Data Visualization framework is the buzzword for any and every business activity that actually matters these days. And that’s what makes it imperative for any serious business executive to learn a few ropes in its arts and crafts. A variety of approaches, methodologies and solutions for data visualization collectively comprise a framework. And it takes an amount of patience, dedication and interest to delve deeper into the subject matter.
In order to attain a sound understanding of a data visualization framework, a few core steps are involved.
How to Identify the Information to be Represented
Before anything, it is important to figure out what sort of information you need before you conduct any research. Methods for getting information can take any form: surveying thousands of customers, obtaining user data from an API or conducting in-person interviews. More often than not, this happens to be the most time-consuming part of the process.
Learn the Craft of Data Clean-up
Prior to analyzing the data, it is important to perform a clean-up upon the raw data to make it more analysis-friendly. During the clean-up process, poor quality responses need to be weeded out it might be necessary to change the scope of the project. Further, the prototype of a data visualization design must be created with real data, so that it can be implemented perfectly when needed.
The Art of Visual Design
In data visualizations, visual design should emphasize clarity and readability above all else. Practically, it means that instead of complicated visualization concepts, basic and convenient graphs and charts must be used. Though it doesn’t mean the design should be dull at all. Aesthetic choices must be used to indicate value and colors may be used as an added layer of meaning, to indicate intensity.
How to Develop Interaction
It is important to learn how to make various elements of data visualization interact with each other. So to offer a flexible and rapid access to the desired information. One vital aspect is to learn how to show a macro view of all of the information, yet show the details of each subset pertaining to the same. This is a central struggle of data visualization, as the best pieces let the user easily access the macro and the micro.
Data Visualization Framework, Conclusion
The conventional practice of data mining sits on the top of data infrastructure and receives exceptional preference over others. The reason being its over-the-time validation of future predictions and profitability. Visualr, a data visualization software manifests equal caliber to dig into your data to ensure business growth.
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