Importance And Benefits Of Data Visualization

We generate data through all of our actions not only from anything we do on the internet but also from anything we do in the offline world. The data we collect is in either numerical or textual format though, making it difficult to understand and find trends until it has been converted to visual forms such as charts or plots. This is where Data Visualization comes in.

In this blog, we’ve covered the benefits of data visualization Services, its uses for businesses and a step-by-step data visualization process.

What Is Data Visualization?

Data visualization is a method that uses visuals, both static and interactive, to help people understand the large amount of data being collected. Data visualization is an important skill in applied statistics and machine learning.

It can be helpful when you need to get information from some datasets; the information we can mine from datasets can be about finding patterns and around identifying outliers, and much more. With some prior domain knowledge, visualization can be used to find relationships between the data, which can be insightful to you and your audience.

Why Python For Data Visualization?

Python has evolved extensively in every field, be it Automation, Machine Learning, Testing, Scraping etc. Python has grown into a large community which is further fueling the growth with new contributors and ecosystem.
Python has many visualization tools/libraries which provide excellent features and are easy to implement. It includes support for all types of visual, live, customized charts.

Worth mentioning, below are some of the most used python libraries for data visualization:

  • Matplotlib: It is a low-level library, which provides lots of freedom to customize.
  • Pandas Visualization: Built on Matplotlib, It has an easy-to-use interface and makes visualization a breeze.
  • Seaborn: It has a high-level interface, and also has great default styles.
  • Bokeh: Supports unique visualizations like Network graphs, Geospatial plots, etc.
  • Plotly: It can create interactive plots.

Step-By-Step-Data Visualization Process

To get the visualized data, you need to follow the below-mentioned steps:
Data Visualization Process | Mindbowser
  • Collecting Data

The first and most important step of data visualization is gathering data in large amounts. Only after we have substantial data, we can apply data visualization techniques to the collected data and get some helpful insights from it.

  • Clean Your Data

Data cleaning is an essential step to perform before creating a visualization. A bunch of data out of a large dataset that has inappropriate, empty or false values may lead to adding erroneous visuals with anomalies in it.

The output received from a data cleaning process is usually a dataset that is free of errors and anomalies etc, which gives much more accuracy when data is processed. Data cleaning is pretty much dependent on the dataset domain that you’re working with.

  • Choose A Chart Type

Before choosing a visual chart or graph, it is important to understand your audience and then choose a chart or graph accordingly which will best communicate the message.

Choosing a chart totally depends on what findings you need to convey to your audience.

  1. Do you want to show how the merging data columns can give meaningful insights?
  2. Do you want to show some data patterns from the datasets?
  3. Do you want to show how data variables are compared to each other?
  4. Do you want to show the relationships between the data variables?

Choosing a couple of these can help to select the charts that will be best suitable for you. This usually requires some playing around with different charts before choosing the best.

  • Prepare Data

To prepare the data before sending it further for visualization is to determine the type of graph, chart or any other visualizations you need to create and the supporting library you will be integrating for it. After the chart is finalized it may be necessary to transform the data as per requirements.

Data preparation tasks include finding data columns that help make some decisions out of it, giving some meaningful insights about data, grouping data, creating aggregate values for groups, combining variables to create new columns, etc.

Meet Our Tech expert

Sandeep Natoo

Sandeep is a highly experienced Python Developer with 15+ years of work experience in developing heterogeneous systems in the IT sector. He is an expert in building integrated web applications using Java and Python. With a background in data analytics. Sandeep has a knack for translating complex datasets into meaningful insights, and his passion lies in interpreting the data and providing a valuable prediction with a good eye for detail.

Get Free Consultation
  • Visualize Data

In the final step, you’ll have the required data you need to create visualizations. Now you can apply all your visualization skills to the prepared data and represent the data in charts or graphs with meaningful insights.

Types Of Data Visualization Charts | Mindbowser

 

Now that we understand how the data visualization process works, we can now apply different data visualization types to their uses. As mentioned in the earlier section by using those visualizations libraries, we can create some visualizations as follows:

  • Line Chart

Line charts are used to display trends over time. The X-axis is usually used to represent a period, and the Y-axis is used to represent the quantity associated with the time period on the X-axis. For e.g: an A-line chart can illustrate a shopping mall’s peak visit time for the day broken down by weekdays and hours.

  • Area Chart

An area chart is a line chart with the areas below the lines filled with colors. Use a stacked area chart to display each value’s contribution to a total over some time.

  • Bar Chart

A bar chart also displays trends over time. In the case of multiple variables, a bar chart can make it easier to compare the data for each variable, at every moment in time. For e.g, a bar chart can be used to compare the company’s growth year-wise.

  • Histogram

A histogram represents data using bars of different heights. Usually, each bar groups numbers into ranges in a histogram. Taller the bars, the data falls in that range. It is used to display the shape and spread of continuous data set samples. For e.g, we can use a histogram to measure each answer’s frequencies in a survey question. The bars would be the answer: “bad,” “good,” and “best”.

  • Scatter Plot

When there is a need to find the correlations, Scatter plots are used. If there exists a data XY, then a Scatter plot is used to find the relationship between variables X and Y.

  • Bubble Chart

The bubble chart evolved from a scatter plot. Where unlike scatter plots each data point is assigned a label or category and shown as a bubble. It is used to show and compare the relationship between the labeled circles. A bubble chart makes it hard to read the chart with multiple bubbles, so it has a limited data set size capacity.

