Sunday, September 19, 2021

Big Data Visualization: The Ultimate Interpreting Heritage

Introduction:

With so much information and data available, it becomes difficult to understand complex concepts. But with big data visualization, we get an opportunity to represent the difficult stats and facts into easy formats. But before we begin with the introduction of data visualization, let us first introduce you to its basics- Data & Big Data!


What is Data?

Data refers to a set of facts and statistics that are being collected together for analysis or reference purposes and can be transmitted or used in the form of electrical signals.


What is Big Data?

Going forward, big data is a form of data that is already huge in size and is yet growing significantly big with time. It’s data that is so big and complex that the traditional data management tools cannot store or process it efficiently.

big-data-visualization
Big Data


So now that we know what data and big data are, let’s move towards the elephant in the room that is “Big Data Visualization”.


What is Big data visualization?

Big data visualization refers to the easy and clear interpretation and presentation of complex data with the help of graphics. However, data visualization is far ahead of those traditional histograms, graphs, or pie charts. Rather the graphical visualization of big data includes advanced forms of data representation such as Fever charts, Heat Maps fever, Polar Area Diagrams, and so on.

The purpose of visualization is to enable decision-makers to establish a relationship between unknown patterns. And a big goal is to make any kind of complex data easy for people to understand. With graphical visualization of images and data, everyone can grasp the most difficult concepts with minimal effort.


Importance of Big Data Visualization

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Importance of Data Visualization Image Credits: Great Learning

The pictorial and graphical images speak much better than words. For Big data and data analysis, Data visualization is very crucial. Most business companies use machine learning to store data to represent data so that any person can and understand a large bulk of data in just one picture, Data visualization plays a key role.

Your data needs to be visualized in case of anything like finance, marketing, research, technology. Even a common man who looks at the graph as a result of data interpretation is able to analyze business patterns and trends, retrieve and use all these data insights for employee performance, customer journey, making appropriate decisions, and improving one’s business. Following are the salient features of Big data visualization:

● The large amounts of data are compressed into a pictorial format to provide a summary of the whole content.
● It makes data more understandable: representing Data in the form of tables and text involves a long time to view and understand the trends but the graphical form makes easy and quicker perseverance of data.
● The Easy pictorial format helps the decision-makers understand how the business trends are happening to make good business decisions.
● The graphical format reveals previously unnoticed key points that help to build data analysis reports.
● The patterns and trends help to make predictions.


How Data Visualization Works?

The vast amount of the company’s data was challenging to record, store, understand, and analyze. But data visualization techniques paved the way to overcome all these difficulties by just representing them in pictures.

The visual representation of this data can be used to analyze and integrate it into a business decision. Charts act as a communication medium, every representation has a meaning, the structure, the color, even the look also mean something. There is an intentional meaning, for every pictorial it gives a clear idea about the audience. The main feature of Data visualization is to create an impact.

The Data Visualization tools like SAS, Tableau are used to generate automatic dashboards to track company performance, check overall business. Online data visualization tools have made it possible to comprehend the information presented quickly and easily.


Examples of Big Data Visualization


There are many ways to use big data visualization to simplify difficult data for the viewers. Here are few examples of big data visualization that would help you understand the concept better:



1. Making history easy with Node links.

Understanding and learning history has always been a challenge for most of us. From hard-to-pronounce names of places and their rulers to so many dates and complex chronology, node links have turned as the best way to simplify everything for students as well as historians.

With node links, one can easily write the important dates, names, and places and link the detailed explanation with the help of node links.



2. Using visualization to decode election results.

Heatmaps are a leading example of using visualization for data representation during elections. Since the inception of heatmaps, they have always been used to represent the state division between the Republican and The Democratic Party in the USA.



3. Bar Chart Races:


Using bar chart races is another example of visualization. The bar charts help to represent sports data in an engaging way.



Types of Big Data Visualization


By now we have understood data representation, its importance, and how it works! Now let us move to the next section that is types of big data visualization.

Here are the 5 types and examples of Big Data Visualization:


1) Hierarchical

Hierarchical data visualization refers to ordering sub-groups within larger groups. It is the most suited type of data visualization to represent cluster data that flow from a similar origin point.

The aim of hierarchical visualization is to simplify the complexities of critical information flow. One common example of hierarchical data visualization is a family tree.

Hierarchical big data visualization uses tree diagrams, ring charts, and sunburst diagrams to represent the data.


2) Network:

Network big data visualization is a form of data representation that demonstrates relationships between complex facts and statistics without any wordy explanation.

Node-link diagrams are the most used form of network data visualization. However, this kind of visualization also includes alluvial diagrams, word clouds, matrix charts, etc.


3) Temporal:

Data that is linear and one-dimensional in nature comes under this category. This kind of data visualization is represented with the help of lines that are either drawn alone or overlap one another, with a definite start and finish time.

