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Big Data Visualization: Tools and Challenges

Media theorist John Berger believes that people think in pictures. For him, seeing comes before words. He states:

“Unless our words, concepts, and ideas are hooked onto an image, they will go in one ear, sail through the brain, and go out the other ear. Words are processed by our short-term memory where we can only retain about 7 bits of information (plus or minus 2). This is why, by the way, we have 7-digit phone numbers. Images, on the other hand, go directly into long-term memory where they are indelibly etched.”

Big data visualization is a technique based on visual elements like charts, graphs, and maps that represent complex concepts and data in a way that becomes easier to analyze and decipher. The method adds value to your data by removing the noise from data and highlighting useful information (like trends, outliers, and patterns).

If you’ve ever stared at a massive spreadsheet of data and couldn’t find a meaningful pattern, you know how much more effective a visualization can be.

In the world of big data, it is important to analyze massive amounts of information and make data-driven decisions.

This is where data visualization tools and technologies come into play.

Big data visualization is the key tool to make sense of the trillions of rows of data that you generate every day.

However, not all data visualization techniques prove to be effective.

Not all of them can help tell stories. Traditional elements like plain graphs could be too monotonous to make a powerful point.

So, what is effective data visualization then?

It is a delicate balancing act between form and function.

Combining data and visuals is no less than an art. If you want great analysis combined with great storytelling, your data and visuals need to work together.

How big data visualization works

There are studies by psychologist Albert Mehrabian that indicate that language is decoded on a linear level, while visuals are deciphered on a simultaneous level.

This means that an image can be analyzed instantly, while language requires time to analyze. Data visualization is the technique that cuts to the chase, allowing faster analysis of critical information.

Data visualization solutions help companies spot trends and patterns, and track business performance and goal achievements.

Plain graphs are only the tip of the iceberg.

There’s a whole selection of big data visualization methods that help present data in interesting ways. You must combine the right visualization method with the right set of information.

Big data visualization works by enabling and empowering decision-makers at every level of your organization to see and analyze unstructured or unorganized data presented visually with the help of several figurative approaches and methods.

Quality Assurance Service

Data visualization allows handling tons of data by converting it into meaningful visuals using widgets and elements. For this, the best software tools are used to operate various types of data sources.

From politicians to sports enthusiasts, journalists, engineers, and accountants, the application of data visualization is evident in our modern world.

Some of the data visualization techniques used to put together information in a visual way include infographics, heat maps, scatter plots, fever charts, connectivity charts, timelines, treemaps, histograms, and area charts, among others. The choice of technique should depend on the type of data being modeled and the intended purpose.

Big Data Visualization Tools

Big Data is not a new concept. It has existed for decades. It’s just the size of the data that’s new today.

Can you guess the amount of data we’ve generated in just the last 2 years from different sources like mobile devices, computers, and other web-connected devices?

It’s a zettabyte of data!

It technically means that every 2 years, we create as much data as we did from the beginning of time or at least 90% of all the data in existence till today.

That’s a lot of data!

Let’s say you are a proud owner of a diamond mine, but you can’t harness the diamonds from that mine.

Is there a point in being the owner? No, right?

It’s the same with big data.

There is no point in collecting large chunks of data if you fail to grasp the information and insights lying beneath it. Data visualization tools help resolve this issue by showing us valuable hidden insights into the collected data.

Some popular big data visualization tools that will help you make the best visuals of your data in the most time-efficient manner:

Tableau

An end-to-end big data visualization tool that enables you to prepare, analyze, collaborate, and share your big data insights. The platform excels in self-service visual analysis. It helps data-driven companies to see and understand their data, and create workbooks, visualizations, dashboards, and stories.

QlikView

This self-service BI (Business Intelligence) or data visualization tool enables you to work adeptly on the tool without relying on your IT department, with little to no professional expertise. The data security provision of the tool is stringent in the sense that it guarantees the safety of critical corporate data.

PowerBI

This cloud-based big data visualization tool requires no capital expenditure or infrastructure support regardless of how big your business is. It integrates easily with your existing business environment, giving you exceptional analytics and reporting capabilities. Moreover, Power BI ensures that your data is quickly retrievable by removing memory and speed constraints.

Spotfire

This is probably the most complete big data visualization solution on the market that enables you to uncover and visualize discoveries in your data through immersive dashboards and advanced analytics. Its analytics capabilities include predictive analytics, geolocation analytics, and streaming analytics. The tool’s rich API capabilities let you analyze all the data needed for the most powerful insights.

D3js

D3js is the big data visualization tool that brings your data to life using HTML, SVG, and CSS. It combines powerful visualization components and a data-driven approach to DOM manipulation. The JS framework and functional style that it has allowed you to make it as powerful as you want to make it.

HighCharts

This insights platform lets you create customized dashboards with different widgets. With this big data visualization tool, you can easily create an interactive chart that uses the data from the HTML tables and presents it more appealingly. HighCharts solely run on native browser technologies and there are no plugins required. It won’t be an exaggeration to say that they are the future of data representation in an approachable way.

Conclusion

By now, you’d know that we are hard-wired to find emotional cues within visuals rather than text.

Data visualized properly leads to information and knowledge. Visualized data has more value because it has been transformed into information. On consuming this information, it becomes knowledge.

“Identifying patterns, anticipating outcomes, and proactively optimizing a response will be the basis for competition in the future. In the next 10 years, the companies that don’t have analytics deeply embedded in their business model will most likely cease to exist.” – Gartner

Your business data can communicate critical insights if it’s visualized the right way.

Jellyfish Technologies will help you reimagine your unstructured or unorganized business data on interactive dashboards easily with interactive data visualization services.

FAQs

1. What are some popular big data visualization tools that help make the best visuals of your data?

Ans: Tableau, QlikView, Power BI, Spotfire, D3js, and HighCharts are some of the most popular big data visualization tools that help make the best visuals of your data in the most time-efficient manner.

2. Name some data visualization techniques.

Ans: Some of the data visualization techniques used to put together information in a visual way include infographics, heat maps, scatter plots, fever charts, connectivity charts, timelines, treemaps, histograms, and area charts, among others.

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