Business Intelligence: Text Mining, Artificial Intelligence, and Dashboard Design

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BUSINESS INTELLIGENCE
By (Name)
The Name of the Class (Course)
Professor (Tutor)
The Name of the School (University)
The City and State where it is located
The Date

Business Intelligence 2
Business Intelligence
Question 1
In most situations text and data mining are used interchangeably by most people as a
means of defining data and information processing. This assumption is generally true, but there
are specific attributes to both that make them distinctly different (Dang 2014). Data mining can
be viewed as there discovery and extraction of knowledge from structured data stored in
databases. This form of information gathering has been until recently the norm. However, with
the emergence of unstructured data that is commonly referred to as text; a new method of
information collection was devices in the form of text mining (Dang 2014). Even thou,
unstructured data may contain some information in structured fields like dates, numbers, and
facts; a significant proportion of the information is presented in text format. For instance, the
following web-resources can be defined as unstructured data: website articles, online journal
articles, and blog posts. The presence of unstructured information in data warehouses renders
traditional business intelligence assets/tools ineffectively in the optimal performance of
knowledge management. Therefore, the discovery of knowledge resources that provide
unstructured information (text) is referred to as text mining. As such, the chief difference
between text and data mining is in terms of the data type: unstructured or structured respectively
(Dang 2014).
There are several technologies that are employed in the performance of text mining, some
of which are considerably similar to those used in data mining. However, the technologies or
approaches used in text mining tend to be more refined to accommodate ”machine learning”
commonly referred to as artificial intelligence. Here are two text mining technologies: Keyword-

Business Intelligence 3
based technologies; and Statistics technologies (Brussel 2017). Under keyword-based
technologies the input value is founded on the selection of given keywords in text that are
filtered as a chronological arrangement of string characters (not words, concepts, or facts). On
the other hand, statistics technologies refer to computer systems that are centered on machine
learning. As such, the approaches are supported by a training set of documents that are used in
the management and categorization of unstructured data. The applications of text mining are
numerous especially in the fields of cyber-security and risk management. For instance, text
mining is applied in sentiment analysis that centers on the extraction of given information from
social media posts for a variety of uses such as audience-sensitive advertising. Moreover, text
mining intelligence software is being employed in the prevention of cyber-crimes through the
identification of potential offenders that use anonymous profiles to conduct internet-based crimes
(Chalmers 2010).
Question 2
Importance of artificial intelligence in business smart systems
In the bid to improve business services through the introduction of smart systems,
numerous organizations have adopted artificial intelligence. For instance, social media, phone
manufacturing, and banking companies have introduced image recognition as a mean to better
security measures. Image recognition is categorized under smart technology that is intended to
simplify the various procedural elements of different systems (Hughes 2014). For instance, social
media platforms like Facebook use this technology to recognize the faces of friend in
photographs and then ask you to kindly tag them in you post. Moreover, given mobile device
applications has been presented with artificial intelligence technology that allows for the