Connect with us

Big Data

What is Data Visualization?

Which image gives you a better idea of the data represented? Most people would get a clear view of the second image. This is something data visualization is all about. It is merely the representation of data in graphical form.




What is Data Visualization

“There is magic in graphs. The profile of a curve reveals in a flash a whole situation — the life history of an epidemic, a panic, or an era of prosperity. The curve informs the mind, awakens the imagination, convinces.” – Henry D. Hubbard

Whenever you are given a lot of information, you may be converting it into tabular form to get an easy understanding. Data is essential only when you can get insights from it, and else it is redundant.

Consider the two images shown below:

Importance of Data Visualization How to Make a Chart or Graph in Excel Source

How to Make a Chart or Graph in Excel Source

Which image gives you a better idea of the data represented? Most people would get a clear view of the second image. This is something data visualization is all about. It is merely the representation of data in graphical form.

If we have a small data set, understanding it in a tabular form is secure. But if the amount of data is enormous, assuming it becomes too complicated, hence the need to visualize it. By using representations like charts, graphs, or maps, you get an accessible way to understand the trends and patterns in the data.

It is a personal tendency to get attracted to colors and patterns rather than standard text. The human brain is quick to differentiate between green and red, or square and circle. Visual effects create a better impression on our minds.

For example, a process explained through animations is understood quicker compared to that described in a book. This is why data visualization is essential for people as well as businesses.

Today, companies believe in making data-driven decisions as it allows them to meet customer needs and develop best-in-class products.

Rising technologies like Artificial Intelligence, Machine Learning, and Data Analytics have made data visualization more critical than ever.

Data visualization helps in converting trillions of rows of data generated into a form that is easy to understand, and through which we can find growth patterns, profit/loss, Return on Investment (ROI), total sales, and a lot more information that can be conveyed to stakeholders.

Read on to know why data visualization is essential, its types, data visualization tools, and why taking Tableau course training should be your next career move.

1. Importance of Data Visualization

Whether you are working in retail, banking and finance, marketing, sales, education, eCommerce, or even in governmental organizations, data visualization is crucial to communicate information clearly and efficiently. It is also one of the steps of the process of data science.

The data science process involves collecting data, processing, and modeling it, and after that, the data should be visualized to draw conclusions and make better decisions.

Data Visualization has witnessed continuous growth over the past few years. The global data visualization market that was worth USD 4.51 billion in 2017 is expected to reach USD 7.76 billion by 2023, at a Compound Annual Growth Rate (CAGR) of 9.47 percent over the forecast period of 2018 to 2023.

The driving factor of data visualization is the fact that there is an increasing emphasis on measuring the performance of every operation to check the daily progress.

2. Here are some of the points depicting the importance of data visualization:

Traditional descriptive statistics are not capable of giving insights from the massive amount of data that we generate today. It is a data visualization that gives improved insights.

Going through spreadsheets or reports is a tedious task which delays decision-making. Data visualization allows for quicker decisions by presenting complex data through interactive dashboards and graphs.

Data visualization can also be used to identify areas that need improvement, understand which factors affect consumer behavior, predict sales volume, and more.

Companies today are focusing more on big data and data analysis related projects, and data scientists take the help of data visualization to ensure that the process is giving desired results.
Data visualization also gives an increased understanding of the next steps to be taken to improve the processes.

3. Types of Data Visualizations

Data can be visualized in various ways:

  • Charts
  • Tables
  • Maps
  • Infographics
  • Graphs
  • Dashboards

These mentioned ways again have sub-types like line charts, pie charts, treemaps, histograms, bullet graphs, dot distribution maps, heat maps, population pyramids, Gantt charts, and more.

4. Data Visualization Tools

Data scientists can achieve data visualization through many powerful tools like:

  • Tableau
  • Qlikview
  • Plotly
  • Power BI
  • Datawrapper
  • Highcharts
  • Sisense

Tableau is one of the numerous robust data visualization tools which can prove to be the most secure and flexible end-to-end analytics platform for your data. Here are the products that it offers:

Tableau Prep – It can be used to quickly combine, shape, and clean data and make it ready for analysis

Tableau Desktop – This is where the analytics happen. There are interactive dashboards to help you uncover hidden insights.

Tableau Online – This analytics platform is cloud-based, and you can access it anywhere or share the dashboards with stakeholders or clients.

Tableau Server – It allows the organization to run the tool in a trusted environment where the data and analytics are secured, and employers have the freedom to explore data.

Now that you know why data visualization is essential to get ready to learn it. If you continue beginning on a career in the field of Artificial Intelligence, Machine Learning, Data Science, or Big Data Analytics, then it is a must-have skill.

You can take a Tableau online course to gain hands-on experience of this powerful tool. Industry experts often make the online courses, and they not only train you to design interactive dashboards but will also help you visualize data more effectively.

We are an Instructor's, Modern Full Stack Web Application Developers, Freelancers, Tech Bloggers, and Technical SEO Experts. We deliver a rich set of software applications for your business needs.

