First published: 22 August 2022 @ 7:31 pm
Written By: The Big Writer
Published At: Read More
What is Data Visualization?
Data visualization is data analysis and visualization that allows one to see information in a more meaningful way. It is one way in which you can analyze data.
Data visualization uses the visual aspects of data to allow users to analyze the data in a way that helps them understand it.
It can be a useful tool for understanding the meaning behind data, especially when there are many variables involved.
Why is Data Visualization Important?
Data visualization provides benefits such as:
1. Communication
You can use data visualization for communicating complex information and making it easier for people to understand.
2. Analysis
Using data visualization, one can analyze and see patterns within the data that otherwise would not have been visible.
3. Integration
Data visualization can integrate the information from different sources, making it easier for people to combine the data from different sources and see what the true value of the data is.
4. Accuracy
Using data visualization can make it easier for users to find accurate information that would otherwise hide within large amounts of data.
5. Visibility of Data and Relationships Between Data Sets
Using data visualization, one can clearly see what relationships exist between different data, the exact meaning behind the data, and gaps in their knowledge.
6. Seeing the Unseen
Data visualization can visualize data that is not visible to the naked eye.
This can be useful for detecting patterns within the data and understanding it better.
Photo by Luke Chesser on Unsplash.
7. Knowing What People Have Previously Done
By using data visualization, one can see what other people have done before with similar data sets and compare their findings with the results of the new analysis.
8. Creating New Value for Data Sets and Visualization Techniques
Using data visualization, one can create new ways of looking at a set of data, which can create new value for this set of data.
9. Seeing Trends
Data visualization can detect trends within data sets and help users to understand what is happening with the data and how it may change in the future.
10. Interpreting Data for End Users
Data visualization is a useful tool for communicating data and assessing the relevance of results.
It interpret data for end users and make it easier for them to understand the meaning behind the data.
11. Understanding Data Sets and Algorithms Used in Calculations
Data visualization can understand how algorithms within calculations function and why they are important, as well as indicate what values these algorithms will return.
What Does Big Data Mean?
Big data is a term used to describe large amounts of data that are so large you cannot analyze them traditionally.
There are many advantages to analyzing extensive data sets, such as:
Making good use of the data that we have already collected, in order to understand what this data means and how to bridge the gaps that may exist.
Finding trends that are hidden within large amounts of data that would not be visible without doing this type of analysis.
Visualize large amounts of data and put creative ways of looking at it to help people understand it better.
Finding accurate information from the huge amounts of information that we have available.
Using algorithms that calculate values from the huge amounts of data that are available.
Analyzing big data allows users to see where there are gaps in their knowledge about how these algorithms work or why they are important for calculating certain values from different data sets.
Types of Data Visualization
There are many types of data visualization. Each type of data visualization can analyze different data sets and provide different benefits to the user.
1. Color Visualization
In data visualization, color is used to highlight certain values in a dataset. A color wheel is an example of a visualization that uses color to show which data is important.
Business performance visualization can also use color to highlight the most important data.
You can show the importance of data by changing the color of different items in a visualization.
2. Bubble Visualization
Bubble visualization in data analytics has been around for a while. It is still in its infancy, but it’s a good idea to know what it is and how it works.
This technique is used to illustrate complex relationships in data.
Think of it as a visualization of data with the aid of bubbles that represent variables that are correlated with each other.
We often make business decisions based on a correlation between two variables.
The major advantage of this technique is that it allows users to compare data from different time periods easily and quickly.
Photo by Firmbee.com on Unsplash.
3. Bar Graphs
Bar graphs are used to show the data as a continuous series of rectangles. This type of graph is especially good for showing information about a range of values.
For example, you might use bar graphs to compare the price of various products over time or the price of various commodities in a certain region.
Customer satisfaction surveys might use bar graphs to compare the number of complaints for various products or services.
4. Line Graphs
Analytics is all about telling a story. A line graph tells the story of your conversion rate or your overall sales. It is easy to understand, simple to use, and immediately actionable.
Line graphs are a very simple yet effective way to show how your conversion rate or revenue has changed.
5. Pie Charts
Pie charts are easy to read and comprehend, which is the main reason people use them in financial data visualization.
Its visual format allows you to represent the data simply and also identify trends. Pie charts are useful for identifying large groups of information that are like each other.
It helps us see which groups of information are the most popular or have the highest volume.
6. Scatter Plots
The visual representation of scatter plots is an important concept in statistical graphics. A scatter plot is a plot of two variables that are not linearly related.
We plot them on the same axis with the values represented by the data points.
You can use scatter plots to illustrate the relationships between two variables. The plots can be used to:
Identify relationships between variables.
Identify outliers in a data set.
Understand the data distribution in a sample or population.
7. Time Series Graphs
Time series graphs are used to show the relationship between two different data sets and show how they change.
This graphical representation is most commonly used to show how sales change over time, and it is a great way to visualize data.
Visualization Tools for Business Owners
Data visualization is a useful tool for detecting trends within the data and see where there are gaps in understanding.
As business owners, we must make sure that we understand how to effectively use data visualization to see what data is important and where there are gaps in understanding.
By using data visualization, we can see the true value of our data and understand how it relates to other data sets.
About the Author
I hope that my post has helped you know more about Startups. Feel free to leave a comment and tag me and I will answer them. Follow my profile to get the latest content I post to stay ahead of the curve.
I am the Founder of Cudy Technologies, a full-stack EdTech startup helping teachers and students learn better. I am also a mentor and angel investor in other Startups of my other interests (Proptech, Fintech, HRtech, Ride-hailing, C2C marketplaces, and SaaS). You can also find me on Cudy for early-stage Startup Founder mentorship and advice.
Connect with me on LinkedIn if you have further questions. Let me know that you are a reader of my Medium posts in your invitation message.