Data Visualization Theory and Data Compression

Information, in a broad sense, is systematically structured, processed and organised information. It gives context to diverse data and allows effective decision making by individuals or companies. For instance, a single customer’s sale at a certain restaurant is data, which become information when the company is able to associate the most popular or least common dish with it. Decision making through information is an important process and is at its best when the process of information transfer is both quick and effective. The ability to process large volumes of information quickly and effectively will help the company to provide a fast service as well as reduce costs associated with doing the same.


Another key concept is that meaning arises from data, which can be both continuous and discontinuous. Ordinal data refer to those that repeat themselves. Continuous data on the other hand, refer to those that change (for example prices or stock prices) and thus require a process of analysis or aggregation to determine which changes affect a product’s value. In order to create a better understanding of how and why companies use information visualization tools, it is necessary to understand the different types of information used and their required definition. There are four main categories:

The four main types of information may also be classified by the methods of storing and accessing them. The meaning may also be determined by the way in which information is created, managed and used. This article briefly outlines the main categories of data visualization and provides an overview of how the theory of meaning relates to the field of data compression.