Chapter 1 – Introduction
The first chapter introduces the problem space in terms of making sense of very large, complex datasets and outlines the vision for visual analytics. Extracts from this chapter:
We are living in a world which faces a rapidly increasing amount of data to be dealt with on a daily basis. In the last decade, the steady improvement of data storage devices and means to create and collect data along the way, influenced the manner in which we deal with information. Most of the time, data is stored without filtering and refinement for later use. Virtually every branch of industry or business, and any political or personal activity, nowadays generates vast amounts of data. Making matters worse, the possibilities to collect and store data increase at a faster rate than our ability to use it for making decisions. However, in most applications, raw data has no value in itself; instead, we want to extract the information contained in it.
Time and money are wasted, scientific and industrial opportunities are lost because we still lack the ability to deal with the enormous data volumes properly. People in both their business and private lives, decision-makers, analysts, engineers, emergency response teams alike, are often confronted with large amounts of disparate, conflicting and dynamic information, which are available from multiple heterogeneous sources. There is a need for effective methods to exploit and use the hidden opportunities and knowledge resting in unexplored data resources.
The overarching driving vision of visual analytics is to turn the information overload into an opportunity ….. the goal of visual analytics is to make our way of processing data and information transparent for an analytic discourse. The visualisation of these processes will provide the means of examining the actual processes instead of just the results and ultimately improve of our knowledge and our decisions.
On a grand scale, visual analytics provides technology that combines the strengths of human and electronic data processing. It is highly interdisciplinary and combines various related research areas such as visualisation, data mining, data management, statistics and cognition science (among others). …. Because visual analytics is an integrating discipline, application specific research areas can contribute existing procedures and models …. The requirements of visual analytics introduce new dependencies between these fields.
Did you know:
An important step towards what we now call visual analytics was stated in the statistics research community by John W. Tukey in his 1977 book, Exploratory Data Analysis?
The first use of the term visual analytics was in the IEEE Computer Graphics and Applications journal article by Wong and Thomas in 2004 …. entitled ‘Visual analytics’.
Characteristics of visual analytics applications were already apparent in earlier systems, such as Minnie, an interactive electronic circuit designer created by Bob Spence in 1968. [View a short video of Minnie from 1973]