What is Visual Data Analysis?
Sometimes, data can be overwhelming. There’s too much of it, too little time to comprehend it, or you simply can’t see the data you have available at your disposal. If so, visual data analysis can help you make sense of it all, by combining data analytics and data visualization techniques.
Data analytics alone can be powerful. However, it can be difficult to see the big picture or how one set of data relates to another. Visualization tools by themselves may make static mashups and presentations of data easy to grasp. Yet they may lack the ability to drill down, tweak or explore. Visual data analysis brings you the best of both worlds.
See it in action:
Components of a Visual Data Analysis Solution
A visual data analysis solution will have an interface, often an interactive dashboard on a screen, for users to select sources of data and choices for displaying the data. Data display options may range from basic line, bar, and pie charts to more sophisticated gauge indicators, scatter charts, and tree maps. Ideally, the solution should:
- Need only little or preferably no coding
- Make it simple to locate and bring in data from different sources
- Offer graphics that are easy to customize
- Make it easy to drill down to the underlying data at any level of detail
- Make it possible to combine multiple views for at-a-glance, overall understanding
What is Visual Data Analysis Best Used For?
Visual data analysis makes it easier for human beings to understand data. Broad relationships and patterns can be brought out, as can emerging trends. An interactive dashboard can also be a great tool to explain the story about the data to others, and for answering their questions about the data and possible insights as they think of them.
There is another very useful side to visual data analysis too. Visualization helps you quickly narrow your search for information of interest. You can then apply data analytics algorithms to the datasets for a thorough analysis and report. Visualization also lets you apply common sense to check intermediate results. You catch any mistakes earlier. By toggling between data and analytics and visualization, you can home in faster on datasets of interest and insights of value.