Data Visualization is an integral part of Data Analysis but Not Synonymous with

As Data Visualization is an integral part of Data Analysis and no data analysis can be successfully possible without the use of appealing visualizations, Data Visualization and Data Analysis are often the most confusing terms among the learners. Learners aspiring to develop a career in Data Science often find confusing between a Visualization expert and a Data Analyst. As visualizations go hand in hand with analysis and an analyst job also contain the role of making effective visualizations, Data Visualization and Data Analyst are often taken synonymous with each other. For all those whose mind contains such confusions, this article has been articulated for the same purpose. But Before digging deeper into the differences separating Data Visualization and Data Analysis and understanding why should we not taken Visualization experts as Analysts, let us have a quick look at what a Data Visualization and Data Analysis is.

What actually is Data Analysis?

The analysis of user data gathered after long processes of cleansing data to find out useful insights from it is Data Analysis. Data Analysis is a thinking and analytical process in which Analysts analyses a particular amount of data to find out the hidden patterns underlying. These hidden patterns are important for any business as these contain useful answers to the business challenges. Patterns could be of anything, say from the growth rate of the sales team over two years or the profit business has achieved over the past years.

What is Data Visualization than?

Data Visualization is the visualization of data in appealing visual graphics. These visualizations can either be done after data cleansing to help the analysts find meaningful information out of the data or the visualizations can be done after finding meaningful insights, to present the insights before the other business department or the stakeholders. Visuals are often easier to study and so Data Visualization is an integral part of Data Analysis.

What is the thin line between Data Visualization and Data Analysis?

You can take A Visualization as a simple visual depicting some information, say for example a visual depicting the sales of the company for consecutive 2 years or the number of cars that are manufactured by a company for 2 years. See the following typical visual depicting information about some aspect of a company.

Now take the following visual.

Can you take the difference between the two?

The first image was a simple visualization of some typical information while the second one contains the visual as well as some information mentioned on it. This is the work of a Data Analyst and this is where Analysis differ with that from a simple visualization. An Analyst uses visuals to show the relevant information because he also knows that his the audience is more inclined towards seeing visuals. But apart from just showing the information on the visuals he also takes care of the information being provided to his audience. He imparts the insights he has gained from the data to his audience and does the additional work from a Visualization expert �?? �??Analyzes the data�??. While a Visualization expert is all busy in choosing out the best visual for depicting the necessary information, Data Analysts analyses the information and presents it before its potential audience.

Can Data Visualization and Data Analysis be separated from each other?

Have you handled a big dataset? Have you felt the difficulty lies in understanding every single amount of data lying in text and numbers? If yes, then you must have felt the need for a Visualization system which can represent the long and tiresome data into an appealing visual format which is easy to study and as well as interesting to explore. If you have not dealt with any big dataset till now, then you must try handling one. The need for a visual will be felt by you in the first run only.

Visualizations are so important for any analysis of data:

It reduces the amount of information lying in pages to a compact visual, which is obviously quick to grasp.
It also prevents the loss of important information that could be forgotten by us while analyzing big datasets without the use of a visual (either we just save the important data somewhere else, but that too takes a lot of time).

Source: HOB

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