Visual Discovery Element in Data Visualization
Visualization does not only have to be the creation of charts and objects which can help ease interpretation but it should be informative and convenient. According to Bernato (2016), convenience in data visualization can be a perfect replacement for good. Bernato offers four components of visualization which include idea generation, idea illustration, visual discovery and everyday DataViz. Visual discovery helps in answering various statistical questions such as whether the visualization objects created are exploratory or data-driven (Few, & Edge, 2007). The visual discovery generally implies the steps and processes needed to develop data visualization which is not only going to help declare a given stand but to be able to help in discovering some trends such as the Key Performance indicators (KPIs).
Visual confirmation usually seeks to answer some questions on whether what is suspected is actually true. It touches on various common ways of presenting data such as the use of spreadsheets and paired analysis. Visual exploration touches on the open-ended data which needs further additional processes to ensure that data makes sense. Commonly applied in analytical programming and furthering business intelligence.
Visual discovery also aids in breaking down data into various components which can allow for comparisons and decision making based on the performance of various sections. For instance, It is more informative to break sales figures into the region and compare them to see the performance of each region other than just communication overall quarterly sales (Berinato, 2016). This process is motivated by the need to make visual discoveries. The element which breaches the data-driven and exploratory visualization is the data discovery.
Most of the Data scientists usually use complex statistical models and charts to depict trends, patterns, and anomalies. According to Berinato (2016), these visual objects automatically change as new data are added. The exploratory research will use visual discovery to make segmentation and make possible groupings to for easy comparisons and analysis. Visual discovery is not necessary depicted in charts but done behind these objects to develop other objects to be used in the next steps.
References
Berinato, S. (2016). Visualizations that really work. Harvard business review, 94(6), 18.
Few, S., & Edge, P. (2007). Data visualization: past, present, and future. IBM Cognos Innovation Center.