Visualizations

Overview

Visualization is the process of transforming data into a graphical or visual representation that may include various elements such as charts, graphs, and tables. These elements are gathered from different sources and organized on a dashboard, which serves as a tool for monitoring and analyzing key performance indicators (KPIs) or metrics in real-time or over a specified time period.

Crafting effective data visualizations necessitates the expertise to judiciously select and present data in a manner that is readily comprehensible and accessible to end-users.

Visualizations Type

The Search Anywhere Framework offers the following visualization types:

Visualization	Description
	Table is a type of data visualization presented as an ordered collection of rows and columns. Each row in a table represents a separate record or data element, while each column represents a specific attribute or characteristic of these records.
	Pie Chart is a type of data visualization that represents data as a circular graph divided into slices. Each slice represents a portion or percentage of the whole. Pie charts are often used to illustrate the relative proportions of different categories within a dataset. They provide a quick way to visualize how parts make up a whole.
	Single Value visualization is a data representation tool that focuses on displaying and highlighting a single numerical value or metric. This type of visualization is commonly used to clearly present a specific metric or key performance indicator (KPI).
	Line Chart is a data visualization technique used to depict the change in a variable's value over time or in relation to another variable. Line charts are created by connecting data points on a graph with a line, providing a visual representation of the trend or changes in the data.
	Column Chart is a data visualization technique that represents information using rectangular columns of varying heights. Each column corresponds to a distinct category or group of data, and its height is proportional to the quantity or values within that category. Column charts are commonly used to compare the relative magnitudes of different categories.
	HTML-based Data Visualization is a data representation technique that utilizes HTML markup to structure and present information. HTML code can be written in an editor, incorporating tokens to dynamically access data, while CSS can be used in a separate style editor to style the layout.
	Heatmap is a data visualization technique that represents information using rectangular areas of varying colors. Each area corresponds to a distinct category, and its color encodes a quantitative value within that category. Heatmap are commonly used to illustrate the magnitude of quantitative measures across different categories using color.