What's the best way to analyze incident response data from multiple sources?

1 Define your objectives and metrics

Before you start analyzing your data, you need to define what you want to achieve and how you will measure it. For example, do you want to assess the effectiveness of your incident response team, the impact of incidents on your business, or the compliance with industry standards and regulations? Depending on your objectives, you will need to select relevant metrics, such as time to detection, time to containment, time to recovery, incident severity, incident cost, incident root cause, or incident lessons learned. You should also align your metrics with your organization's goals and priorities and communicate them clearly to your stakeholders.

2 Normalize and standardize your data

To analyze data from multiple sources, you need to ensure that it is consistent and comparable. This means that you need to normalize and standardize your data, which involves converting different units, formats, and scales to a common baseline. For example, you might need to convert timestamps to a common timezone, use a common incident classification scheme, or map different incident categories to a common taxonomy. You can use tools such as parsers, converters, or scripts to automate this process and reduce errors and inconsistencies.

3 Integrate and correlate your data

Once you have normalized and standardized your data, you need to integrate and correlate it to gain a holistic view of your incident response performance and outcomes. This means that you need to combine and link data from different sources, such as logs, alerts, tickets, reports, surveys, or feedback, to create a comprehensive picture of each incident and its context. You can use tools such as data warehouses, data lakes, or data platforms to store and manage your integrated data and tools such as dashboards, visualizations, or analytics to explore and correlate your data. For example, you can use a dashboard to display key metrics and trends, a visualization to show the incident timeline and workflow, or an analytics tool to identify patterns and anomalies.

4 Validate and verify your data

To ensure that your data is accurate and reliable, you need to validate and verify it before you use it for analysis and reporting. This means that you need to check and confirm that your data is complete, correct, and consistent with your objectives and metrics. You can use tools such as data quality checks, data audits, or data reviews to perform this task and identify and resolve any issues or gaps in your data. For example, you can use a data quality check to detect missing, duplicate, or invalid values, a data audit to compare your data with external sources or benchmarks, or a data review to solicit feedback from your incident response team or other stakeholders.

5 Analyze and interpret your data

To extract meaningful insights from your data, you need to analyze and interpret it according to your objectives and metrics. This means that you need to apply appropriate methods and techniques to explore, measure, and explain your data and its implications. You can use tools such as descriptive statistics, inferential statistics, or data mining to perform this task and generate useful information and knowledge from your data. For example, you can use descriptive statistics to summarize and describe your data, such as the average, median, or range of your metrics, inferential statistics to test and infer hypotheses and causal relationships from your data, such as the correlation, regression, or significance of your metrics, or data mining to discover and extract hidden patterns and trends from your data, such as the clusters, associations, or outliers of your metrics.

6 Report and communicate your data

To share and communicate your data with your audience, you need to report and present it in a clear and concise way. This means that you need to select and organize your data according to your audience's needs and expectations and use appropriate formats and styles to convey your message and findings. You can use tools such as reports, slides, or infographics to perform this task and create engaging and informative deliverables from your data. For example, you can use a report to document and detail your data, such as the background, methods, results, and recommendations of your analysis, a slide to highlight and summarize your data, such as the key points, charts, and graphs of your analysis, or an infographic to illustrate and simplify your data, such as the icons, images, and colors of your analysis.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?