Running title: TRENDS IN COMPUTER INFORMATION SYSTEMS 1 TRENDS IN COMPUTER INFORMATION SYSTEMS 4 Trends in Computer Information Systems, and the Rise to Business Intelligence Shad Martin School for Professional Studies St. Louis University ENG 2005 Dr. Rebecca Wood November 23, 2016 Introduction Our quest to increase our knowledge of Computer Information Systems has produced a number of benefits to humanity. The innovation humans have discovered in Computer Information Systems has led to new sub-areas of study for students and professionals to continue their progression to master all that Computer Information Systems has to offer. Amy Web of the Harvard Business Review reported 8 Tech Trends to Watch in 2016, She noted, “In order to chart the best way forward, you must understand emerging trends: what they are, what they aren’t, and how they operate. Such trends are more than shiny objects; they’re manifestations of sustained changes within an industry sector, society, or human behavior. Trends are a way of seeing and interpreting our current reality, providing a useful framework to organize our thinking, especially when we’re hunting for the unknown. Fads pass. Trends help us forecast the future” (Harvard Business Review, 2015). In short, Amy’s reference to understanding the emerging trends in Computer Information can provide a framework from which, students, professionals, and scientists to conscientiously create a path towards optimizing their efforts. Ensuring we have a fundamental approach to analyze data will enhance our understanding of this subject further. In this paper I will expound on three of the top trends used to provide insight into the data produced from the advancements in Computer Information Systems. These trends or methods are taking place in my workplace within a financial institution, and in many other industries. It is important to note this paper does not provide an inclusive list of all methodologies that exist. Individuals can now leverage analytics to synthesize insights from data to identify emerging risk, manage operational risks, identify trends, improve compliance, and customer satisfaction. Data in and by itself is not always useful. Regardless of the data source, trained professional must understand the best approach to structure the data to make it more useful. In this paper, I will touch on three popular methodology trends occurring in Computer Information Systems. Students and professionals who work with large data would benefit from having a solid understanding of the fundamental principles of Business Intelligence as data scientific approach and when to use these methodologies. The rise of Business Intelligence Computer Information Systems allow many companies to gather and generate large amounts of data on their customers, business activities, potential merger targets, and risks found in their organization. These large sets of data have given rise to vari.

1. Running title: TRENDS IN COMPUTER INFORMATION
SYSTEMS 1
TRENDS IN COMPUTER INFORMATION SYSTEMS 4
Trends in Computer Information Systems, and the Rise to
Business Intelligence
Shad Martin
School for Professional Studies
St. Louis University
ENG 2005 Dr. Rebecca Wood
November 23, 2016

2. Introduction
Our quest to increase our knowledge of Computer
Information Systems has produced a number of benefits to
humanity. The innovation humans have discovered in Computer
Information Systems has led to new sub-areas of study for
students and professionals to continue their progression to
master all that Computer Information Systems has to offer. Amy
Web of the Harvard Business Review reported 8 Tech Trends to
Watch in 2016, She noted, “In order to chart the best way
forward, you must understand emerging trends: what they are,
what they aren’t, and how they operate. Such trends are more
than shiny objects; they’re manifestations of sustained changes
within an industry sector, society, or human behavior. Trends
are a way of seeing and interpreting our current reality,
providing a useful framework to organize our thinking,
especially when we’re hunting for the unknown. Fads pass.
Trends help us forecast the future” (Harvard Business Review,
2015). In short, Amy’s reference to understanding the emerging
trends in Computer Information can provide a framework from
which, students, professionals, and scientists to conscientiously
create a path towards optimizing their efforts. Ensuring we have
a fundamental approach to analyze data will enhance our
understanding of this subject further.
In this paper I will expound on three of the top trends used to
provide insight into the data produced from the advancements in
Computer Information Systems. These trends or methods are
taking place in my workplace within a financial institution, and
in many other industries. It is important to note this paper does

