Importance of Data-Centered-Design in Visualizations.
Tech is revolutionizing the way we interact with almost everything around us. The rate of growth of the information domain is exponential and is nearly explosive. The importance of information is very dear to the Massive topmost fortune companies as well as a single individual. Users of the Internet generate approximately 2.5 quintillion bytes of data each day, on average as of August 2017. Out of this data, the insights derived each minute has the potential to turn the tables.
Someone once said that best results come out of the merging of several domains. That is why almost every product developed these days is a result of interdisciplinary processes. I personally am an experienced individual in the fields of Data-Science, Machine Learning and Design. Design is something that could be incorporated into almost everything to make it more visually accurate and appealing.
There's this thought which recently was biting my mind. We as Data Scientists and Machine Learning Engineers generate a lot of meaningful insights from Complex Datasets daily. This derived information from raw facts and figures is often just represented in tabular form or in the form of paragraphs (Text Strings). Though Mathematical data can be represented in the form of graphs and chart-models, still this representation is vague sometimes. Data-Centered-Design is a recently coined phrase that has gained much popularity due to the realization of the indispensable need of design in the visualization of data.
Since past few decades, Data Visualisation has gained a stature where it is recognised as an effective tool for both storytelling and analysis, overcoming most educational, language and demographic barriers. Visualizations can convey information more effectively and impactfully to the diverse set audiences as compared to traditional methods.
Why is this so? How are abstract shapes and colours able to communicate large amounts of data more effectively than a table of numbers or paragraphs of text? An understanding of human perception answers this question well and also provides clear guidance and tools for improving the design of your data visualizations.
With an understanding of human perception about data models and visualization, one can
- Prove — that the prepared visualisation clearly represents the story which the original data told.
- Reveal — future opportunities/scope or incorrect practices, discover new patterns and trends, prediction of unknown quantities.
- Improve — your design of visualization by adding objectivity and user-surveys.
As a designer, the main aim must be to create a visualisation that provides a summed whole of the entire story. If a picture tells a thousand words, those thousand words must not miss a single instance of the entire data-scene. Data comes from people and the information derived from that data is for the people. The main point here is that obtaining data from people is easy as compared to explaining it to the people. Even the slightest rounded-corner of the rectangular bar in the Bar-chart makes an impact on the viewer's mind. The colour scheme of the visualisation also plays a key role. The elements of distraction such as the superfluous gridlines or ticks or legend must be avoided wherever possible. The purpose of such entities is only to specify something that is not obvious to the viewer.
Choosing the right kind of visualisation model is the most important of all the decisions to be made in prior. There have been several cases where intentionally or otherwise the incorrect visualization has been employed to convey information inaccurately. This is not ethical or justified in any manner. The information must not be modified in order to manipulate the perception of the viewer towards unimportant facts.