Temporal data is easy to understand. Even the most complex data can be understood by everyone with the help of temporal big data visualization.

The temporal big data visualization includes: timelines, scatter plots, time sequence, line graphs, polar diagrams, and other methods of linear representation.


4) Multidimensional:

As the name suggests, this type of visualization includes data representation which has multiple dimensions. In multidimensional big data visualization, there are always two or more variables included. Multidimensional visualization is capable of breaking down difficult concepts into key takeaways. Also, this is proven to be the most attractive form of data visualization.

Multidimensional data visualization uses histograms, Venn diagrams, Pie charts, Bar graphs, etc. to represent the data.


5) Geospatial:

This kind of data visualization includes the demonstration of geographical data and physical locations that are otherwise difficult to understand.

Geospatial data visualization is commonly used during political campaigns, concept mapping, or to display market penetration.

There are different types of data Visualization. The aforementioned includes density maps, heat and flow maps, cartograms, etc. to decode the data.



Top 7 Tools of Big Data Visualization

big-data-visualization
Data Visualization Tools

So after knowing about big data visualization, its importance, and types, are you also planning to leverage it to simplify the complex data for you or your team? If yes, we are now going to tell you about the top tools of big data visualizations. These tools will help you use big data visualization more effectively.

Scroll down to have a look at the top big data visualization tools to decode clustered data:


1. Infogram:

Infogram is one of the most popular infographic tools. It features more than 1 million images to make big data visualization easier for everyone.

big-data-visualization
Infogram



-Infogram features:
● Multi-format visualization exports
● Real-time Data import
● Themes And layouts can be customized.
● Multi-format visualization exports.
● Engagement
● Drag And drop options

Pros:
● Outputs from the infogram are mobile-friendly.
● There are multiple program formats in infogram.
● Infogram provides an intuitive interface which makes it easy to get started..
● A wide range of charts, timelines, graphs is available in Infogram.

Cons:
● Infogram is not that friendly with big data.
● Page sizing is a bit fiddly.



2. Tableau

Tableau is a perfect tool to combine data from multiple sources. This tool is equipped with advanced features that let you customize designs and allows you to work naturally the way you think.

big-data-virtualization
https://www.tableau.com/en-gb/products/dashboard-startersTableau Software



Tableau Features:
● Provides collaborations and sharing.
● Offers Data sources.
● Robust security.
● Advanced visualizations.
● A Mobile version is available.
● Ask Data option.
● Predictive analysis.
● Revision history.
● Metadata management.
● Third-party integration.

Pros:
● Tableau quickly creates data visualizations.
● Without having proper knowledge of coding one can learn tableau easily.
● Tableau can handle large data.
● Tableau allows customized dashboards for mobile and laptops.

Cons:
● Limited 16 column table displays and conditional formatting.
● Only single values can be used with a parameter.
● The screen resolution on the tableau gets distorted a little bit.



3. Tibco Spotfire

It is another analytics tool that deep dives into the clustered data to give you quick insights. This tool works perfectly on Cloud and Desktop.

YouTube video



-Tibco Spotfire Features:
● Big data analytics
● Predictive analytics
● Data discovery and visualization
● Content analytics
● Dashboard
● Analytic applications
● Advanced collaboration tools
● Event And d location analytics

Pros:
Tibco Spotfire provides many visualizations.
● Tibco allows flexibility to cater to users need
● Offer third-party tools.
● It allows to access many users over the network with a single database.
● Tibco has a cross-analysis function that helps to robust enough.


Cons:
● Sometimes it takes a long time to create pleasing graphs.
● Sometimes lack of embedded BI and production reporting.
● Limited functionality to personalized visualization.



4. Plot.ly

Plot.ly is a Quebec-based advanced visualization tool. One can easily make interactive charts and dashboards to simplify the complex data.

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Plotly



-Features:
● File exports
● Open sources
● Slide check
● Snapshot engine
● Image storage
● Application manager
● Kubernetes
● Dashboards
● Authentication
● Embedding

Pros:
● Plot.ly is very user-friendly.
● Plot.ly provides improved productivity as it speeds up work and avoids delay.
● Plot.ly is packed with high-power tools.
● Helps the users to experience a customized product.
● All kinds of tools are available even it is for enterprises or solo practitioners.

Cons:
● 40 lines of code don’t allow to make proper use of plot.ly.
● Unique layouts are not intuitive for Plot.ly.
● Pip installation is not applicable for Plot.ly, it requires an API key and registration to start.

5. QlikView

This popular data visualization tool belongs to the US-based software company Qlik. It is one of the rapidly growing data visualization tools which is easy to use. It simplifies data to understand, easing the complicated job of decision making.

