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

This article covers the differences between text mining and data mining, the importance of artificial intelligence in smart systems, and critiques of a selected dashboard design. It includes discussions on the technologies used in text mining, the benefits and limitations of AI, and suggestions for improving the 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

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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-

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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

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identifications of different birds and animals in the wild. Image recognition is expected to
replace ID cards in different corporate buildings. Artificial intelligence has considerably boosted
cognition and problem solving offered through smart services. For instance, numerous
advancements in artificial intelligence has created superior machine learning that allows
computers to beat the best human players in games like chess, and poker. Moreover, IBM
software used in a Singaporean insurance website has completely automated the claims page
making the process considerably smooth and friendly to first time users. Other ways that
artificial intelligence is employed is in the development of advice platforms as a means to
enhance customer support (Mannino et al. 2015).
How artificial intelligence helps to transform companies
There are numerous benefits to organizations that are created by machine learning due to
the quick spread of artificial intelligence (Armstrong 2015). To accommodate the increasing
demand for machine learning professionals and data scientists, universities and colleges are
providing online courses to students in these fields. Therefore, artificial intelligence has
drastically changes the workforce requirement for any company by creating new IT positions
that are essential for the successful performance of different departments. Moreover, companies
are now required to provide training programs to help employees adapt to artificial intelligence
platforms in the workplace (Mazzini, Beridze & Tuveson 2017). Global software giants like
Microsoft, Google, IBM, and Amazon are providing avenues through which artificial
intelligence algorithms and hardware can be bought or rented. In addition, such technology
companies provide machine learning infrastructures in the form of cloud computing for all
businesses. Competitive markets are forcing companies to introduce technology that is centered
on artificial intelligence with the hope of reducing cost in terms of decrement in human labor and

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increment in overall efficiency. Lastly, the introduction of machine learning system into a
company’s can be done even with limited data. However, the true gain from machine learning
systems is observes as more and more data is presented. Therefore, the management of large data
is made considerably easy (Rossi 2016).
Threats of artificial intelligence and the limitations of smart systems
There are two paradigms through which scientist considered the potential threats
presented by artificial intelligence. It is believed that regards of the complexity of the artificial
intelligence it is unlikely that computer systems will adapt human emotion that can make them
prone to considerable acts of maliciousness. Therefore, these two paradigms are used as the
based on which harm can be carried out by artificial intelligence or create limitations for smart
systems (Markoff 2014). One paradigm focuses on the notion that artificial intelligence is
developed with the sole intention of performing a destructive task. For instance, the development
of autonomous weapon systems by given military groups can result in widespread casualties if
control of the system falls into the wrong hands. Another view point examines a situation where
two artificial intelligence systems in charge of weapons face-off against each other resulting in
loss of human lives. This view point is supported by the ideology that autonomous weapon
systems are design with complicated “turn off” to prevent them from being deactivated by enemy
IT experts. The second paradigm focuses on the idea that a smart system is developed to perform
a beneficial task but it creates destructive avenues of fulfilling this objective (Lawal & Goni
2015).
Question 3
WEKA PROJECT MISSING FILES

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Question 4
Business performance of the Selected Dashboard Design
The business performance is centred on the discernment of web traffic and internet
presence on social media platforms. The information platform provide data on CPU traffic,
Google+ likes, sales volume, new members, web traffic across two platforms, programming
languages used, inventory volume and percentage change, social mentions and percentage
change, download quantity and percentage change, direct messages received and percentage
change. The change relates to increment or decrement in a given statistical variable in percentage
terms. Only 10% of the overall data on the dashboard is presented in qualitative form (i.e.
programming language used- JavaScript, html, or CSS) the rest of the data is presented in
quantitative form (e.g. sales volume-760). The three visual objects employed in the dashboard
are single item tables, a line graph, and a pie chart (Ivanovs 2018).

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Critiques to the Selected Dashboard Design
The dashboard does not provide real-time overview of members currently online,
business transactions taking place, and geographical location of logged in individuals. Moreover,
it would be ideal for the dashboard to provide information on the utilization of server storage
space, bandwidth, and profit charts. These additions to the current dashboard will improve the
overall effectiveness of the information platform in the acquisition of user data, running of
marketing campaigns, and identification of cost centric areas (Ivanovs 2018).

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References
Armstrong, S 2015, 'General Purpose Intelligence: Arguing the Orthogonality Thesis', Analysis and
Metaphysics, vol I, no. 12, pp. 68-84.
Brussel 2017, 'Article 3 – Text and Data Mining', Information Society Directive, pp. 1-2.
Chalmers, D 2010, 'The Singularity: A Philosophical Analysis', Journal of Consciousness Studies, vol XVII,
no. 9, pp. 7-65.
Dang, S 2014, 'Text Mining : Techniques and its Application', Masters Thesis, Department of Information
Technology and Computer Science, Maharishi Markandeshwar University, Maharishi Markandeshwar
University, Mullana.
Hughes, JJ 2014, 'Are Technological Unemployment and a Basic Income Guarantee Inevitable or
Desirable?', Journal of Evolution and Technology, vol XXIV, no. 1, pp. 1-4.
Ivanovs, A 2018, Top 22 Free Responsive HTML5 Admin & Dashboard Templates 2018, viewed 3 June
2018, <https://colorlib.com/wp/free-html5-admin-dashboard-templates/>.
Lawal, A & Goni, I 2015, 'Artificial Intelligence and Its Global Risk', International Journal of Scientific
Engineering and Applied Science (IJSEAS) , vol I, no. 3, pp. 1-5.
Mannino, A, Althaus, D, Erhardt, J, Gloor, L, Hutter, A & Metzinger, T 2015, 'Artificial
Intelligence:Opportunities and Risks', The Effective Altruism Foundation, pp. 1-22.
Markoff, J 2014, Study to Examine effects of Artificial Intelligence, viewed 2 June 2018,
<http//www.nytimes.com/2014/12/16/science/>.
Mazzini, M, Beridze, I & Tuveson, M 2017, 'The Risks and Benefits ofArtificial Intelligence and Robotics',
UNICRI, pp. 1-28.
Rossi, F 2016, 'Artificial Intelligence: Potential Benefits and Ethical Considerations', European Parliament,
pp. 1-8.

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