3. not provide an inclusive list of all methodologies that exist.
Individuals can now leverage analytics to synthesize insights
from data to identify emerging risk, manage operational risks,
identify trends, improve compliance, and customer satisfaction.
Data in and by itself is not always useful. Regardless of the data
source, trained professional must understand the best approach
to structure the data to make it more useful. In this paper, I will
touch on three popular methodology trends occurring in
Computer Information Systems. Students and professionals who
work with large data would benefit from having a solid
understanding of the fundamental principles of Business
Intelligence as data scientific approach and when to use these
methodologies.
The rise of Business Intelligence
Computer Information Systems allow many companies to gather
and generate large amounts of data on their customers, business
activities, potential merger targets, and risks found in their
organization. These large sets of data have given rise to various
forms of intelligence, often referred to Business Intelligence or
BI. Business Intelligence is the next best thing to an
organization having a crystal ball into the future. Within
Business Intelligence there are specific techniques used to
examine the data. Based on my research there are three widely
fundamental concepts of Business Intelligence: Predictive,
Descriptive and Prescriptive Analytics. These methods used by
many Business Intelligence professionals allow individuals and
companies detailed insight into understanding what has
happened, which could help companies predict what could
happen. In this paper, I will touch on these three methods.
Predictive Analytics
Predictive analytics is an emerging field of study used to mine
data from various systems. It entails extracting actionable
information from data. Actionable information is widely
considered to be information that could lead to organizations to
make better business decisions and implement strategic business
approaches. Bersin by Deloitte, defines this subject as:

4. “[a]ctionable information” provides data that can be used to
make specific business decisions. Actionable information is
specific, consistent, and credible.” Bersin provided one of the
best examples on this topic. Bersin goes on to describe a related
example, in which he cites the following, “[f]or example, a
report which shows trends in "employee retention" is important
and interesting, but not necessarily actionable. However, a
dashboard or simple red / yellow / green report which shows
managers the turnover rate by department, accompanied by the
"top three reasons for leaving the company," is far more
actionable. In any HR or L&D data and reporting program, it is
always important to drive toward giving managers data which is
not only interesting, but actionable” (Bersin).
Business Intelligence utilizes many techniques to derive
actionable information from datasets, one of them is referred to
as Predictive Analytics. Predictive Analytics can help
organizations and companies predict behavioral patterns, trends,
forecasting, and probabilities into various business activities,
e.g., customer buying patterns or healthcare costs. These
methods are being applied to a number of areas impacting our
daily lives; e.g., engineering, biometrics, social media, city
planning, and a number of others areas. This methodology has
seen widespread implementation within many companies. A
multitude of universities have also begun to offer classes on this
topic to help students strengthen their understanding of this
discipline. Predictive Analytics uses historical data understand
what has happened in the past to predict what might happen in
the future. This forward looking approach is a new trend many
companies are using to improve their decision making and
create a stronger competitive advantage. Predictive Analytics is
be a valuable tool for companies to evaluate current business
process and identify areas where risks exist, or are in need a
business process redesign.
Descriptive Analytics
Descriptive Analytics is another fundamental principle
(methodology) to evaluate data for deeper insight. Dr. Michael

5. Wu, a renowned chief scientist of San Francisco-based Lithium
Technologies (2), gave a simplistic definition of the Descriptive
Analytics in a March 2013 blog series on this topic. According
to Wu, who believes descriptive analytics "the simplest class of
analytics," one that allows you to condense big data into
smaller, more useful nuggets of information (Wu,
InformationWeek 2013). This approach allows students and
professional to manipulate large datasets to be scaled to levels
where useful granular information can be shared more easily.
This approach is critical when providing information about the
data to the highest levels of an organization or the general
public. Especially, since many senior leaders simply do not
have the time to read through numerous pages of data to get to
the main point of the information. The general public also does
not have the appetite to consume large amounts of information
found in data. Concise issues from data is the best way to share
information to the general public.
Prescriptive Analytics
Prescriptive Analytics is the last method included in this paper.
Prescriptive Analytics is used to help companies chart a
decision on which way a company or organization should go,
based on the data. In short, it is used to ‘prescribe’ an answer
for an organization. This data driven approach is critical
because Predictive, and Descriptive Analytics can plot multiple
scenarios. When used correctly it can generate several solid,
fact based information paths from the data. As Wu describes
“Predictive Analytics is used to predict the possible
consequences” (Wu. InformationWeek, 2013), based on
different choices an organization, company, or professional
chooses. Just as in the medical field, once the data is analyzed,
a diagnosis is needed.
Conclusion
In the future, Business intelligence will be less about
investigative patterns from past events, rather, Business

