robust collection systems and aggregation methods capable of handling diverse data types and volumes ensures you capture all relevant information without loss or corruption.

Step 2. Normalize and transform

Standardizing data formats and structures is crucial for consistency. Raw data often comes in formats that may be incomplete or inaccurate. Normalization improves data quality by cleaning, organizing, removing duplicates, and resolving inconsistencies. Transformation then converts this cleaned data into a usable format suitable for analysis. These processes allow you to compare and combine data seamlessly, regardless of source.

Topology plays a crucial role by determining the flow of data through an IT infrastructure. By mapping the network topology, you can strategically place data collection points to capture relevant data and ensure comprehensive coverage across the infrastructure.

Step 3. Process

Use sophisticated tools and algorithms to process data and derive valuable insights. Preparing the data for analysis involves filtering out irrelevant data, sorting based on predefined criteria, and summarizing to reduce its volume while retaining important information. These can include statistical analysis, machine learning, or other data mining techniques that identify trends, correlations, and patterns.

Data processing retains only meaningful data for further analysis, making the aggregation process more efficient and manageable. Storage solutions such as databases, data warehouses, or data lakes must be scalable and efficient to handle the volume of collected data.

Step 4. Integrate and analyze

Aggregated data is integrated and analyzed to identify insights and patterns. This involves analysis across different data sources to identify relationships and dependencies. Pattern-recognition can detect anomalies, trends, and recurring issues within the data.

Step 5. Report

Access aggregated data using dashboards, reports, and visualizations. Effective presentation makes data easily understandable and actionable. Dashboards provide an organized display of metrics and KPIs, enabling an instant perspective of IT system health and performance. Use graphs, charts, and other visualization tools to convey complex information clearly and concisely, helping stakeholders quickly grasp vital insights and make informed decisions.

Visualization tools also enable interactive data exploration, providing deeper insights through drill-down capabilities and real-time updates.

Enhance the ability to detect, respond to, and resolve IT issues. Use integration to streamline operations and facilitate proactive incident management.

Centralized alert management and event correlation

Data aggregation also improves IT alert management. Combining alerts from various sources like logs and sensors into a single platform or dashboard provides a unified view of the IT environment. Event correlation can identify patterns and relationships, helping distinguish isolated incidents from broader systemic issues. This centralized approach streamlines alert handling to reduce the chance of alerts being overlooked or lost in the noise.

Automated incident creation and enrichment

Data aggregation supports automation by providing a comprehensive dataset that triggers incident-creation workflows based on predefined thresholds or anomalies. The aggregated data enriches incident records with contextual information like historical data, system configurations, and relevant metrics. Context ensures that you have a complete understanding of the incident’s nature and scope from the start.

Incident prioritization and triage

Easily assess incident severity and impact using aggregated data and real-time analysis based on criteria like business criticality, user experience, and service dependencies. Use automated algorithms or manual rules to promptly address critical issues and prioritize less urgent ones. Prioritization streamlines incident triage, supporting more effective resource allocation.

Intelligent alert suppression and noise reduction

Identify normal behavior patterns and detect abnormal events with aggregated data. Combined with intelligent alert suppression, analysis filters out redundant or low-priority alerts, reducing noise and helping teams focus on meaningful alerts. Additionally, ML algorithms can adjust alert thresholds based on historical data, further refining alerting and improving efficiency.

Use BigPanda AIOps to aggregate system data for better insights. The Real-time Topology Mesh component provides a comprehensive topology model of your entire IT stack, combining data from multiple sources into a single, full-stack visualization.

By ingesting data from configuration, cloud, and other management tools, BigPanda enables multisource topology aggregation. This approach helps identify connections by visualizing patterns across complex environments to expedite understanding of incident impact. BigPanda topology-driven correlation matches monitoring alerts to the real-time topology model, ensuring accurate and actionable correlations.

BigPanda also speeds up root-cause analysis using contextual and change data. It matches alerts from different sources with change data to find and fix incident-causing changes, ensuring thorough incident management in hybrid-cloud setups and use cases.

Next steps

Explore the BigPanda platform or take a self-guided tour.

See how BigPanda customers, including the New York Stock Exchange and Autodesk, benefit from BigPanda.