Analyzing Data Visualization Software
90 percent of the transmitted information in the human brain is visual. Visualization is an important aspect of our lives and is highly applicable to the business world, which relies on Big Data. To derive meaningful insights, data scientists have now resorted to data visualization—the modern equivalent of visual communication as it involves the creation and study of visual representation of data.
Data visualization has become an integral part of Business Intelligence (BI) systems. Forrester Research goes as far as to claim that an agile BI wouldn’t be complete without data visualization capabilities. By incorporating Data Visualization, reports become more detailed and informative. They are also easily understandable as graphs and charts provide great details in a very simple manner. Simple, well defined, appealing and informative, data visualization is the answer to procuring valuable information and details, and number crunching or so it seems.
Although data visualization has made life better for the data scientists and their organizations, it has its own drawbacks. Displaying the statistics in forms of graphs maybe a great way for people to understand data but in case that data perceived and collected turns out to be wrong then the damage and the outcome can devastating—chaos of the highest order (in terms of data). When choosing data visualization software, it is important for an organization to introspect and understand their requirements and select the software that satisfies their needs. For example, an organization using a BI tool requires only simple bar and pie charts, and need not spend on visualization software. Such simple elements are already incorporated within the BI tool. When an organization requires more complex features such as providing support for mapping geospatial data, more specialized data discovery tools or visualization-specific software is required.
When companies decide to install visualization software, it is highly recommended to opt for the ones that are simple to use even for a layman. It should be capable of deeper analysis of visualized data, support for displaying visualizations on a variety of mobile devices through HTML5 interfaces, and provide functionality for creating tree-maps, bubble charts, infographics and other types of visualizations. The software should further be able to shift between visualization types and table-based representations as and when required. Another feature that can be considered is the software’s connectivity to various data sources like the NoSQL databases and cloud-based systems. This way any faulty data or value can be removed and replaced at any point in time.
While installing Visualization software, one always has to consider the aspect of speed. Rapidly accessing the required information from the enormous amount of data available often rises with the increase in the degree of granularity. Using increased memory and powerful parallel processing is one way to tackle this problem. The organization can also consider putting data in-memory by using a grid computing approach, where many machines are used to solve a problem.
Regardless of the speed at which the data is served up to the user, the quality of the data has to be of the highest order. The value of data for decision-making purposes will be jeopardized if the data is not accurate or timely. Having a good Data Governance or information management process in place to ensure the data is clean will help maintain the quality of the organization’s data.
Understanding the data is an important aspect of data visualization. Analyzing who the target groups are and recognizing what one is trying to visualize out of the data is critical. Domain expertise is key in eliminating or reducing this problem. Furthermore, displaying meaningful results is the main objective of visualization. Clustering data with similar and relatively close values would be helpful in creating a better plot—be it any graph or chart.
Building data visualization software must go to great lengths not only to link to the sources and fully explain any caveats relating to the data or graphic itself, but should also state the degree to which their work should be taken as scientific fact. Before releasing any report, the public must be made aware of certain statistics which may have the probability of deviating from the original prediction.
As the world becomes increasingly interconnected and interdependent, opportunities to generate value through data visualization will only increase. With both technical and non-technical professionals producing and comprehending insights from Big Data, Visualization tools can help deduce information better. Although these data visualization software maybe great tools in providing the facts and statistics that make life easier for everyone, a deeper introspection is definitely required before integrating them into an enterprise system.
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