6. Intelligence will be more about predicting the future. All data
has a story, and the employing the correct approaches can help
the audience understand what the information in the data is
saying. We cannot tell the future by looking at the past; this
assumes that conditions have or will remain the same. The three
most common analytical methods: Predictive, Descriptive, and
Prescriptive analytics, can help us approach data in a targeted
fashion. This will help us understanding the information of the
past to better predict the future. Business Intelligence will
continue to exist as an emerging area of new methodologies for
data mining, extracting information from data and using it to
predict trends, and one day human behavior patterns. Although
this paper named three of the Business Intelligence to Computer
Information Systems, these approaches, and many others should
not be considered in isolation. Each one has an important roles
based on the objective of the data evaluation goal. The
challenge for us will be how to communicate the best
information within the data in the most concise, effective, and
coherent manner that best demonstrates a forward-looking
approach.
References
Web, Amy. “8 Tech Trends to Watch in 2016.” Harvard
Business Review, retrieved 12, November, 2016. Web.
From: https://hbr.org/2015/12/8-tech-trends-to-watch-in-2016
Bertolucci, Jeff. “Big Data Analytics: Descriptive Vs.
Predictive Vs. Prescriptive.” InformationWeek. December,
2013. Retrieved 14, November, 2016. Web.
From: http://www.informationweek.com/big-data/big-data-

7. analytics/big-data-analytics-descriptive-vs-predictive-vs-
prescriptive/d/d-id/1113279
Parashar, Manish and George Thiruvathukal. “Extreme Data.”
Computing in Science Engineering. Vol. 16, No. 04. Pp: 8-10
July-Aug, 2014. Retrieved 14, November, 2016. Web.
From:
http://doi.ieeecomputersociety.org/10.1109/MCSE.2014.83
Abbott, Dean. “Applied Predictive Analytics: Principles and
Techniques for the Professional Data Analyst.” Wiley. Pp 6-30.
17, November, 2016.
Bibliographies
Holland, P.R. (2015). SAS Programming and Data
Visualization Techniques. New York, NY: Springer
Science+Business Media.
The SAS Programming and Data Visualization Techniques
book provides insight into one of the many software systems
utilized by Computer Information System professions to gain
insight from large quantity of data within various Computer
Information Systems. The book details the best approaches for a
user of this software to become proficient in coding efficiency.
The book also provided rationale for which charts, or graphs
should be used to visualize data. SAS as known as Statistical
Analysis Software is a power tool used to extract extremely
large datasets from various data warehouses within many large
financial, insurance, and technological institutions. The book
explains data like epidemiology, “[i]n epidemiology, most data

8. sets from SAS and other databases are big, and SAS
programmers need particular skills to work with data.” Holland
explains, an undisciplined approach to large data extracts can
lead to many issues. Holland asserts, “[c]reating reports
containing multiple output can result in files that are too large
to e-mail or that contain too many individual files to transfer
together.” In the book, Holland details a number of proven
techniques to help users gain a stronger understanding of the
many programming techniques needed to gain a proficient
knowledge of this tool. The book details many the best approach
to displaying information extracted from large datasets. Some of
the techniques mentioned in the book were: industry wide
accepted analytic approaches, which will help novice users
understand which software applications are the most effective to
use based on the size of the data.
Provost, F. and Tom Fawcett. (2013). Data Science for
Business. Sebastopol, CA: O’Reilly Media.
This book provides excellent information on the importance of
Business Intelligence in today’s digital age, and the associating
methods needed to approach large data sets strategically. The
book was not over technical. The author has a pragmatic
approach, which helped me understand the content and convey
the information in a relatable fashion. The detailed examples
provided in the book helped me frame the message. Data
Science for Business offers a fundamental approach to digest
large datasets. Understanding data will continue to be an
important step for business “[i]f solving the business problem is
the goal, the data comprise the available raw material from
which the solution will be built. It is important to understand
the strength and limitations of the data because rarely is there
an exact match with the problems” (Provost, 2013, p. 28). This
book highlights great point about data. It strongly infers that
not all data is good data. In Data Science for Business, Foster
Provost explains the importance of a strategic approach to

