Why Visualization is the most significant “V” for Big Data?
Big Data Analytics is the analysis of huge data sets with large volume, variety, and velocity. But as the term says, the information extracted from it will be large in size which is not beneficial in decision making. Big data Analytics should be focussed on understanding the relationships between people and processes and then defining patterns that will lead to outcomes that are user specific and determined. Data Visualization helps in identifying the data that is important to produce graphs and charts that are relevant to get insights from Big Data.
Challenges with Big Data Visualization are Visual Noise, Information Loss, High rate of Image change or Data Change and Performance Requirements- Scalability.
Some probable solutions are:
- Veracity: Reliability of the data sets is important as the analysis will not yield good results if the integrity of the data is questioned. Data Visualization helps in checking the quality of data following data governance. It also helps to deal with outliers- to remove them or to highlight them using another chart.
- High-Performance Requirements: Increased memory and powerful parallel processing can be used for high dimensional data. By performing Interactive Visualization: selection, linking, filtering and rearranging or remapping, Big Data dashboards can be used to display meaningful results.
The most effective Big Data Visualization techniques with their Big Data class are:
- Treemap and Circle Packing: Applicable to hierarchical data.
- Sunburst: Volume & Velocity.
- Parallel Coordinates: Volume, Velocity & Variety.
- Streamgraph & Circular Network Diagram: Volume & Variety.
Data Visualization is the most significant way Big data will be accessible to large and wider audience and will be essential to transforming analysis and reporting to effective decision making.
Source: http://pubs.sciepub.com