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What Is Data Visualization and Why It Is Important?

Data visualization is an essential step in the analytics life cycle. Before we dive into the topic, let us check the sales and profit data in different regions for various products.

Looking at the data for table ‘Sample – Superstore,’ can you answer this question: “Which region has the highest profit for Technology and the lowest profit for Furniture”?

Using just numbers from the data set, getting to the correct answer is very daunting.

For instance, answering this question using a visual would be relatively easy and give us the correct answer. (This was created using Tableau.) Visually, we also understand the profit variation across regions by looking at the graph.

Take your analysis to the next level by seeing How do you use Data Visualization in Tableau.

profit variation across regions
Figure 1

A large amount of abstract information from excel is also displayed in this graph. Still, we can easily and quickly grasp it because the brain processes visualizations at a much faster speed. When we look at visuals, it holds our attention longer, and we process much more information than raw text. Our brains can process data much faster using colors and known patterns in order to draw accurate data insights and trends.

What Is True About Data Visualization?

Terabytes and petabytes of data generated every day in order to be analyzed to get insights to be valuable. But, can you imagine scrolling through this massive amount of data and trying to find trends and patterns? As a result, With the overwhelming volume and rate at which data is growing, it is almost impossible to do it without visual help. Every data set tells us some story, but we need practical tools to find and communicate the story’s purpose with the stakeholders.

Visualization refers to techniques that help us graphically visualize data to find out trends, communicate insights and understand complex relationships between variables. 

Why Is Data Visualization Important?

  1. Quick and Easy: Visuals communicate easy-to-understand information precisely.
  2. Accurate Insights: As we saw in the above example, visuals give us accurate insights from the data. It helps us form correct decisions. 
  3. Faster Decision Making: Visuals help us to look at a massive amount of data in a small space, map relationships across variables, identify patterns, highlight outliers, and much more which, leads to faster and accurate decisions than scanning rows and rows of data.
  4. Dynamic Updates: Instead of creating more and more charts and graphs, we can dynamically change the data selected and see how it changes the graphs by making the visual interactive. 
  5. Spotting Errors: Visuals help identify errors in the data that can be removed to get the right visuals.
  6. Drill up and Drill down: It gives users the control to look at the overall picture by drilling up or go deeper and explore by drilling down.
  7. Applicable Across Various Sectors: Visuals provide valuable inputs in many different fields: business, crime management, healthcare, education, public sector, and many more.

How To Create Good Charts?

  1. The visual must be clear, simple, intuitive, aesthetic, and should highlight patterns. 
  2. Use metrics that businesses can understand.
  3. Understand the business goal: Once the business questions we are trying to answer using the visuals are clear. we can create correct graphs displaying the right trends to answer the questions.
  4. Identify the visual: declarative or exploratory.
declarative or exploratory charts

5. Choose a compelling visual: Once we know the goal, choosing the correct chart or graph is the key to display information from data accurately.

Examples of Data Visualization in Action:

  1.  Line Chart: It views data trends over time, as sales over the past few years, as shown in Figure 2.
  2.  Bar Chart: It compares data across categories like the popularity of books based on the number of books read or the chart in Figure 1.
popularity of books for data
Figure 1
Figure 2

3. Eliminate Unwanted Parts: Reduce the font size, arrange or remove labels and borders. It shortens names on axes, remove extra colors, and fine-tune other aesthetic factors.

4. Focus on the Correct Part: By choosing the required variables only, focus on the question you want to answer. It shows using a visualization. Remove extra variables.

5. Form a Compelling Story: A story is a sequence of visualizations that work together to convey information. An exciting story should tell a data narrative, provide context, demonstrate how decisions relate to outcomes and make a compelling case.

To summarize, data visualization forms an integral part of the analytics life cycle. It enables -a business to identify trends and patterns and use data to benefit the company.

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