Data Visualization

Data visualization provides a quick and effective way to communicate information in a universal manner using visual information. The practice can also help businesses identify which factors affect customer behavior; pinpoint areas that need to be improved or need more attention; make data more memorable for stakeholders; understand when and where to place specific products; and predict sales volumes.

Other benefits of data visualization include the following:

• the ability to absorb information quickly, improve insights and make faster decisions;
• an increased understanding of the next steps that must be taken to improve the organization;
• an improved ability to maintain the audience's interest with information they can understand;
• an easy distribution of information that increases the opportunity to share insights with everyone involved;
• eliminate the need for data scientists since data is more accessible and understandable; and
• an increased ability to act on findings quickly and, therefore, achieve success with greater speed and less mistakes. 

The increased popularity of big data and data analysis projects have made visualization more important than ever. Companies are increasingly using machine learning to gather massive amounts of data that can be difficult and slow to sort through, comprehend and explain. Visualization offers a means to speed this up and present information to business owners and stakeholders in ways they can understand.

Big data visualization often goes beyond the typical techniques used in normal visualization, such as pie charts, histograms and corporate graphs. It instead uses more complex representations, such as heat maps and fever charts. Big data visualization requires powerful computer systems to collect raw data, process it and turn it into graphical representations that humans can use to quickly draw insights.

While big data visualization can be beneficial, it can pose several disadvantages to organizations. They are as follows:

• To get the most out of big data visualization tools, a visualization specialist must be hired. This specialist must be able to identify the best data sets and visualization styles to guarantee organizations are optimizing the use of their data.
• Big data visualization projects often require involvement from IT, as well as management, since the visualization of big data requires powerful computer hardware, efficient storage systems and even a move to the cloud.
• The insights provided by big data visualization will only be as accurate as the information being visualized. Therefore, it is essential to have people and processes in place to govern and control the quality of corporate data, metadata and data sources. 

The science of data visualization comes from an understanding of how humans gather and process information. Daniel Kahn and Amos Tversky collaborated on research that defined two different methods for gathering and processing information.

System 1 focuses on thought processing that is fast, automatic and unconscious. This method is frequently used in day-to-day life and helps accomplish:

• reading the text on a sign;
• solving simple math problems, like 1+1;
• identifying where a sound is coming from;
• riding a bike; and
• determining the difference between colors.

System 2 focuses on slow, logical, calculating and infrequent thought processing. This method is used in one of the following situations:

• reciting a phone number;
• solving complex math problems, like 132 x 154;
• determining the difference in meaning between multiple signs standing side by side; and
• understanding complex social cues