Why Data Visualization Requires Planning
Every business sector now is going through a paradigm shift, thanks to the data analytics revolution. Powered by a host of powerful data analysis tools and D3 data visualization solutions, companies of all sizes are adopting visual representation of data as an effective tool for gaining better insights into their business activities. Business organizations in increasing numbers have now begun imparting extensive data visualization training to their executives, still there remains a lot that needs to be shared with them about planning the data visualization. While a host of free online presentation tools make it easier for companies to execute visual presentation of data, the process of planning data visualization enables them to carry out the exercise in a streamlined and optimal manner. A sensible planning of data visualization makes it possible for an enterprise to leverage maximum potential out of minimum technical resources. Apart from that, it also ensures a clear picture of the message that needs to be sent across is etched out in a lucid and easy to understand visual format so that every concerned person can grasp its essence and offer his or her valuable inputs.
Steps In Data Visualization
Broadly speaking, the process of data visualization entails five elaborate and well defined steps. Let’s have a look upon them, one by one.
In this step, the entire datasets are obtained or fetched from a variety of sources. The source may be a file or a disk or a networked system. Tables generated after conducting surveys or study reports also constitute datasets to be acquired for data visualization.
Most often, the raw datasets are unorganized and unstructured which are difficult for analysis and visualization. Therefore, the data needs to be structured and ordered in categories first. This process is also known as parsing. Structuring and organization of data may be done on the basis of tags, indices and names.
Now arrives the next step, in which the data that is unnecessary with respect to current point of view for analysis and visualization is filtered out. The data to be filtered out may be crucial with respect to the point of view for some other data analysis or visualization; therefore it must not be assumed it is less important.
The filtered data that needs to be analyzed is now mined by applying various mathematical and statistical formulae upon it. This process converts the filtered data into variables denoting values or quantities that we exactly need to analyze and display through data visualization.
After the data is mined successfully, you need to decide the visual format which will be most appropriate for visualizing it in a clear and concise manner. For example, you may wish to select the bar graph for representing a certain data set, while pie graph may be more appropriate for representing a different data set.
The data to be represented through charts, reports and dashboards needs to be made visually more engaging, captivating and enriched. For doing the same, various graphic design tools and technologies are used. Online reports, charts and dashboards usually bank upon technologies such as HTML5, CSS and SVG for achieving the purpose.
An essential quality of digital dashboards and charts is interactivity. Users must be able to select varying data ranges, time intervals and chart forms for analyzing a dashboard according to multiple viewpoints. A number of graphics and programming tools and technologies are extensively used for making the reports and dashboards interactive.
A Case Study
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