Big Data Visualisation

With all this data and fancy data mining and data analysis techniques, how can the human at the end of the pipeline make any sense of it? Computers may be able to understand 1’s and 0’s but humans require a bit more imagination. This is where data visualisation comes in. Data visualisation is the process of taking raw information from the Big Data processes and creating graphical representations using things like charts, graphs, and maps. These techniques are essential for making sense of the overwhelming amount of data and information and being able to take action on the insights and predictions from the Big Data process. There are many techniques used in data visualisation such as:

Maps – These come in many forms such as geospatial maps for integrating geographical data with analytical data, heat maps for showing a simpler view of large and complex amounts of data using density and magnitude, and tree maps for showing the part-to-whole relationships within a dataset. (1)

Graphs – Graphs are good for showing trends and relationships within data sets. They are a versatile tool that can show a range of information with more specific graphs such as stream graphs showing changes in data overtime.

Charts – Charts come in many forms such as line charts used to connect data points through straight lines to illuminate trends, patterns, and variable changes. Bar charts are the most common kind of chart and visually represent data in bars or columns to show proportionality of data to compare differing values of categories or groups. (2)

 

1. https://www.geeksforgeeks.org/what-is-big-data-visualization/

2. https://www.thoughtspot.com/data-trends/data-visualization/types-of-charts-graphs




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