A Complete Guide To Everything You Need To Know About Data Visualization

Most people believe that data visualization is a modern tool that we discovered due to recent technological advancements. However, that is not the case. In fact, methods of data visualization have been used throughout human history long before we were a civilized society or had access to modern technology.

A Brief History of Data Visualization

A-Brief-History-of-Data-Visualization
The Lascaux Star map that was found in France used grids and visualizations to create a map of the night sky. Archaeologists were unable to determine with certainty how old this map was, but they estimated that it was created some time in between 30,000 to 10,000 B.C. Another ancient method of data visualization was used by a number of cultures in the region of Andean South America in 2600 B.C. People in this region used a device called Quipu, which was fashioned from strings. It was used for collecting data and collecting records. Even back then, the Inca people used this tool for monitoring tax obligations, organizing the military, and collecting census records. In the modern world, we use our advanced data visualization techniques for some of the same things, but it is unfathomable that people were already doing this over 2000 years ago. 
The first concrete documented use of data visualization can be dated back to 1160 B.C. in Ancient Egypt. An Egyptian map called the Turin Papyrus Map uses figures to show the distance between quarries, and it also semi-accurately depicts the size of the stone used. Much like our modern methods, it also used colors to code information about the different types of stones used in each quarry. The birth of what we know as modern data visualization techniques was in 1679, when Rene Descartes, the father of geometry used analytic geometry and the two-dimensional coordinate system to visually describe algebra using geometry. A few decades later, in 1781, William Playfair created the first bar chart to represent Scotland’s imports and exports from 17 countries. Playfair is also credited with the creation of the line, area, and pie charts. He firmly believed that charts were a better form of communication than large tables of data. 
So, that is the history of the first use of data visualization and the birth of modern data visualization methods. Now, let us have a look at the events in recent years that have led to the creation of the tools that we use today. Jacque Bertin’s work in 1967, is used as a foundation of how we visualize data to date. He was the one to find that the visual perception of data operated according to the rules could be used to express data clearly and efficiently. The most significant recent contribution to modern data visualization was in 1987, Edward Tufte, an expert in informational graphics, published his work that revolutionized how we view data today. His work focused on the importance of accurately and efficiently depicting data. He introduced the data-ink ratio, which encourages presenters to present any graphic as simply as possible. The best practices of today, including the removal of background colors and redundant data labels, sprung from his work.

What is Modern Data Visualization and How Does it Work?

What-is-Modern-Data-Visualization-and-How-Does-it-Wor
Even today, large organizations use some of the same data visualization methods that were used by people in the past. However, the most significant factor between data visualization today, and in the past, is the primary objective of visually representing data. While it was used for a variety of reasons in the past, today, we use data visualization to distil large datasets into visual graphics. By doing so, we allow ourselves to understand the complex relationships within the data easily. Data visualization is often used interchangeably with terms such as information graphics, information visualizations, and statistical graphics.
The implementation of data visualization as a data science process was developed by Joe Blitzstein, a professor at Harvard University. He created the framework for using data visualization to be used to approach complex data science tasks. To put it simply, the process of data visualization starts with the collection of data, the data is then processed, the relationships that need to be viewed are modelled, and finally, a conclusion can be made. The modern methods used in data visualization have also become a part of the data presentation architecture (DPA), which is a structure that seeks to identify, locate, manipulate, format and present data in the most efficient way possible. 

Why is Data Visualization So Important and Do We Really Need It?

The second half of the question, which is “Do we really need data visualization?” can be answered by the following illustration. According to the World Economic Forum, globally, we produce 2.5 quintillion bytes of data every single day (We Forum). Just to give you some perspective, one quintillion has eighteen zeros. What is even more surprising is that 90% of all data that has ever been created in human history, was created in just the last two years. This must have given you a general idea of the exponential rate at which we are creating more data.
 It is becoming increasingly difficult to make sense of it all. If we cease our constant efforts to understand this abundance of data, soon, the task will become unfathomably challenging. It is impossible for humans to sit down and go through the data in their lifetime. This is where data science comes in. It makes the process significantly more manageable and automated to a great extent. Data visualization is also a crucial part of the data science process. Here are some of the advantages of using data visualization. 
Why-is-Data-Visualization-So-Important
  • Improved Insight

