Concepts

Understanding Insights concepts can help you create dashboards that answer your business questions. Data reporting and data visualization are distinct aspects of data analysis. Insights combines both to enable you to create dashboards that provide comprehensive answers.

Data reporting

Reports provide a comprehensive, granular view of your data to inform decision-making. You can build a report by defining metrics by dimensions and using aggregations and filters to structure and segment your data.

Define metrics and dimensions

Building a report involves defining what to measure (metrics) and how to categorize or segment the data (dimensions), then aggregating and filtering the data to provide actionable insights.

• Metric: Quantitative values that you plan to analyze, track, and present visually. Metrics can be aggregated to find the sum, average, or count. It's typically the key performance indicator (KPI) or main value you plan to report on.

• Dimension: Categorical variables that give context or descriptive information to the metrics in your report. Dimensions are used for slicing, grouping, and filtering data.

For example, you might plan to analyze the number of successful and failed jobs (metric) by Environment (dimension). You can use the Environment dimension to determine if integrations are performing well in your DEV, TEST, or PROD environments. Similarly, you can analyze money saved (metric) by project (dimension) to understand which projects are saving you the most money.

Data Visualization

Data visualization is about choosing the right way to visually represent your reports by selecting the appropriate charts and formatting elements. It aims to make the data easy to interpret at a glance by presenting it visually.

Best practices for dashboard design

A well-designed dashboard uses a combination of text, charts, and UI components to present your data in an engaging and functional way. We recommend the following best practices for effective dashboard design:

• Contextualize your data.
  • Give your dashboard meaningful titles and headings. For example, a dashboard that focuses on high-level insights might have the title ROI (Return on Investment) and distinct containers with the headings Overview and Task consumption, which naturally communicates the purpose of the dashboard and differentiate the containers from each other.
  • Customize the labels in your charts to communicate what your data refers to. For example, if you've run a query to aggregate the total number of jobs, choose a custom label, such as Number of jobs to better describe what you're reporting than the the default Count of Rows.
• Apply color strategically and consistently.
  • Use color to convey meaning and to make your dashboards more intuitive for your audience. For example, a chart component that visualizes successful and failed jobs, should use green for successful jobs and red for job failures.
• Organize your dashboard to prioritize readability.
  • Group different, but related, charts in a container. For example, a dashboard might have a container dedicated to Task consumption which includes multiple charts focused on task consumption by recipe, project, and environment.
  • Use padding to separate your components and avoid visual clutter.
• Use interactive filters for customization.
  • Apply filters at the dashboard or container-level to enable your audience to focus on a specific project, recipe, or date range.