YouTube video



-Features:
● Mobility and scalability
● Data collaboration and connectivity
● Agile application development
● Natural analytics and Data discovery
● Interactive guide analytics

Pros:
● It allows quick and efficient data interpretation.
● It helps in real-time data sharing and helps in collaborations.
● It requires low maintenance.

Cons:
Qlikview has less RAM limit.
● End-user application is difficult.
● Old functions are not that updated with a recent program that makes a bit disadvantageous.



6. Candela:

Here’s another efficient tool for visualization. It allows the users to make scalable, rich visualizations with advanced tools.

-Features:
● Flexible to use.
● Comes with millions of designs.
● Allows customization in the templates.
● Complete bar code enabled software.
● User access security management.

Pros:
● High-quality used cosmetic laser make visualization quick.
● Flexible customization.

Cons:
● Not efficient for auto-visualization.



7. Chart Studio

Last but not the least, Chart Studio is an open-source visualization tool. The free community allows you to access public charts, dashboards, attractive visualizations, and other advanced features.

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Chart Studio



-Features:
● Use drag-and-drop interface
● Works with SQL
● Secure to work online

Pros:
● Efficient for creating D3.js and WebGL charts
● Allows to create your own components

Cons:
● Not so good with linear visualization

The best part about these big data visualization tools is that they are easily available as open-source big data visualization.

Also read our related article: 21 Big Data tools that will lead your business to the next level in 2021


FAQ SECTION

1. Which data visualization tool is best?

Well! Which would be the best tool for data visualization for you totally depends on your needs and requirements. Although there are various tools available online that help you with efficient visualization of data. So choose the one which suits the best to your needs.


2. What is good data visualization?

Data visualization tools are the easiest method that helps to understand even complex data in an understandable way. A good data visualization must provide two aspects of the data while presenting, first
it should be able to explain the complex data connections which are not that easy to express with wordy explanations; & second, it should be able to make the audience understand the information provided quickly and also the outcomes from the given data.


3. What is the easiest data visualization tool to use?

It is totally up to the requirements of those using data visualization. The easiest visualization tools include Google Chart, Tableau, Grafana, Datawrapper, and so on. So it is totally up to the users.

4. What is the most popular form of data visualization?

Data visualization is the way of representing data in graphical representations. Some of the most common data representations include column charts, bar graphs, pie charts, area charts, bubble charts, and many more, even though they all work to demonstrate the data but each one has a different use. A column chart is considered the most useful and easily understandable way of data visualization.


5. Is data visualization hard?

Data visualization is the easiest method to make you aware of the information, even though it is not that easy to create one. It takes lots of research and creativity. There should be a right balance between the elements. The user must be careful while using it to avoid certain mistakes.

6. What are the benefits or advantages of data visualization?

Data visualizations have so many benefits, that’s why it has high acceptance. Data visualization allows to identify the errors easily and make the information understandable. Data visualization also helps to analyze the data better, and give the desired action fast, as the human brain understands the visualization of data faster than wordy explanations


7. How does data visualization improve decision-making?

Data visualization allows even the ordinary person to understand the complexity in a very easy manner. The combination of data and technology helps the observer to understand and identify the winning formulas, pinpoint areas with great potential. Also, it helps to figure out the weak spots and helps to avoid mistakes. With all these features, data visualization improves decision-making.

8. How do I learn Tableau, from 0 to 10, in a few months?

There are so many features that make tableau easy for the users. One should not need to be an expert in the technical areas, even an ordinary person can handle tableau. It has a tableau dashboard that allows a wholesome review of your data. It also has a collaboration and sharing option which helps to securely share the data. Data sources in the tableau can easily establish a secure connection and also give the most relevant data to our queries.



Conclusion:

Now we all know that humans are visual learners, therefore all information must be organized for better understanding and good outcomes. Almost 26% of companies use Data visualization and decisions from these data lead to revenue growth and have a planned investment.

Data visualization helps identify errors, risks, places that need attention to improve and clarifies which factors influence customer behavior. Through such a concept, tools, and advancements, the users can also understand which products to place where and predict sales volumes. Thus this is one of the most trending interpretation methods!


Big Data Visualization: The Ultimate Interpreting Heritage

I am pursuing an MCA (MASTER OF COMPUTER APPLICATION) from IGNOU. I did my BSc honors degree from Kanpur University. I also have a certificate of “fundamental of digital marketing” by Google and “fundamental of remote sensing” by IIRS. I am keenly interested in the Data Science and data analytics field. That is why I write articles on this kind of topic. I have been working as a blogger for Edu Data Online web since January 2021.

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I am pursuing an MCA (MASTER OF COMPUTER APPLICATION) from IGNOU. I did my BSc honors degree from Kanpur University. I also have a certificate of “fundamental of digital marketing” by Google and “fundamental of remote sensing” by IIRS. I am keenly interested in the Data Science and data analytics field. That is why I write articles on this kind of topic. I have been working as a blogger for Edu Data Online web since January 2021.