If you think, what can data visualization do that traditional descriptive statistics cannot do since both methods were created with the sole purpose of drawing valuable insights from data. While that is true, the answer to this question can be better explained with an example. The figures given below depict Anscombe’s quartet, and it was created by Francis Anscombe in 1973. This might get a little technical here, but bear with me because this is essential to understand the limitations of traditional descriptive statistics. 
The figures given above are an illustration of four different datasets with almost identical mean, variance, and the correlation between the X and Y coordinates. However, when each of these patterns are plotted on a graph, they look vastly different. It is apparent to anyone who sees these graphs that there is not much in common between them while these datasets are almost identical. This example outlines the importance of data visualization and how the visual representation of data using traditional descriptive statistics can be misleading. 
  • Faster Decision Making

The current business scenario is exceptionally competitive, and everyone is in a race to get to the top. Therefore, companies that can quickly gather and act on their data will gain a competitive edge over their competitors as it will allow them to make informed decisions sooner. Today speed is of the essence and data visualization will aid any organization in understanding vast quantities of data by applying visual representations to it. Data visualization is usually the final layer that lies on top of a data warehouse, and it allows users to discover and explore data on their own. Using data visualization not only spurs creativity, but it also reduces the need for I.T. to allocate resources to build new and advanced models continually. 
This too, can be better understood with the help of an example. Suppose a marketing analyst who works across twenty different ad platforms and internal platforms needs to understand the effectiveness of his organization’s marketing campaigns quickly. Of course, he could do it manually by physically going to each system, pull out the necessary reports, combine the data, and analyze it in Excel using traditional descriptive statistics. But we have already discussed the problems related to the use of descriptive statistics to analyze large datasets. The analyst will have to wade through a swarm of metrics and attributes. As a result, it will be exceedingly difficult for him to draw conclusions. The advantage of using data visualization and modern business intelligence platforms for this process is that it will be able to connect the data sources and create visual representations that the analyst will be able to understand with ease. Therefore, he can quickly make conclusions about the marketing performance of his organizations. 
  • Identify Hidden Patterns

Data visualization can help companies analyze large amounts of data and create reports regarding sales, marketing strategies, and product interests. They can then use this analysis to focus on the areas of their business that require more attention to increase profits, which in turn makes the business productive. However, large amounts of complex data can have a lot of valuable hidden trends that could prove to be extremely useful for companies.
The human brain’s ability to grasp information from visuals is substantially better from data visualizations than table reports. Therefore, data visualization allows organizations to identify hidden trends and gain insights from them quickly. Thus, enabling teams to take the necessary action for their business growth. An explicit focus on these patterns will help teams to focus on specific areas of data that require more attention, from large datasets. Only if teams are quickly notified of new data insights, they will be able to take the necessary actions to take their business forward.
  • Understanding the Story That Your Data is Trying to Tell

Understanding data in modern times is so critical because it tells a story about the state of growth of the world we live in. This story can give us further insights into the mind of consumers and the functioning of our society. The real purpose of data visualization is to tell the story that the data has to say. If you design your visuals in a way that is meaningful, you will be able to help your target audience grasp the story in a single glance. It is also crucial that you communicate this story to your target audience in the simplest way possible. 
The key to identifying business insights is finding accurate data correlations using visual representations. Exploring data insights is vital for business executives to set their organizations on the path to success. However, the use of excessively complicated visuals can hinder this process. So, remember to keep it simple. Another meaningful way in which data visualizations helps businesses is by enabling them to identify any errors in the data quickly. Sometimes, data can suggest taking actions that could be harmful to businesses. But data visualizations will help organizations identify erroneous data or outliers that can then be removed.    

A Step by Step Guide to the Data Visualization Process

Data visualization helps businesses see the big picture by assisting them in understanding the hidden meaning behind large quantities of data. But due to data’s continually changing nature, each step of the data visualization process must be completed with utmost care and consideration. Mentioned below are the steps you must follow in order to maximize the utility from your organization’s data visualization efforts. 
  • Acquire

Data visualization helps businesses see the big picture by assisting them in understanding the hidden meaning behind large quantities of data. But due to data’s continually changing nature, each step of the data visualization process must be completed with utmost care and consideration. Mentioned below are the steps you must follow in order to maximize the utility from your organization’s data visualization efforts. 
  • Parse

Once the data is acquired, it needs to be changed into a format that tags each portion of the data with its intended use. For this process to be efficient, each line of data in a large file must be broken down along its individual parts. Through parsing, the data is successfully tagged and is significantly more useful to a program that will manipulate it in any manner. 
  • Filter

