Data Visualization

Data Visualization can be described as the transformation of a set of data into diagrams, graphs or any other forms that aids in projecting major findings that are extracted from the dataset. The field can also be considered as a right blend of science and art. It involves a great responsibility of presenting data in a simple and appealing manner without compromising the clarity of the same. In today’s blog we will address some important aspects to consider in the process of visualizing a data.

For creating an effective representation of data, first of all we must understand the data and pick the trends or patterns that the dataset exhibits, followed by this, we must select an exact tool that would communicate the trend to the target group (audience) in a simplified manner. For example, for exhibiting a profit of a company, usually we prefer a line graph.

One important aspect in designing the graphs is the choice of colours. Always we must use simple and aesthetically appealing colour pallets to visualize graphs. And always use colour blind friendly pallets.

Always try to create graphs that is proportion to the data values, that is try to reduce the Lie factor. Lie factor can be defined as the ratio of the size of effect in graph to the size of effect in data. And this value should always be 1. Any value above 1 is highly misleading and wrongly represented.

Clear Mapping and Labelling should be done to help a clear understanding of the data. And never quote data out of context as this would delimit the quality of the data visualization.

Apart from the above things always try to maintain low data ink ratio.  Data ink ratio can be defined as the ratio of data ink to the total ink used to print the graphic. It can also be described as the proportion pf the graph’s used to the non-redundant display of data-information. This helps in generating better visualization graphs that is aesthetically appealing and effectively communicating.