Updated March 21, 2023
Introduction to Benefits of Data Visualization
Data Visualization involve the graphical representation of the data in the form of charts, graph, and maps which make the analysis of the complex data easier to the user, due to this data visualization has a huge benefit in the field of Healthcare industries, Military and Finance Industry along with this it is also helpful in industrial applications like data comparison, data analysis, data distribution over a period of time and visualization of a geographical dataset.
Benefits of Data Visualization and Its Applications
Data visualization brings easiness in understanding and increases effectiveness. The human mind learns fast from visuals than that from text and tables. Moreover, it is applied to a large population; for e.g., one can remember dialogues and scenes of Sholay movie, which he might have watched years before; on the other hand, it is difficult for him to recall the engineering subjects.
Nowadays, we have a good number of tools for data visualization tools, which are fast and effective. Data visualization creates a better selling strategy. Data visualization boosts the ability to process information in an easy /faster way to compare and make conclusions. For e.g., pie charts give percentage-wise allocation, bar graphs give a better understanding of statistics.
Source link: https://matplotlib.org/gallery.html
Figure A Bar chart
Source link: https://matplotlib.org/gallery.html
We will discuss the Benefits of Data Visualizations only with some industries, but this applies to almost all industries.
1. Healthcare Industries
Creation of a dashboard to visualize patients’ history can help an existing or a new doctor understand a patient’s condition. In case of emergency, it could provide quicker care facilities according to disease. Data visualization can help in identifying the trend instead of going through reports of 100s of pages.
Health care is a complex process, and most of the time is wasted in analyzing the previous reports. Data visualization gives a better selling point by increasing response time. It provides matrices by which it is easier to analyze, thus increase response time.
2. Military
For the military, it is a matter of life and death; it is of utmost importance to have clarity of actionable data, and to take the right action, one must have clarity of data to draw out actionable insights.
Today, the enemy is out there in the field and is also threatening through digital warfare and cybersecurity. It is of utmost need to gather data from multiple sources – structured and unstructured. The amount of data is quite huge, and data visualization tools play a key role to ensure the timely delivery of proper information in the best-consolidated way possible. A better understanding of historical data provides a better prediction.
Dynamic Data Visualization helps in understanding geography/ climate better, which helps in a better approach. Military equipment and tools’ cost is quite high; with bar and pie charts, it is easy to analyze existing inventory and make the purchase as per need.
3. Finance Industries
Nowadays, Data visualization tools are a must for financial sectors for exploring/explain data of related clients, understanding customer behavior, having the transparent flow of information, the efficiency of decision making, etc.
Data visualization helps in creating patterns for associative firms and businesses, which helps in better investment strategy. Data visualization highlights the latest trends for better business opportunities.
Data visualization helps in almost all industries; it depends on need, what business wants and how visualization can help!
How to Create Data Visualization for Industries?
Before Visualizing data, one must know what the need for business/industry is? Data visualization helps in answering that question in an easy manner. Data analysis and visualization go hand in hand; in python, we have libraries like NumPy and pandas for data analysis and for data Visualization, we have libraries like Matplotlib, Pandas Visualization, and Seaborn.
Let us understand the basic purpose of data visualization. In the end, we will see the python code for data visualization.
For Comparing Data
Comparison plays a very important role in quantitative analysis. The following data visualization techniques can be used.
- Bar graphs (Histograms): With bar graphs, it is easy to do quantitative analysis among different categories.
- Stacked charts: This has added the advantage of composite data analysis with different categories.
Figure B: Stacked Charts
Source link: https://matplotlib.org/gallery.html
Composition analysis
Here we define the composition of data; the following techniques are used for visualization.
- Pie charts– These are circular charts and provide percentage-wise composition.
- Pyramid charts - This basically is used for the hierarchy of data.
Figure C: Pyramid Chart
Source link: https://matplotlib.org/gallery.html
Analysis over a period
For many instances, one needs to track the data for a period and then analyze the trends. Some visualization tools for this type of cases are as follows:
- Line charts- Shows basic highs and lows of the trend.
- Area charts - To show cumulative data of line chart over a period
- Stock charts - Are basically used for trend analysis of stocks and market indexes over a period.
Figure D: Line Chart
Source link: https://matplotlib.org/gallery.html
Distribution analysis of data
In most cases, we need to see the relationship between features and the effect of one feature on others. Data visualization aids with the following techniques.
- Scatter Plot : Here, we represent each data point as a dot and then analyze the trend.
- Box plot/ violin plot: This gives a better understanding of data for outliers with the median as a center of a distribution.
- Heat map charts : These are cool plots where you can show correlation and distribution with colors.
Figure E: Heat Map with Correlation between features
Source link: https://matplotlib.org/gallery.html
Visualization of Geographical Dataset
In many cases, we deal with geographical data, like population growth in different regions. Effect of any natural calamity in certain areas. The following graphs can help us visualize the location-wise effect.
- Choropleth maps — variation across different locations.
- Dot maps — same as choropleth with dots showing data density.
Figure F: Choropleth for the population of India
Source link: https://matplotlib.org/gallery.html
Conclusion
Data is processed faster when one can visualize it. Data visualization puts all the information in a comprehensive which may be missed in the traditional approach. For data analysis, we have libraries like NumPy and pandas. For data Visualization, we have libraries like Matplotlib and Seaborn.
We discussed how visualization benefits in different industries. We also have discussed how can we benefit from different visualization techniques. At last, we have discussed references for Python code.
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