In this step, the data is filtered to remove parts that are not relevant. The removal of data that is not of any use to an organization will further reduce the complexity of the data set.  
  • Mine

The process of data mining mostly involves a lot of math and statistics. Various methods of statistics are applied to the data in order to discern patterns and place the data in a mathematical context. 
  • Represent

This step determines the basic form that any data set will take once it is represented visually. For instance, some data sets are shown as lists, while others are structured as graphs or trees.
  • Refine

In the penultimate step of this process, graphical design methods are used to further simplify and clarify the final representation of data by calling more attention to particular aspects of the data by establishing a hierarchy. This can also be done by changing attributes such as color that contribute to readability.
  • Interact

This is the final step of the data visualization process. This step consists of letting the user control or interact with the data. It includes processes like selecting the subset of the data or changing the viewpoint. In some instances, a change in viewpoint might also require the data to be designed differently.
The incredibly high significance of data visualization in the modern world can be attributed to the fact that humans can grasp data better through visualization than through numbers. In the context of a business, using Mindbowser’s data visualization services helps businesses to effectively convey the data’s story to key decision-makers in the organization. This allows users to act quicker than if all their data was presented as reports. It also enables our customers to interact with data more efficiently and answer questions that may otherwise have been difficult to answer or even missed. By revealing latent trends businesses are able to focus on business insights that require more attention. Data visualization also helps organizations better manage their growth and convert new trends into innovative business strategies. 

Why Python is the Ultimate Data Visualization Tool for Your Organization

Data science is a field that when applied correctly, it can offer immense potential for your business to grow. Data science is a highly complex field, and in order to properly implement it, you must ensure that you are following all the critical rules that must be followed. One of the most essential tools that will allow your organization to understand your data efficiently, and scale your business’s growth exponentially, is Python. Why? Because Python is the most widely used language in the field of Data Science and has a wide array of ready libraries and can be integrated with existing data structures and other languages such as Java. Since it is an open-source programming language, it is not only quite flexible, but it also improves continually. Here are a few reasons that might convince you that Python is the best data visualization tool for your organization:
Why-use-Python-for-Data-Science
  • Simplicity

The first thing that comes to mind when one is talking about Python in the data science community or even programming in general, is its simplicity. Python’s inherent simplicity and readability makes it a beginner-friendly language. Since a user does not need to learn a complicated syntax in order to understand Python, it offers a significantly shorter learning curve than any other language. Learning to write a program in Python is considerably faster than writing one in C++ or Java.
  • Libraries

Python’s vast collection of the most useful libraries is what makes it so appealing to data science professionals. These magnificent libraries have only improved over time, and even today, continue to upgrade. Its assortment of libraries is so vast that you can be sure that you will find one that is tailor-made to suit your particular needs. PyTorch, tensorFlow to name a few.
  • Multi-Paradigm Approach

One of the best things about Python is that it is a multi-paradigm programming language. This means that unlike other object-oriented programming languages, Python is not limited in the variety of approaches that it can take to solve the same problem. For instance, if you want to print “Hello World” in Java, you would be required to create a separate object-oriented class. Whereas in Python, there is no need to do so. Since it uses a multi-paradigm approach, Python supports functional, procedural, object-oriented and aspect-oriented programming approaches. 
  • Enterprise Application Integration

Python can be easily embedded in applications, even those written in other languages, it is an excellent tool for Enterprise Application Integration (EAI). The ease of integrating it with other languages significantly speeds up the web development process. Its strong integration bonding with Java, C, and C++, also makes it a fantastic choice for application scripting. Python also comes with a unique unit testing framework that makes it an ideal choice for the development of sophisticated GUI desktop applications.
  • Community

The extensive community of Python developers is one of its most significant advantages that is often overlooked. There exists no problem or question that will not be answered by enthusiasts and volunteers in the Python community. All you need to ask a question on one of Python’s open-source communities and you will always find a bunch of people who are open to discussion. 

Why Mindbowser for Data Visualization

Our experience and agile team of full-stack engineers, data scientists, and mobile app developers accelerate innovation and implementation of customizable ML and AI products. Our experts bring vast cross-industry expertise supported by scientific rigor and in-depth knowledge of advanced techniques to design, develop, and deploy bespoke Artificial Intelligence solutions.

Subscribe to our newsletter

   
   
Related Posts

Leave a Comment

Authentication-using-AWS-Amplify-and-Cognito