Page 149 - Data Architecture

Chapter 4.4: Unstructured Data
Chapter 4.4

Unstructured Data

Abstract

There are different definitions of big data. The definition used here is that big data
encompasses a lot of data, is based on inexpensive storage, manages data by the “Roman
census” method, and stores data in an unstructured format. There are two major types of
big data—repetitive big data and nonrepetitive big data. Only a small fraction of

repetitive big data has business value, whereas almost all of nonrepetitive big data has
business value. In order to achieve business value, the context of data in big data must be
determined. Contextualization of repetitive big data is easily achieved. But
contextualization of nonrepetitive data is done by means of textual disambiguation.

Keywords

Big data; Roman census method; Unstructured data; Repetitive data; Nonrepetitive data;
Contextualization; Textual disambiguation

It is estimated that over 80% of the data in the corporation are unstructured information.
There are many different forms of unstructured information. There is video. There is
audio. There are images. But far and away the most interesting and useful for
unstructured data is textual information.

Textual Information—Everywhere

Textual information is found everywhere in the corporation. Text is found in contracts, in
e-mail, in reports, in memorandum, in human resource evaluations, and so forth. In a
word, textual information makes up the fabric of corporate life, and that is true for every
corporation.

Unstructured information can be broken into two major categories—repetitive
unstructured data and nonrepetitive unstructured data. Fig. 4.4.1 shows the categories
that describe all corporate data.

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