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Three Reasons Why Visual Discovery Falls Short:

Business Needs More than Visualization for Analytics

Visual Discovery Technology Issues

Visual discovery enables organizations to visualize large volumes of data to find areas of opportunity and challenge. It provides a more intuitive representation of data than numbers in rows and columns. Visual discovery thus is an important addition to the business analytics toolbox.

Today, new visualization approaches allow users to move beyond pie charts and bar graphs to more advanced visualizations such as heat maps and scatter plots that can render data in multiple dimensions. Moreover, in-memory processing technology enables exploration of data without having first to model data relation-ships. For these reasons, visual discovery tools have become popular among line-of-business analysts and knowledge workers who find they are able to explore various data sources and information without involving the IT department.

However, visual discovery technologies have three serious shortcomings. The first is a lack of governance and quality control, the absence of which can lead decision-makers to distrust the basis of the insights provided to them. The second problem is that most visualizations of data are not designed not for the average business user who needs fast access to metrics on performance in his or her business area. The presentation of data through visualization is best designed for analysts and data scientists trained to understand data, interpret different types of visualizations and derive insights. Finally, visual discovery technologies can provide a good first step for exploring data, but to build analytic workflows for data blending or predictive analytics or to provide enterprise-level reports, additional software is needed.

 

 
 

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