What is Data Visualization?
It is essential to represent the high amount of information or data, i.e., big data into convenient depiction and data visualization is a process to represent the data in a graphical or pictorial representation. Data visualization helps to compress and represent the data in an accessible way so that anyone can find loophole or variation in the data for solving the problems in the business or other work. This technology uses various elements like charts, maps, and graphs to represent the data to analyze massive amounts of data. It is also a significant component of the data presentation architecture (DPA) that identifies, manipulates, locates, formats, and presents data in the most efficient way.
Figure 1 Data visualization elements
Data visualization is an essential part of every career because it can be used by teachers to provide information to students regarding their test results. It can also be useful for data scientists in big data projects. It is a subpart of the data science process because we need the data in the visualized manner after it is collected, processed, and molded from various processes. A simple representation of data helps to find unknown new facts conveniently, for example:
- Line charts represent the data in a visual form to display variation in the information
- The line graph or bar graph represents the data for having the relationship and comparison in various sets of information.
- Pie charts represent the values to find expenditure and usage in the information.
Importance of Data Visualization
Data visualization provides an excellent and effective way to turn big data sets into communicative and visual information. This technology helps to identify various factors in the business like customer behavior or profit and loss to eliminate issues in the industry, and some of the prime benefits of data visualization include:
- This technology can absorb the information very fast and makes quick decisions out of it.
- Quick decisions can make the work more convenient hence data visualization can improve the working of the organization.
- Improved process and workability create audience interest in the audience towards the organization.
- Convenient distribution of information increases the value of sharing appropriate amounts of data between everyone.
- Data visualization helps to eliminate risks due to the process of display of relevant data so that individuals or organizations take action for future work.
Types of Data Visualizations
Now we have understood the data visualization, but there are various types of processes or tools available in the data visualization. Some of these tools are automated, and some are manual. Still, all process provides a representation of big data sets through data visualization, so the types of data visualizations are:
1. Line Chart
A line chart simplifies the data and visually represents the changes over time. It consists of x-axis usually represents the time, and the y-axis represents the quantity. It helps to find the monthly profits of the company.
2. Area chart
It is an adaptation of a line chart in which a specific area is filled in to emphasize its significance, and the color filling should be a bit transparent in case of overlapping of areas.
3. Bar chart
A bar chart also represents the changes in overtime, but it is used when there is more than one variable present in the dataset. The bar chart simplifies the comparisons of the variables based on time.
A histogram is like a bar chart, but it used to measure the frequency apart from trends over time. In the histogram, the x-axis represents intervals of variable, and the y-axis represents the frequency so that each bar can represent the frequency of that particular variable.
5. Scatter plot
Scatter plots provide relationships (correlations), and every point on the scatter plots are plotted between the x and y to find the appropriate relations in particular ways.
6. Bubble chart
It is a complete adaptation of scatter plots in which each point represents the bubble that consists of meaning according to its placement between axes.
7. Pie chart
It is the best option to provide the representation regarding percentages because it represents each element as part of a whole. The pie chart represents the piece in the appropriate proportions if the information needs to explain under percentages.
A gauge used for representing the distance between the intervals. It represents as a tube-type gauge, round clock-like gauge, or resembling a liquid thermometer. Multiple gauges can be shown next to each other for representing the difference between multiple intervals.
There are various data in business that deal with the location elements that make it easier to illustrate on the map. It is beneficial for business because it visually provides location information.
10. Heat map
A heat map is a representation of the data in the color-coded matrix because it needs a specific formula for each cell of the matrix to represent the value or risk in the colored format. Usually, the color range of heat maps can be from green to red in which green refers to better, and red refers to bad.
11. Frame diagram
Frame diagrams refer to treemaps that show the structure of hierarchical relationships. It includes branches that have more branches that represent each level of the diagram.
Uses of Data Visualization
There are a bunch of uses of data visualization, and here are some of the use cases that discussed below:
1. Determination of Overtime Changes
It is one of the most common and valuable uses of data visualization because every data involves some element of time. Hence it is the first step in data analytics to analyze data trends over time.
2. Frequency Determination
Determining the frequency of information in any data set is essential because it helps to find the appropriate frequency of the specific information in the complete dataset, and it can be done through data visualization.
3. Relationship Determination
It is crucial to find relationships between various elements of data, and it is challenging to find a relationship without the visualization of data. Therefore it is an excellent example of how useful data visualization is for analytics.
4. Risk and Value Analytics
It requires various factors to determine the risk and value because it is a sophisticated metrics, and it is impossible to mold it in a spreadsheet accurately. Data visualization simplifies the data and shows the valuable opportunities and risks in the system/organizational process.
5. Network Examining Process
Market research is the best example of network examining because professionals need to know about the target audience in the market. They analyze the entire market to identify the necessities and types of the audience.
Data visualization is the essential and final process in data analytics because it helps to make decisions according to the collected data. It is important to understand the needs of data visualization in web data integration solutions. Hence data visualization consists of various types that are beneficial for different sectors.
Review more information with a deeper dive in the Northwestern Data Visualization With Tableau course. It covers fundamentals and techniques beyond those we discuss above. Those who need help implementing big data projects may find value in ProjectPro.
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