9. evaluate data, since most data is backward looking. In the book,
Provost asserts that “[h]istorical data often are collected for
purposes unrelated to the current business problem, or for no
explicit purpose at all” (26). This message is critical in
determining what data to capture and which approach could
yield the most insight. This book highlights the most effective
Business Intelligence methods, which helps unfamiliar people
more competent on this topic.
Abbot, D. (2014). Applied Predictive Analytics: Principles and
Techniques for the Professional Data Analyst. Indianapolis, IN
John Wiley & Sons.
This book provides excellent foundation from which I was able
to understand Business Intelligence and why it is so important
for users who analyze data. The book provides easy to follow
examples of why various analytic approaches should be
considered. “Analytics is the process of using computational
methods to discover and report influential patterns in data,”
(Abbot, p.3, 2014). Business Intelligence is often used
interchangeably with analytics. Abbot’s approach to introducing
the reader to foundational principles of Business Intelligence
helped ensure the readers comprehension on this topic. Business
Intelligence is a huge area of study for many professionals, and
students. Business Intelligence will remain an important aspect
in our everyday lives. The detailed examples on Business
Intelligence and analytics provided in the book helped me select
the key strategic approaches to highlight in this paper. Datasets
are also discussed in the book. The size of a dataset can
determine which Business Intelligence approach should be used.
Conversely, the book does a great job allowing the reader to
participant in the learning exercises. The overall theme of the
book was to explain the logic-driven methods in a practical
manner. The book achieved this goal.

10. Schniederjans. M, Dara Schniederjans, and Christpoer Starkey.
(2014). Business Analytic – Principles, Concepts, and
Application. Upper Saddle River, NJ. Pearson Education.
This book instrumental to understanding the logic behind the
various Business Intelligence approaches. The book explained
why exploratory data needs to be visualized. The book
presented operational problems to help guide the reader through
the various problem solving steps. The book also does a really
good job of explaining the undertaking of Descriptive
Analytics. Schniederjans asserts an explanation of data in his
book, “regardless of the Business Analysis assignment, the first
step is one of exploring data and revealing new, unique, and
relevant information to help the organization advance its goals,”
(Schniederjans, 2014, p. 63). The book explains the importance
of selecting the appropriate analytical method based on what
one’s initial observation of the data produces. The book also
explains why it is important to first study the data and later
come back to select the best approach to reveal the insight.

11. Fall Semester II | Shad Martin
Module 4 Assignment: Becoming a Scholar of Change-
Elementary Reading
Createan 7-slide, narrated multimedia presentation (e.g.,
PowerPoint, Prezi, etc.), using the notes section to create an
ADA-compliant transcript, which includes the following:
· An explanation of a problem or challenge in your school
setting or community and how it fits within the global
educational climate
· A plan for how you intend to be an agent for positive social
change with regard to the problem or challenge you identified.
· An explanation of the impact of positive social change on your
local environment you hope to make through the implementation
of your plan.
Your presentation must include:
· A cover slide and reference slides (in APA format).
· Citations within in the presentation to researched information
outside of the Learning Resources for this course.
· Color and graphics that demonstrate that your presentation is
professional in content and context.
Helpful reference within this course.
DuFour, R. (2004). Schools as learning communities: What is a
“professional learning community?”Educational
Leadership, 61(8), 6–11.
Niesz, T. (2007). Why teacher networks (can) work. Phi Delta
Kappan, 88, 605–610.
Walden University. (n.d.e). Social change. Retrieved March 18,
2016, from http://www.waldenu.edu/about/social-change

12. Running title: IMPORTANT ISSUES IN THE ADAVNCEMENT
OF COMPUTER INFORMATION SYSTEMS 1
IMPORTANT ISSUES IN THE ADAVNCEMENT OF
COMPUTER INFORMATION SYSTEMS 7
Important Issues in the Advancement of Computer Information
Systems
Shad Martin
School for Professional Studies
St. Louis University