Continue Reading
Click to comment

Leave a Reply

Your email address will not be published. Required fields are marked *

Artificial Intelligence (AI)

Why AIOps Needs Big Data and its Importance in Business

AIOps is an Artificial Intelligence used for IT Operations. It mainly uses Analytics, Machine Learning (ML), and Big Data to automate IT




The Role of AI in digital marketing Artificial Intelligence (AI) Marketing, Business


The current IT environment has evolved to a point where old, manual methods were not sufficient to keep up with today’s needs. Increasing complexity, the need for quick solutions, and the massive size of data in IT operations require AIOps to function smoothly.

1. What is AIOps?

AIOps is an Artificial Intelligence used for IT Operations. It mainly uses Analytics, Machine Learning (ML), and Big Data to automate IT operations and produce results in real-time. It is an essential tool for monitoring and managing IT Operations.

If issues in digital services are not quickly detected and resolved, business operations will be negatively affected. Customers will not have a satisfying experience. To avoid this situation, AIOps must be implemented.

AIOps does an algorithmic analysis of all the data and helps the IT Operations and DevOps (Development Operations) teams to identify and resolve issues with high speed. AIOps prevents outages, reduces downtime, and provides seamless services. AIOps can give better insights as all the information is centrally stored in one place.

2. Aspects of IT Operations monitoring using AIOps:

a. Data Selection

The modern IT environment generates massive amounts of heterogeneous data. For example, event records, metrics, logs, and other types of data from different sources like applications, networks, storage, cloud instances, etc. This data is always high in volume, and the majority of it is redundant. AIOps use entropy algorithms to remove noise and duplication.

Leveraging the Power of AI for Digital Asset Management

b. Pattern discovery

Selecting meaningful data and grouping them by correlation and identification of the relationship between them by using various criteria. These groups of data can be further analyzed to discover a particular pattern.

c. Inference

Recurring problems are analyzed, and root causes are found. Identifying such issues makes resolving them more comfortable and quicker.

d. Collaboration

AIOps tools help in reporting to required operators for collaboration without any mishaps, even when these operators are in different departments or different geographical locations.

e. Automation

Automation is the heart of the AIOps. When the business’s infrastructure continues to grow and multiply, AIOps helps automate all the business processes. It helps in storing data centrally, auto-discovering, and mapping the infrastructure, updating databases (CMDB), automating redundant tasks and processes. Thus, leading to agile and efficient IT and business operations.

3. What is Big Data?

Big Data is a high volume of structured and unstructured data that is generated by businesses at high speed at varying veracity.

It systematically extracts meaningful insights from this data to make better decisions and strategic business moves. With the advent of digital storage in the year 2000, data creation increased as digital storage was cheaper than analogue storage. DVDs made data sharing easier.

As institutions like universities, hospitals, businesses started using technology, the amount of data created went through the roof. This resulted in two problems.

What Does a Data Center Technician do

a. The rigidity of relational data structures

This was solved by using Data lakes. The data lake is a centralized repository which allows data storage of all the structured and unstructured data (usually files or object blobs) at any scale and makes it available for analysis.

b. Processing queries in the relational database has scaling issues.

When queries were processed in a single queue, it was time-consuming. The use of Massive Parallel Processing (MPP) resolved this technical issue.

Hadoop 1.0 is an open-source software framework. It was implemented using data lakes and MPP. Apache Hadoop facilitated the use of big data in all organizations. Hospitals, Scientists, and businesses used big data to analyze large sets of data and derive valuable insights quickly.

Hadoop 1.0 had few drawbacks. Optimization of data was complicated. The organization had to employ data scientists to get the required insights.

The introduction of Hadoop 2.0 resolved those issues and further commoditized big data. Hadoop 2.0 also enabled the use of AIOps.

4. The necessity of big data for AIOps

AIOps can function only with big data as older datasets are small and inefficient.

Hadoop 2.0 had a YARN feature that supported data streaming. It also enabled interactive query support. It allowed the integration of third-party applications.

This means that analytics could be improved, but only if it was re-architectured. Organizations without data science resources still had difficulty in optimizing and using Hadoop for better data analytics.

The requirement for more purpose-built and easy use solutions brought companies like Logstash, Elastic, and Kibana to the market. They replaced Hadoop in a few use cases.

Guide to Pursue DevOps Agile Development Cycle

5. What does this mean for your business?

This is important for both Core IT Operations and Service Management because they both rely on an interactive query and streaming data technology.

The Digital Transformation of organizations elevated the need for IT solutions. IT had to deal with increasing complexity, massive data size, and speed.

Transition by upgrading or re-architecture method to support Big Data was also tricky due to purpose-built applications, and the data remained in silos.

AIOps makes Artificial Intelligence take over manual analysis. Data from all the silos form the dataset. Interactive solutions are designed from both technical and usability perspectives.


IT operations need to work on diverse data, analyze real-time streaming data, identify and automate workflows, derive meaningful insights, and support historical analysis. All this requires businesses to build a Big Data backend on the AIOps platform.

AIOps initiative must not be built with a traditional, relational database. AIOps improves the functionality of IT operations. Hence, we can say that AIOps needs Big Data to function efficiently. Also, businesses and corporations who need to store large amounts of data will need AIOps to function correctly, automate tasks, obtain insights, and work efficiently as per the trending demands from end-users.

Continue Reading


Copyright © 2021 | All Rights Reserved by TwinzTech