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.
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 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.
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.
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.
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.
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 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 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.
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.
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.
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.