Visualization: An important aspect of data analytics

“Scientists are considered artists and vice-versa.” I read this some time back and at that time I could not understand why so. Today I realize why. Look at the five pictures (taken from various sources on internet) below and each represents a different kind of data representation. If stored in text or in an excel format, they will still be in a form where all will look almost same. However, below visualization gives a meaning to each data type and thus makes them more understandable. 

Visualization is an important and a crucial aspect of Big Data Analysis. When we use the term Big Data, it is just not the huge size of unorganized data, but also the complexity involved, that adds to the difficulty. Very popular 4Vs of big Data – Volume, Variety, Velocity, and Value – indeed make the data completely undecipherable. One can imagine how a raw data pulled from Twitter or Facebook will look like. It will make no sense and the entire data will not even fit in one visible screenshot.

Visualization plays a very important role in such situation. How the data and trends will be represented in a way which makes the whole analysis or the final statement very clear is what most of the visualization tools try to achieve.[ for details of some tools please visit http://blog.profitbricks.com/39-data-visualization-tools-for-big-data/ ] Although these tools are highly sophisticated and can decide by default, which graphic to be picked up for showing a specific analysis, however the data analyst’s imagination and experience can add the perfect seam to it.

With time, visualization has evolved. From simple write-ups to blogs and infographics to interactive visualization techniques, all are helping the research author to explain idea and hence the final statement very crisply. The graphical aids have added another dimension to the whole analysis write-up. This extra dimension will definitely make the analysis more understandable.

Visualization is where the engineering meets the scientific analysis. The junction has to be very strong, appropriate and in-accordance with the goal of analysis.