BACKGROUND A dataset is data collection, which most usually

BACKGROUND
A dataset is data collection, which most usually correspond to the single statistical data
matrix or single database table, where each row corresponds to the specified data set member
and each column in table indicates a specific variable. And it lists out values corresponding to
each variable, like object’s weight and height, for each dataset’s member. The datasets are
tremendously useful for any organization to analyze the performance, expenses, profits,
drawbacks, challenges and many more. When the analysis of past is the primary the benefit is
scaled up more than ten times, with the usage of these datasets through data analytics to
develop new strategies to follow (Atz, 2014). In a nutshell, datasets help the organizations to
win more and more profits, by exploiting the past data by understanding the patterns and
applying the logic of pattern, in newer business functions. The dataset related projects and
respective patterns enable the organizations to achieve their objectives and goals, in a much
less time with confident data and strategies.
Visual analytics is a scaled up and extended versions of scientific visualization and
information visualization, which focus on reasoning analytically and it is facilitated by visual
interfaces that act interactively (Manuela & Carlos, 2014). Visual analytics are usually
concerned with coupling visual representations, interactively with the analytical processes
underlying, such as data mining techniques, statistical procedures, so that complex and higher
level activities, like decision making, reasoning, sense making, etc. can be performed
effectively.
Data visualization is considered by contemporary big data experts as both science and art.
Dataset visualizations are treated as grounded theory development tool, along with as
descriptive statistics branch. Huge data created and collected by the activity of internet and
increasing number of sensors are considered as internet of things or big data. Analyzing this
huge or tonnes of data may take even lifetime for a human being to make a smaller analysis.
This big data is made possible to analyze only because of visualizations and organizations get
benefited spending less time to exploit big data and gain big profits (Nikos, 2018).
REFERENCES
Atz, U (2014). The tau of data: A new metric to assess the timeliness of data in
catalogues". CEDEM 2014 Proceedings
Manuela Aparicio and Carlos J. C. (November 2014). Data visualization. Communication
Design Quarterly Review.
Nikos Bikaks (2018). Big Data Visualization Tools. Encyclopedia of Big Data
Technologies, Springer 2018.
Snijders, C., Matzat, U., Reips, U. D. (2012). 'Big Data': Big gaps of knowledge in the
field of Internet. International Journal of Internet Science