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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.


A new, data-oriented mind-set is permeating the business world. Source - Data Visualization Is The Future

What are the features of Data Visualization?

  • Identify areas that need attention or improvement.
  • Clarify which factors influence customer behavior.
  • Decision-making Ability.
  • Integration Capability.
  • Predict sales volumes.

What are the best 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

  1. Identifying the purpose of creating a chart is necessary as this helps define the structure of the process.
  2. Select the right chart type.
  3. Selecting the right type of chart is very crucial as this defines the overall functionality of the chart.
  4. Attention to Detail using colors, shapes, and sizes.
  5. Choosing the correct type of color, shape, and size is essential for representing the chart.

Visual Analytics Process Workflow for creating visualizations: A Nested Model for Visualization Design and Validation.

What are the challenges of Data Visualization

Big Data is a large volume, complex dataset. So, such data can not visualize with the traditional method as the traditional data visualization method has many limitations.
  • Perceptual Scalability: Human eyes cannot extract all relevant information from a large volume of data. Even sometimes desktop screen has its limitations if the dataset is large. Too many visualizations are not always possible to fit on a single screen.
  • Real-time Scalability: It is always expected that all information should be real-time information, but it is hardly possible as processing the dataset needs time.
  • Interactive scalability: Interactive data visualization help to understand what is inside the datasets, but as big data volume increases exponentially, visualizing the datasets take a long time. But the challenge is that sometimes the system may freeze or crash while trying to visualize the datasets.

What are the components of Data Visualization?

Data Visualization Components helps to give more details and alternative views to look after the data. Listed below are the components of Data Visualization.

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. XenonStack Line Chart Data Visualization Services

Area chart

Area chart structure is a filled-in area that requires at least two groups of data along an axis. XenonStack Area Chart Data Visualization Solutions

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. pie chart data visualization services

Donut Chart

Doughnut Charts are pie charts that do not contain any data inside the circle. XenonStack Donut Chart Data Visualization Solutions

Drill Down Pie charts

Drill down Pie charts are used for representing detailed description for a particular category. Visual Analytics Process

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. bar chart data visualization company

Stacked Bar

In a stacked bar chart, parts of the data are adjacent to each bar and display a total amount, broken down into sub-amounts. XenonStack Stacked Bar Chart Data Visualization Services

Gauges

The gauge (gauge) component renders graphical representations of data. XenonStac Gauge Chart Data Visualization Services

Solid Gauge

Creates a gauge that indicates its metric value along a 180-degree arc. XenonStack Stacked Gauge Chart

Activity Gauge

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. XenonStack Gauge Activity Chart

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. XenonStack Heat Map Graph Business Intelligence Services

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. XenonStack Heat Map Levels Graph Data Analytics Solutions

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. XenonStack Scatter Plot Visual and Data Analytics Solutions

Combinations

Creates a graph that uses various kinds of data labels (bars, lines, or areas) to represent different sets of data items. Combination Graph Data Visualization Solutions

3D charts

Creating a 3D chart helps rotate and view a chart from different angles, which supports in representing data. Visual Analytics Process

3D Column

A 3D chart of type columns will draw each column as a cuboid and create a 3D effect. Xenonstack 3D Column Data Visualization Solutions

What is 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.

Acquire

Obtaining the correct data type is a crucial part as the data can be collected from various sources and can be unstructured.

Parse

Provide some structure for the data's meaning by restructuring the received data into different categories, which helps better visualize and understand data.

Filter

Filtering out the data that cannot serve the purpose is essential as filtering out will remove the unnecessary data, further enhancing the chart visualization.

Mining

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.

Represent

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.

Refine

Refining and Improving the essential representation helps in user engagement.

Interact

Add methods for handling the data or managing what features are visible.

Big Data Visualization Tools

Nowadays, there are many data visualization tools. Some of them are:

  • Google Chart: Google Chart is one of the easiest tools for visualization. With the help of google charts, you can analyze small datasets to complex unstructured datasets.
    We can implement simple charts as well as complex tree diagrams. Google Chart is available cross-platform as well.
  • Tableau: The tableau desktop is a very easy-to-use big data visualization tool. Two more versions are available of Tableau. One is "Tableau Server," and the other is cloud-based "Tableau Online." Here we can perform visualization operations by applying drag and drop methods for creating visual diagrams. In Tableau, we can create dashboards very efficiently.
  • Microsoft Power BI: This tool is mainly used for business analysis. Microsoft Power BI can be run from desktops, smartphones, and even tablets. This tool also provides analysis results very quickly.
  • D3: D3 is one of the best data visualization tools. D3.js is an open-source visualization tool.
  • Datawrapper: Datawrapper is a simple tool. Even non-technical persons can use the Datawrapper tool. Data representation in a table format or responsive graphs like a bar chart, line chart, or map draws quickly in the Datawrapper.
  • Databox: Databox is another visualization tool. It is an open-source tool. The whole data set can store in one location in the Databox tool. Then discover the insight data and perform visualization operations. In dashboard can view or match data from different datasets.

Many more data visualization tools are available per requirements and based on datasets, and visualization tools are chosen.

Purpose of Big-Data Visualization tools?

Big-data visualization tools' purpose is to read complex unstructured data with the help of visual diagrams to benefit our business. One relevant example is the covid 19 affected report. That is a large dataset. Only data scientists can read and find out the pattern and predict the percentage of affected patients.

Approach to Visualize Big Data

Big data is unstructured, and such data can be very easily stored on a NoSql database like MongoDB or relevant information needed to extract from the data and stored on a SQL database. Then from that dataset, with the help of data visualization tools, some charts like bar charts, pie charts, etc., need to plot. Then from those visual chart analyses can be performed.
NoSql database is mainly used for storing unstructured data like Big Data.
Choose appropriate data visualization tools
Use different algorithms as per requirement.
Visualize the dataset.

Use Cases of Big Data Visualization Tools

  • Sports Analysis: Based on previous datasets with the help of visualization tools, a winning percentage prediction is possible. Graph plotting for both teams or players is possible, and analysis can be performed.
  • Fraud Detection: Fraud detection is a famous use case of big data. With the help of visualization tools after analyzing data, a message can be generated to others, and they will be careful about such fraud incidents.
  • Price Optimization: In any business product, price set is a significant issue with visualizing tools and all the components used; price can be analyzed and finally compared with market price, and then a relevant price can be set.
  • Security Intelligence: Visualizing criminals' records can predict how much threat they are to society. Each country has its security intelligence, and its task is to visualize information and inform others about a security threat.

Java vs Kotlin
Our solutions cater to diverse industries with a focus on serving ever-changing marketing needs. Click here for our Data Visualization Consulting Services and Solutions

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.

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