What is Data Visualization?
Data Visualization Techniques 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. With the help of Data Visualization Techniques, enterprises are able to see the overview of their unstructured enterprise data in a better way.
What are the Features Data Visualization?
- Identify areas that need attention or improvement.
- Clarify which factors influence customer behavior.
- Decision-making Ability.
- Integration Capability.
- Predict sales volumes.
A new, data-oriented mind-set is permeating the business world.
Source – Data Visualization Is The Future
What are Data Visualization Techniques?
For growing companies, Data visualization is the essential part of the business to make business strategies. They follow various visualization techniques listed below:
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 the representation.
- How different colors symbolize visualization.
Identify the Purpose of the Visualization
- Identifying the purpose of creating a chart is necessary as this helps define 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 essential for representing the chart.
Workflow for creating visualizations: A Nested Model for Visualization Design and Validation.
Data Visualization Components
Data Visualization Components helps to give more details and alternative views to look after the data. Listed below are the components of Data Visualization.
1. Line Charts
Line Charts involves Creating a graph in which data is represented as a line or a set of data points joined by a line.
Area chart structure is a filled-in area that requires at least two groups of data along an axis.
2. Pie charts
Pie charts represent a graph in the shape of a circle. The whole chart is divided into subparts, which look like a sliced pie.
Doughnut Charts are pie charts that do not contain any data inside the circle.
Drill down Pie charts
Drill down Pie charts are used for representing detailed description for a particular category.
3. Bar Charts
A bar chart is the type of chart in which data is represented in vertical series and used to compare 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.
5. 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 identifying if any associations exist in-between them.
6. 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.
7. Scatter and bubble charts
Creates a chart in which the position and size of bubbles represent data. 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 rotate and view a chart from different angles, which supports in representing data.
A 3D chart of type columns will draw each column as a cuboid and create a 3D effect.
What are the Data Visualization Process Flow and Stages?
Each and every data has its particular need to illustrate data. Listed below are the stages and process flow for Data Visualization.
Obtaining the correct data type 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 better visualize and understand data.
Filtering out the data that cannot serve the purpose is essential as filtering out will remove the unnecessary data, further enhancing the chart visualization.
Building charts from statistics in a way that scientific context is discrete. Data visualization helps viewers seek insights that 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.
Get in touch with us for “Data Visualization Consulting and Solutions.”
Top Big Data Visualization Tools
- Power BI
- Google Chart
How Can XenonStack Help You?
Data Visualization Techniques helps enterprises engage and Interactive dashboards for Real-time and Batch Analytics and Visualization of IoT Devices and Network components. It also provides customized and Reusable templates for Data visualization solutions using React.js,HighCharts,Canvas.js and d3.js that help enterprises for data exploration and visualization of a large dataset from 2 million records to 100 million records. To know more about Data Visualization, we recommend to talk to our expert.