13. ENG 2005 Dr. Rebecca Wood
November 14, 2016
Introduction
Computer Information Systems is rapidly changing. The
pace of this change creates an ambiguity of implications on the
overall society of humankind. It is not heavily regulated, and as
a result it has flourished. The advancement in computer
information systems or science has elevated economies and
stimulated growth in the world. However, this new found
economic growth carries great risks. The speed of the computer
information systems advancement can possibly leave behind
untold scores of people. Access to the new technology, and
training to understand the deeply complicated embedded
analytics that consist of computer information systems is
intimidating in itself. Of the many examples shared in this
paper, mobile devices is one area where we have seen
tremendous growth. “Mobile devices to become more powerful
have made possible through the technological breakthroughs in
the miniaturization of processors, networking technologies,
memory, displays and sensors. A smart device refers to an
electronic wireless, mobile, always connected (via WiFi, 3G,
4G, etc.) and is capable of voice and video communication,
internet browsing, "geo-location" (for search purposes) and that
can operate to some extent autonomously,” (International
Journal of Multimedia and Ubiquitous Engineering, 2013).

14. Mobile devices have led to the creation of countless
applications. It has introduced so many users (people that use
the device), to new, and exciting technologies. This paper will
endeavor to look into the challenges facing computer
information systems, reasons why some of challenges exists,
and why it is so important to make this topic a focal point for
people, regardless of their age. Thereafter, some of the risks are
inherent to the discipline of computer information systems and
everyone must remain vigilant to be aware of the every
changing landscape.
Important Issues in the Advancement of Computer Information
Systems
Advancements in Computer technology is changing faster than
anyone could ever have imagined. The advancements made in
Computer Information Systems begin with an idea that begins
the journey. It could be an idea for a new feature, application,
service, or business. As a civilization, we have advanced
beyond the photographs, print or scribed books, and newspaper
columns; as a source of current events, columnist opinions, and
historical readings. We now have the ability to post our
thoughts - as we have them - on various types via social media.
In 2016, we have seen many new emerging technologies in our
daily lives. The new dichotomy of Computer Science has
changed the way we go about our daily lives. In the last four
years alone, we now have automobiles with voice recognition,
bank tellers who can engage their customers virtually, and
autonomous driving automobiles.
We have new data environments, which literally store data
everywhere, known as the ‘Cloud.’ Artificial Intelligence (AI)
will become more integrated into the fabric of how humans
engage each other and the world. There are billions of smart
phones that can process requests for data and quickly dispense
information at our finger tips. We can now ‘live stream’ for all

15. of our friends to see what we are doing, feeling or have to say.
We have the ability to sign into our phones or software
applications with biometric methods. This new technology
(biometric) allows us to sign into systems, applications, and our
phones with a finger print, retinal scan, or voice recognition.
These technological advances have lead industry leaders to
predict how more advanced these new technologies are far more
intertwined in the fabric of our lives. As Andrew Drazin, of
Theron LLP, proclaimed; “Working in technology is going to be
a far more interesting and challenging place to work in than it
has been to date. There are going to be more upsides than
downsides.” In this paper I will expound on many types of the
challenges humans can expect as technology continues to
advance. In 2016, it has become a common place to store
information, or data – everywhere.
Access to the new technology
Therefore it is clear from the above, that there will be
inevitable challenges with these new technological
advancements and how some will be significant for the end
users. Innovations in computer science will consequentially
leave many users behind. Many will be unable to comprehend,
or embrace the ever changing computer information revolution.
There will also be people who struggle to gain access to this
new technology due to poverty, or geopolitical governance.
Especially those in developing countries and individuals whose
governments cannot afford to purchase the latest advancements
in technology. Others will struggle with the changes being made
in computer information systems, particularly our aging
population of citizens. This will present many dangers for
everyone. For example, if people are not properly introduced to
new and emerging technologies in computer science there could
be significant human errors that lead to hundreds if not
thousands of casualties. Information security breaches could
occur more frequently, and cost millions, if not billions of