  • Pie Chart

A pie chart is a circular graph representing the data set in which the slices of pie are divided to represent a numeric proportion. Pie charts are used when there is a need to show the contribution of a data point inside a whole data set.

  • Gauge

A gauge chart is evolved from a pie chart and doughnut chart. It is used to visualize the distance between intervals. Multiple gauge charts can be shown linearly to visualize the difference between multiple intervals.

  • Map

Most of the data collected have a location variable, which makes it easy to plot on a map. An e.g, a map visualization, is mapping the number of customers all over the world country-wise, where each country would represent a number of customers. Location information can help businesses to grow their business in a particular region where the business has not scattered compared to other regions.

  • Heat Map

A heat map is a visualization tool that uses color the way a bar chart uses its height and width. Two dimensions are shown as a magnitude of a phenomenon. The heat map illustrates it can be used to identify whether the phenomenon is clustered or varies over space.

Benefits Of Data Visualization

It is difficult for humans to understand the data in numeric format because of its complexity and large amount of data. That’s where data visualizations come into the picture as it makes it easy to understand the data, and it allows the decision-makers to act more quickly.

  • With the help of data visualization, decision-makers can easily understand how the data is being interpreted to determine business variations.
  • A large amount of data is handled and is visualized to establish patterns in the data. Many meaningful insights and the evidence behind the data can be used to establish a business goal.
  • Visualizing the data helps managers to achieve growth and use the new pattern trends found in business strategies.
  • Data analysts’ job is to make it easy to make new decisions for business development and expansion by using trends from the data with the help of healthcare data visualization.

Why Mindbowser?

Our experience and agile team of full-stack engineers, data scientists, and mobile app developers accelerate innovation and implementation of customization 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.

mindbowser-data-science

coma

Conclusion

In this blog, we’ve covered the benefits of data visualization, its uses and its importance to businesses. The blog can be a start to help you decipher How we can implement Data Visualization and which are the most useful strategies to achieve data visualizations.

Shubham

Tech Expert

Shubham is a Lead Python developer with 3+ years of experience. He is very passionate about his work. He is always eager to learn new programming skills and technologies and looking for new ways to optimize the development process. His areas of expertise are in Building Machine Learning models, Creating REST APIs in Django/Flask, Web Scraping and Writing Automation Scripts for businesses.

The complete guide on "Data Science" is released - Get your copy and learn the trends of Data Science in 2022 :)

Download Free eBook Now!

Get in touch for a detailed discussion.

Hear From Our 100+ Customers
coma

Mindbowser helped us build an awesome iOS app to bring balance to people’s lives.

author
ADDIE WOOTTEN
CEO, SMILINGMIND
coma

We had very close go live timeline and MindBowser team got us live a month before.

author
Shaz Khan
CEO, BuyNow WorldWide
coma

They were a very responsive team! Extremely easy to communicate and work with!

author
Kristen M.
Founder & CEO, TotTech
coma

We’ve had very little-to-no hiccups at all—it’s been a really pleasurable experience.

author
Chacko Thomas
Co-Founder, TEAM8s
coma

Mindbowser is one of the reasons that our app is successful. These guys have been a great team.

author
Dave Dubier
Founder & CEO, MangoMirror
coma

Mindbowser was very helpful with explaining the development process and started quickly on the project.

author
Hieu Le
Executive Director of Product Development, Innovation Lab
coma

The greatest benefit we got from Mindbowser is the expertise. Their team has developed apps in all different industries with all types of social proofs.

author
Alex Gobel
Co-Founder, Vesica
coma

Mindbowser is professional, efficient and thorough. 

author
MacKenzie R
Consultant at XPRIZE
coma

Very committed, they create beautiful apps and are very benevolent. They have brilliant Ideas.

author
Laurie Mastrogiani
Founder, S.T.A.R.S of Wellness
coma

MindBowser was great; they listened to us a lot and helped us hone in on the actual idea of the app.” “They had put together fantastic wireframes for us.

author
Bennet Gillogly
Co-Founder, Flat Earth
coma

They're very tech-savvy, yet humble.

author
Uma Nidmarty
CEO, GS Advisorate, Inc.
coma

Ayush was responsive and paired me with the best team member possible, to complete my complex vision and project. Could not be happier.

author
Katie Taylor
Founder, Child Life On Call
coma

As a founder of a budding start-up, it has been a great experience working with Mindbower Inc under Ayush's leadership for our online digital platform design and development activity.

author
Radhika Kotwal
Founder of Courtyardly
coma

The team from Mindbowser stayed on task, asked the right questions, and completed the required tasks in a timely fashion! Strong work team!

author
Michael Wright
Chief Executive Officer, SDOH2Health LLC
coma

They are focused, patient and; they are innovative. Please give them a shot if you are looking for someone to partner with, you can go along with Mindbowser.

author
David Cain
CEO, thirty2give
coma

We are a small non-profit on a budget and they were able to deliver their work at our prescribed budgets. Their team always met their objectives and I'm very happy with the end result. Thank you, Mindbowser team!!

author
Bart Mendel
Founder, Mindworks
coma

Mindbowser was easy to work with and hit the ground running, immediately feeling like part of our team.

author
George Hodulik
CEO, Stealth Startup, Ex-Google