Understanding Data Visualization
Data Visualization uses charts and graphs to visualize large amounts of complex data. Visualization provides a quick, easy way to convey concepts and summarize and present large data in easy-to-understand and straightforward displays which enables readers insightful information.
Data Visualization Features-
- Identify areas that need attention or improvement.
- Clarify which factors influence customer behavior.
- Predict sales volumes.
Data Visualizations Techniques
Understanding the motive of the Visualization
- Know your data
- Getting to know the structure of your data
- Which Variables are we trying to plot
- How x-axis and y-axis will be used for representation
- How different colors symbolize for visualization
Identify Purpose of the Visualization
Identifying the purpose of creating a chart is necessary as this helps in defining the structure of the process.
Select the right chart type
Selecting the right type of chart is very crucial as this defines the overall functionality of the chart
Attention to Detail using colors, shapes, and sizes
Choosing the correct type of color, shape, and size is very essential for representation of the chart.
Workflow for creating visualizations: A Nested Model for Visualization Design and Validation.
Data Visualization Components
Line Charts involves Creating a graph in which data is represented as a line or a set of data points that are joined by a line.
Area charts structure is a filled-in area which requires at least two groups of data along an axis.
Pie charts represent a graph in the shape of a circle. The whole chart is divided into subparts which looks like a sliced pie.
Doughnut Charts are pie charts which do not contain any data inside the circle.
Drill down Pie charts are used for representing detail description for a particular category.
A bar chart is the type of chart in which data is represented in vertical series and is used for comparing trends over time.
In a stacked bar chart, parts of the data are adjacent to each bar and display a total amount, broken down into sub-amounts.
The gauge (gauge) component renders graphical representations of data.
Creates a gauge that indicates its metric value along a 180-degree arc.
Creates a gauge that shows the development of a task. The inner rectangle shows the current level of a measure against the ranges marked on an outer rectangle.
Heat and treemaps
Heatmaps are useful for presenting variation across different variables, revealing any patterns, displaying whether any variables are related to each other, and for identifying if any associations exist in-between them.
Treemap with levels
The treemap component displays quantitative hierarchical data across two dimensions, represented visually by size and color. Treemaps use a shape called a node to reference the data in the hierarchy.
Scatter and bubble charts
Creates a chart in which data is represented by the position and size of bubbles. Use to show similarities among types of values, mainly when you have multiple data objects, and you require to see the general relations.
Creates a graph that uses various kinds of data labels (bars, lines, or areas) to represent different sets of data items.
Creating a 3D chart helps in rotating and viewing a chart from different angles which supports in representing data.
A 3D chart of type columns will draw each column as a cuboid and thus create a 3D effect
Data Visualization Process Flow and Stages
Obtaining the correct type of data is a crucial part as the data can be collected from various sources and can be unstructured
Provide some structure for the data’s meaning by restructuring the received data into different categories which helps in better visualization and understanding of data.
Filtering out the data which cannot serve the purpose is essential as filtering out will remove the unnecessary data which will further enhance the chart visualization
Building charts from statistics in a way that scientific context is discrete. Data visualization helps viewers seek insights which cannot be gained from raw data or statistics.
One of the most significant challenges for users is deciding which chart suites best and represents the right information. The data exploration capability is necessary to statisticians as this reduces the need for duplicated sampling to determine which data is relevant for each model.
Refining and Improving the essential representation helps in user engagement.
Add methods for handling the data or managing what features are visible.
Top Big Data Visualization Tools
- Power BI
- Google Chart
You May also Love to Read Visual Analytics for Financial Sector using Highcharts & React.JS
How Can XenonStack Help You?
Interactive Dashboard Design:– Create Interactive, Intuitive and Visually Appealing Dashboards for Data Visualization.XenonStack provides data visualization solutions for engaging and Interactive dashboards for Real-time and Batch Analytics and Visualization of IoT Devices and Network components
Customized Data visualization– XenonStack Provides customized and Reusable templates Data visualizations solutions using React.js,HighCharts,Canvas.js and d3.js
Large DataSet Visualization:– XenonStack helps enterprises for data exploration and visualization of a large dataset from 2 million records to 100 million records
Tableau, Power BI and QlikView Solutions for Data visualization– XenonStack also offers solutions integrating Gateways between Data On-Premises and Data Visualization using Platforms like Tableau, QlikView and Power BI for Data Security and governance.
How useful was this post?