16. dollars in stolen credit card data and associating losses. As
expressed above, it is essential to educate and prepare our
society for the changing technological landscape.
Training
For those who can afford access to the new technology in
computer information systems, they will concentrate their
cognitive abilities on programming and coding software boot
camps, academic specific genres related to computer systems,
and advanced degrees that specialize in Computer Information
Systems. For many in the wealthiest countries of the world this
will come easy. From a training perspective, some of the
essential qualities and characteristics successful individuals will
need highlights by “Reich’s four key skills: abstract thinking,
systems thinking, experimentation, and collaboration,”(MIS
Essentials). These four critical skills sets are needed to help in
the design/planning phase of the new computer information
system, application, software, hardware, etc., being created. A
successful product should be designed with the end user’s
perspective that aligns with the end state of the finish product.
If the proper process, procedures, and precautions in the
planning are not followed, then unintentional disasters could
happen. Therefore it is very clear that the developers of these
new technologies have a forward looking approach to problem
solving.
Conclusion
The evolution of Computer Information Systems is an essential
aspect of any country, organization, and individual, as it has
become a major driver in manufacturing and service industries.
Due to its significance in our lives, it is vital that we ensure
people are properly prepared for the changes that are to come
and that they are properly trained for this technology. Also,
ensuring that every user of this technology understands the risk

17. and rewards of a more computerized environment. It is evident
that this technological genre will have a prolonged life and
prosperity as the technology has enormous potential. Many
people, especially, the younger generation will need to be
stirred in to this technological sector.
As mentioned earlier, customers have to find value in learning
more about this technology in order to remain competitive. Our
country would thrive from large scale training of the new
revolution. Many industries will become more efficient,
reducing staff, which could lead to the creation of large scale
unemployable people. There is a very wide array of industries
that have made the advancement of Computer Information
Systems their top priorities. As we witness these changes, its
challenges, and opportunities, we must ensure we are better
prepared to embrace the new and emerging technologies through
access, proper exposure, and training. Out futures literally
depend on the decisions we make today.
References
Drazin, Andrew and Bill Goodwin. “Future Gazing: The Future
of IT in 2020.” Computer Weekly, retrieved 12, November,
2016. Web.
from:
http://www.computerweekly.com/news/2240209132/Future-

18. Gazing-The-Future-of-IT-in-2020
Caytiles, Ronnie and Byungjoo Park. “Future Directions of
Information and Telecommunication Systems Through the
Technological Advancement Convergence.” International
Journal of Multimedia and Ubiquitous Engineering. Vol. 8, No.
1, January, 2013.
Kroenke, David. “The Importance of MIS.” MIS Essential.
Fourth edition. (2014). Chapter 1. pp. 15.
Nickolaisen, Niel “The Constant Evolution of Technology can
have a downside.” Tech Target. SearchCIO, Retrieved 11,
November, 2016. Web.
From: http://searchcio.techtarget.com/tip/The-constant-
evolution-of-technology-can-have-a-downside
Fall Semester II | Shad Martin
Final Paper Proposal
I would like to propose the topic of data and analytics to my
paper two. In paper two, I discussed the challenges facing the
Computer Information Systems, from an education, access to
software, and information security standpoint. Paper two would
benefit from a more granular view of how the evolution of
Computer Information Systems has not only increase the amount
of useful information for various organizations as they seek to
interpret the new datasets. In many cases, the abundance of data
has also led to an information backlog. There is so much
information in the data that the company’s leaders simply do
not understand how to use the all of the data’s content. This
increase of information has caused many users and their
customers to question the data differently than their perceived
notion of the information. For example, if a bank’s automotive
finance division had a lot of complaints in one area. The
company is financing auto loan for consumers, also receives a
higher amount of complaints from their customer’s in the loan
servicing department. How does the company begin to
understand the customer’s pain points? The company needs to

19. understand what their customers are voicing as a concern within
the ‘servicing’ sector of the bank. The data itself can begin to
tell this story, e.g., why their consumer complaints lodged
against the company’s servicing sector exist. This insight into
the data might be useful for the company to redesign their entire
business process model. Conversely, if the company does not
understand the analytic techniques needed to interpret the data,
this could lead to the data being skewed. An example of this is
data not displayed properly or its presentation (charts, Area
plots, Scatter plots, etc.), could cause the company to
misunderstand the information in it further. To gleam insight
from this data the company most also ensure it has the
appropriate tools to perform data analytics on the data. In short,
data and analytics would be a subset of the advancements in
Computer Information Systems.