Matrix Visualization

Introduction

Matrix Visualization is a powerful tool for presenting data in a tabular format, making it easy to showcase aggregated metrics over a specific time period. It's especially useful when you need to display data across multiple groups.

Key Features and Details:

• Combining Categories: Matrix visualization allows users to view metric values for combinations of two category values.
• Different from Data Table: While similar to a data table, the key difference is that matrix visualization organizes data with both row and column groups.
• Useful Features: Some of its notable features include:
  • Control over the number of rows displayed per page.
  • Color coding for better understanding.
  • Serial numbering for easy referencing.
  • Option to display percentages.
  • Ability to create cumulative rows and columns.
  • Special options for reporting.

Working with Matrix Visualization

Matrix Visualization comprises 2 important processes.

1. Configure Matrix Visualization lets you configure your Matrix visualization.
2. Visualization Options will help you personalize your Matrix Visualization and other visualizations to get the most out of your data. You have various options at your fingertips:
   • Panel Options
   • vuMatrix
   • Standard Options
   • Data Links
   • Value Mappings
   • Thresholds
   • Add Field Override

FAQs

How can Matrix Visualization help me compare metric values across different categories?

Matrix Visualization allows you to easily compare metric values by organizing data in a tabular format with row and column groups. This makes it ideal for analyzing trends and identifying patterns across different categories.

Can Matrix Visualization assist me in identifying outliers or anomalies in my dataset?

Yes, Matrix Visualization helps identify outliers and anomalies through:

• Threshold Values: Set specific thresholds for your metrics. Cells exceeding these thresholds can be highlighted using different colors.
• Color-Coding: Apply color schemes to visually differentiate normal values from outliers, e.g., green for normal, yellow for caution, and red for critical values.
• Percentage Bars: Use percentage bars to visualize data distribution, making it easier to spot anomalies.

What options are available for optimizing Matrix Visualization performance for a large dataset?

To optimize performance when working with large datasets:

• Minimal Matrix View: Enable the minimal matrix view to reduce visual complexity by simplifying the display into a 2-dimensional table.
• Row Limitation: Control the number of rows displayed at a time to avoid overwhelming the interface.
• Efficient Data Links: Use Data Links to drill down into specific metrics without overloading the display.

How can I ensure data security and access control within Matrix Visualization in shared dashboards?

Matrix Visualization supports Object-Level Permissions, allowing you to assign view or modify rights to different user roles. This ensures that only authorized users can access or alter Matrix Visualization configurations.