Data Visualization in the Age of Big Data

Bharath Mundlapudi, Co-founder, President & CTO, Orzota

Headquartered in the US, Orzota is a big data solutions company that provides technology enabled services.

The old cliché - 'A picture is worth a thousand words' applies to big data. Studies show that the human brain processes images 60,000 times faster than text, signifying the importance of Data Visualization. It is one of the critical components if not the most important component of any big data implementation. Data visualization can be used for delivering traditional business intelligence reports, tracking organizational KPIs, and to communicate insights gleaned from the data. Big data implementation is a journey that can be mapped into a four step process: 
Step 1- Define clearly your Infrastructure Strategy.
Step 2- Select right Big Data Technologies
Step 3- Integrate right data from various sources
Step 4- Perform data analytics and visualization

Data visualization is the last step. If you look at the idiosyncrasies of big data visualization, you will notice that big data visualization is kind of a misnomer. Plotting the whole big data can be too noisy, slow and challenging due to technology limitations - moving data to the target device (browser, mobile or tablet etc). To solve this problem new approaches and techniques are required.

With the advent of big data, a few new use cases are evolving for data visualization. These are the new drivers for big data visualization. Few such requirements for big data visualization are:
1. High speed data: visualize high velocity data in real-time. 
2. High volume data: visualize huge volume data

To achieve the above requirements, traditional visualization tools don’t cut it. These visualization drivers require new hardware capabilities (like large RAM, Multi-core CPU, in some cases GPU etc) in addition to store, organize and process big data for efficient data visualization.

Visualization of large datasets will be hard for human eye. Even if we present such visualization with large dataset, this will be too noisy for the humans. Another problem related to this is computational complexity in moving large data to the target device for rendering. This will be very slow. 

The above challenges are driving new opportunities - algorithms, efficient hardware, and commoditization of RAM, new ways to visualize information like graphs, temporal and hierarchical and last but not least are the delivery platforms like cloud and mobile playing a crucial role. All these approaches require a new technique for data visualization called interactive analytics. With interactive analytics, you can ask questions, touch and feel the data, and collaborate and brainstorm with teammates. 

To conclude, data visualization is hot and requires new interactive analytics approaches in the age of big data. Look out for new tools in this space which are either too generalized that can do many things like charts, trends, or specialized that can do few specific things like graphs, collaborative, and interactive on large data sets. Pick the right tool based on your requirement and use case.