The Seven Stages Of Visualizing Data

June 15, 2017 By 0 Comments

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.

  1.  Acquire
    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.
  2. Parse
    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.
  3. Filter
    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.
  4. Mine
    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.
  5. Represent
    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.
  6. Refine
    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.
  7. Interact
    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
In a typical case study, Sujeet Enterprises Ltd., an international brass handicrafts exporter from New Delhi wanted to offer an insight into the growth of its business in South American nations during the past five years in its annual meet. The data analyst of the company first acquired the data of its international sales during the past five years, and parsed it on the basis of nations, in order to categorize the data according to different continents. Afterwards, it filtered the data to retrieve the sales information about South American nations only; and further mined this data by devising a few mathematical formulae that were applied upon relevant datasets. The data executive decided bar graph would be the most appropriate form of chart for displaying the data analysis, and sought help some graphics design artists and web designing experts for lending visual appeal to the visualization. Finally, an expert in Javascript helped the executive to turn the data visualization into an interactive dashboard that was ready to offer any minute details about the sales figures pertaining to any South American nation made by Sujeet Enteprises Ltd. during the past five years, and the goal was successfully achieved.

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