Visual Analytics: Definition, Process, and Challenges - Information Visualization: Human-Centered Issues and Perspectives - page 155

Due to information overload, time and money are wasted, scientific and in-
dustrial 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 massive amounts of disparate, conflicting and dynamic infor-
mation, which are available from multiple heterogeneous sources. We want to
simply and effectively exploit and use the hidden opportunities and knowledge
resting in unexplored data sources.
In many application areas success depends on the right information being
available at the right time. Nowadays, the acquisition of raw data is no longer
the driving problem: It is the ability to identify methods and models, which can
turn the data into reliable and provable knowledge. Any technology, that claims
to overcome the information overload problem, has to provide answers for the
following problems:
- Who or what defines the “relevance of information” for a given task?
- How can appropriate procedures in a complex decision making process be
identified?
- How can the resulting information be presented in a decision- or task-oriented
way?
- What kinds of interaction can facilitate problem solving and decision mak-
ing?
With every new “real-life” application, procedures are put to the test possibly
under circumstances completely different from the ones under which they have
been established. The awareness of the problem how to understand and analyse
our data has been greatly increased in the last decade. Even as we implement
more powerful tools for automated data analysis, we still face the problem of un-
derstanding and “analysing our analyses” in the future: Fully-automated search,
filter and analysis only work reliably for well-defined and well-understood prob-
lems. The path from data to decision is typically quite complex. Even as fully-
automated data processing methods represent the knowledge of their creators,
they lack the ability to communicate their knowledge. This ability is crucial: If
decisions that emerge from the results of these methods turn out to be wrong,
it is especially important to examine the procedures.
The overarching driving vision of visual analytics is to turn the information
overload into an opportunity: Just as information visualization has changed our
view on databases, the goal of Visual Analytics is to make our way of processing
data and information transparent for an analytic discourse. The visualization of
these processes will provide the means of communicating about them, instead
of being left with the results. Visual Analytics will foster the constructive eval-
uation, correction and rapid improvement of our processes and models and -
ultimately - the improvement of our knowledge and our decisions (see Figure 1).
On a grand scale, visual analytics solutions provide technology that combines
the strengths of human and electronic data processing. Visualization becomes
the medium of a semi-automated analytical process, where humans and machines
cooperate using their respective distinct capabilities for the most effective results.