What if you wanted to know the number of movies produced in the world per year in different countries? You could always read this data in the form of a black and white text written on multiple pages. Or you could have a colorful bar chart that would immediately tell you which countries are producing more movies and if the total movies per year are increasing or decreasing. In short, a bar chart would tell you information just by looking at it whereas you would have to read multiple pages of data to understand this information normally. This is the power of Data Visualization! It allows us to easily see trends and patterns in the data which would normally be quite difficult to find, let alone analyze.
Are you still confused about Data Visualization? Well then, let’s start at the beginning and first understand what is Data Visualization.
What is Data Visualization and why is it important in Data Analytics?
Data visualization is basically representing the raw data in a visual format such as a bar chart, pie chart, histogram, scatterplot, etc. This is extremely important in this age of Big Data because it is very difficult to understand such large amounts of data without context. We can analyze Big Data using Data Analytics to obtain useful conclusions, but it’s best if those conclusions are presented in a format that humans can easily understand. And that’s where Data Visualization comes in!
For example: What if you analyzed your company data and found out that a particular product was consistently losing money for the company. Your boss may not pay that much attention to a written report but if you present a line chart with the profits as a red line that is consistently going down, then your boss may pay much more attention! This shows the power of Data Visualization!
And Data Visualization is not just important in Data Analytics. It is necessary to understand data and present it visually in almost any field like finance, technology, marketing, design, etc. After all, whatever field you work in, the importance of good data charts in a presentation cannot be understated!
How is Data Visualization used?
Currently, Data Visualization has four major uses from an industrial standpoint. Let’s check them out:
1. Understand data quickly
Businesses can understand large amounts of data much more quickly and efficiently using Data Visualization. After all, it’s much easier to analyze and understand data if its in a visual form like a bar graph or pie chart rather than in a textual form like spreadsheets. Understanding data quickly also means that businesses can take decisions based on that data much more quickly as well.
2. Identify relationships and patterns
It is much easier to identify the relationships and patterns in the data when it is presented visually. Of course, there are some patterns that are obvious and immediately found, but there may be some hidden links and patterns in the data that you never thought were there. These are not visible when the data is in textual form and only becomes obvious when it is visually presented.
3. Pinpoint emerging trends
Businesses can obviously find out current trends in the data but it is sometimes possible to even estimate future trends using Data Visualization. This gives a huge edge to companies in the market that actually use Data Visualization as they can move ahead of their competitors by analyzing future market trends.
4. Communicate the story to others
It is not only enough that only data analysts and other technicians in the business understand the data. It is equally important to showcase the data analysis and results obtained to other people in the company such as the shareholders. In such a situation, Data Visualization is extremely helpful because it condenses the data into a
form everybody can understand.
What are the different types of Data Visualization charts?
There are many different types of Data Visualization charts that you can use depending on what type of data you want to show and how you want to show it. Before deciding on a particular chart, you have to consider the relationship between your data, whether you are comparing single or multiple data values with respect to time, are you showing a specific trend in the data, etc. These are all questions you should answer before you choose any of these Data Visualization charts:
1. Bar Chart
Bar charts organize the data into rectangular bars that can easily be used to compare data sets. You should create a bar chart if you want to compare two or more data values of a similar kind and if you don’t have too many data groups to display. However, bar charts show discrete data so it might not be a good idea to use it if you want continuous data.
2. Line Chart
Line Charts visualize data in the form of a line that is very useful in understanding trends and patterns. It’s best to use the Line Chart if you want to show data relative to a continuous variable like time. Different colored lines for different variables in the data make it very easy to understand a line chart.
Scatterplots are used to understand the relationship between two variables in the data. You can also find the outliers in your data or understand the overall distribution by plotting a scatterplot. If the data moves from lower left to upper right, there might be a positive correlation between the two variables if the data move in the opposite direction, there might be a negative correlation.
Sparklines are the best Data Visualization if you want to show general trends in a speedier manner. Sparkline is also useful if you want to show a particular data variable changing with time. It can paint an approximate picture which is very easy to understand but only if the readers can understand the Sparkline as well.
5. Pie Chart
Pie Charts are best if you want to compare some parts of the whole in the data. They can easily give an idea of the number of different parts on the whole but they are not very precise unless you add numerical values to each part of the pie chart representing each individual share on the whole.
A Gauge is used to compare a value on a single scale. This value is usually specified as the current value and the total possible value with the gauge indicating your progress in green and the rest of the part in red. It is not a good idea to use a gauge if you want to display more than one value simultaneously.
7. Area Chart
Area Charts are similar to Line Charts in that they visualize data in the form of a line that is very useful in understanding trends and patterns. However, the area under the line in an are chart is also colored. This can be used with multiple variables in the data to demonstrate the relative differences between the variables.
8. Geographical Map
A Geographical Map visualizes the data on top of a map of a geographical location. It is best to use this map if the data has geography as an important part with different shades of the same color representing different data meanings on the map. However, if you want to show precise points of data, then a Geographical Map is not the best idea.
9. Heat Map
Heat maps provide the relationship between two variables in the data along with rating information between these variables. This rating information is normally displayed using various shades of the same color with light to dark demonstrating an increase in rating.
Histograms are a cross between bar charts and line charts. They organize the data into rectangular bars across a continuous time interval. This is different than bar charts as they can be across discrete intervals. You should use Histograms to show the distribution of data over time or to compare two variables in the data over time.
- Difference Between Data Visualization and Data Analytics
- Difference Between Data Analytics and Predictive Analytics
- Difference Between Business Analytics and Predictive Analytics
- Difference Between Data Mining and Data Visualization
- How Data Visualization Enables us to Monitor COVID-19 Data?
- Difference Between Data Science and Data Analytics
- Data Visualization Using Chartjs and Django
- 10 Best Data Visualization Tools in 2020
- What is Tableau and its Importance in Data Visualization?
- Top R Libraries for Data Visualization in 2020
- Top 8 Python Libraries for Data Visualization
- Top 10 Libraries for Data Visualization in 2020
- Data Analytics and its type
- How Do Companies Use Big Data Analytics in Real World?
- Difference between Cloud Computing and Big Data Analytics
- Difference Between Business Intelligence and Data analytics
- Difference Between Cloud Computing and Data Analytics
- Top 10 Hadoop Analytics Tools For Big Data
- Difference Between Big Data and Predictive Analytics
- Fundamental Steps For a Data Analytics Project Plan
If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to firstname.lastname@example.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.
Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.