Visualization

Data analytics involves examining datasets to draw conclusions about the information they contain. This process utilizes various tools, techniques, and methodologies to identify patterns, trends, and relationships within the data. By transforming raw data into meaningful insights, VDA data analytics supports data-driven decision-making across diverse industries.

Data visualization is a crucial aspect of data analytics, as it translates complex data sets into graphical representations, making it easier to comprehend and interpret. VDA enable analysts to quickly identify trends, patterns, and outliers that may not be apparent from raw data alone. By presenting data visually, stakeholders can grasp the significance of their data insights more efficiently, facilitating better decision-making.

Steps to create Visualization

Create the Datasource

Datasource creation

Click on Dashboard

Click on + icon (bottom right corner) to add a new Dashboard

Create Dashboard

Create Dashboard

Provide a Title

Select the data source

Write a description

Create Dashboard

Create Dashboard

Click on the Dashboard which we have created

Create Dashboard

Click on + icon to add KPI

Write a query in English and submit, SQL will be generated automatically

Example: Top 10 customer from Customer table

Add KPI

VDA will generate a SQL Query and return relevant result in the side panel

Add KPI

Add a Title and write the required type of Chart

Add Chart Type

Visualization

VDA supports a wide range of visualizations to explore and present data effectively. Here are the types of graphs and visualizations that can be created using VDA:

1. Bar Chart: Displays categorical data with rectangular bars, where the length of each bar corresponds to the value it represents.

2. Line Chart: Shows data points connected by straight line segments, commonly used to display trends over time.

3. Area Chart: Similar to a line chart but with the area below the line filled in, making it easy to visualize cumulative totals over time.

4. Pie Chart: Displays data as slices of a circular pie, where each slice represents a category's proportion of the whole.

5. Scatter Plot: Plots data points on a two-dimensional plane, useful for visualizing relationships between two variables.

6. Bubble Chart: A variation of a scatter plot where data points are represented as bubbles with varying sizes, useful for displaying three dimensions of data (x-axis, y-axis, and size).

7. Histogram: Shows the distribution of numerical data by dividing the data into bins and displaying bars of frequency counts.

8. Heatmap: Visualizes data using colors to represent values in a matrix, ideal for identifying patterns or correlations in large datasets.

9. Box Plot: Summarizes the distribution of numerical data through quartiles, providing insights into variability and outliers.

10. Time Series Chart: Specifically designed for time-based data, displaying trends and patterns over a period.

11. Sunburst Chart: A hierarchical chart that shows relationships between data categories through nested rings, useful for displaying hierarchical data structures.

12. Treemap: Visualizes hierarchical data using nested rectangles, where the size of each rectangle represents a quantitative measure.

13. Dual Axis Chart: Combines two different chart types with shared axes, allowing for easy comparison between two sets of data.

14. Sankey Diagram: Illustrates flows and relationships between different entities, showing the magnitude of flows through the width of paths.

15. Chord Diagram: Displays relationships between entities, visualizing the flow or connections between them through arcs.