Running head: INFORMATION SECURITY 1 INFORMATION SECURITY 6 Information Security Name Institutional Affiliation Information Security Introduction Information security is defined as the means by which data in computer systems are protected. The protection will is designed to ensure that the confidentiality, integrity, and availability of the data is maintained. Regardless, the proposal of the (my?) organization is that it is to provide data analytics services to various companies in the health sector. By taking advantage of emerging technologies such as cloud computing the company will not only be able to offer its services at competitive rates but will also be able to improve overall performance whilst ensuring data security (Peltier, 2016). Cloud computing, in general, refers to the delivery of computer resources from applications to data centers such as those that will be owned by the company. The basis of this strategy is to have easily available and secured data over the internet. Moreover, it has also been identified that the cloud service to be used is Software as a service (SaaS) (Peltier, 2016). It is the use of an application that is run by a distant computer on the cloud via a browser or internet-based application. By understanding this basis of operations, it willwe can better demonstrate how information security will be attained. Comment by Mark O'Connell: Is that a direct quote? “Ensuring” is a pretty bold word. Not much is guaranteed in InfoSec. Comment by Mark O'Connell: In your final report this will probably be redundant with the cloud section Reasoning The SaaS approach was selected for numerous reasons among them, its high flexibility and attractive nature to the clients. Additionally, by simplifying its installation and overall utilization, it eliminates security vulnerabilities. With security as its core value, the SaaS approach to cloud computing offered eliminates control over the hardware by the client (McCoy & Perlis, 2018). This approach is necessary for numerous reasons among them is the fact that having the hardware installed within the organization it will make itnot be as well protected as that provided by the CSP and it might become vulnerable to outside attacks, human error, and malicious employee activities all of which can result in data loss. This realization was after a study conducted by Accense, an analytical company, during the period of 2009 and 2014, the number of cyberattacks increased drastically if the client used on-premises servers instead of cloud-based servers (McCoy & Perlis, 2018). According to their figures, the numbers rose from a total of just over 3 million attacks per year to over 42 million attacks. For example, in 2017, the total number of data breaches cost companies an approximate of $3.6 million (McCoy & Perlis, 2018). With the figure expected to be significantly higher in 2019, the best approach to limiting cyberattacks and overall data breaches is by employing SaaS ...

1. Running head: INFORMATION SECURITY 1
INFORMATION SECURITY 6
Information Security
Name
Institutional Affiliation
Information Security
Introduction
Information security is defined as the means by which data
in computer systems are protected. The protection will is
designed to ensure that the confidentiality, integrity, and
availability of the data is maintained. Regardless, the proposal
of the (my?) organization is that it is to provide data analytics
services to various companies in the health sector. By taking
advantage of emerging technologies such as cloud computing
the company will not only be able to offer its services at
competitive rates but will also be able to improve overall
performance whilst ensuring data security (Peltier, 2016). Cloud
computing, in general, refers to the delivery of computer
resources from applications to data centers such as those that
will be owned by the company. The basis of this strategy is to
have easily available and secured data over the internet.
Moreover, it has also been identified that the cloud service to be
used is Software as a service (SaaS) (Peltier, 2016). It is the use
of an application that is run by a distant computer on the cloud
via a browser or internet-based application. By understanding

2. this basis of operations, it willwe can better demonstrate how
information security will be attained. Comment by Mark
O'Connell: Is that a direct quote? “Ensuring” is a pretty bold
word. Not much is guaranteed in InfoSec. Comment by
Mark O'Connell: In your final report this will probably be
redundant with the cloud section
Reasoning
The SaaS approach was selected for numerous reasons
among them, its high flexibility and attractive nature to the
clients. Additionally, by simplifying its installation and overall
utilization, it eliminates security vulnerabilities. With security
as its core value, the SaaS approach to cloud computing offered
eliminates control over the hardware by the client (McCoy &
Perlis, 2018). This approach is necessary for numerous reasons
among them is the fact that having the hardware installed within
the organization it will make itnot be as well protected as that
provided by the CSP and it might become vulnerable to outside
attacks, human error, and malicious employee activities all of
which can result in data loss. This realization was after a study
conducted by Accense, an analytical company, during the period
of 2009 and 2014, the number of cyberattacks increased
drastically if the client used on-premises servers instead of
cloud-based servers (McCoy & Perlis, 2018). According to their
figures, the numbers rose from a total of just over 3 million
attacks per year to over 42 million attacks. For example, in
2017, the total number of data breaches cost companies an
approximate of $3.6 million (McCoy & Perlis, 2018). With the
figure expected to be significantly higher in 2019, the best
approach to limiting cyberattacks and overall data breaches is
by employing SaaS cloud services. Comment by Mark
O'Connell: Problem. The number of “attacks” is probably the
same but the number of “successful attacks” due to
“vulnerabilities” probably differs. Comment by Mark
O'Connell: Is that “billion”???
SaaS and Information Security
The strategy of using SaaS is advantageous because it allows

3. numerous features to be included. This allows for the automated
implementation of security measures while data is being stored
or extracted from the database. Among the features present are
transit protection between the client and the service. This
security measure is critical as some forms of cyber-attack target
data while they are in transit to the storage areas over the
internet (Rittinghouse & Ransome, 2017). By using complex
encryption algorithms, the data if intercepted during
transmission will be useless to the hacker without the
decryption key. Secondly, all user accounts will have mandatory
authentication processes that will further secure the accounts of
the application users. This will limit unauthorized access to the
application; this strategy will be needed for any data to be
transferred, added, destroyed or manipulated (Rittinghouse &
Ransome, 2017). The healthcare sector in 2017 was the most
affected industry with relation to cyberattacks, by automating
their security measures, future attacks can be limited.
The SaaS approach also allows for auditing or logging of
activities, the objective of this security approach is to maintain
accountability. By reviewing the activities of the users,
malicious employees can be easily identified and the necessary
disciplinary action implemented (Rittinghouse & Ransome,
2017). Finally, the SaaS platform will allow for the utilization
of already available cloud computing security protocols further
ensuring the safety of the data uploaded as well as the
information stored. An example of the security measure in
place include protection against DDoS and access regulation to
prevent unexpected interceptions.
Data Valuing
When valuing the company and its data, the main area of focus
was the market niche it was targeting. Technology is evolving at
a rapid rate and this is especially recognizable in the healthcare
industry. The majority of modern healthcare institutions have
migrated from the legacy system and embraced electronic health
records (Chang, Kuo, & Ramachandran, 2016). It is the digital
format of medical records mandated by the HITECH (Health

4. Information Technology for Economic and Clinical Health) Act.
This act is then enforced by the ARRA (American Recovery and
Reinvestment Act) of 2009 (Chang, Kuo, & Ramachandran,
2016). Nevertheless, the value collected and stored will have to
undergo processes that will not only allow it to be verified but
also screened to be classified in different clusters. The process
allows for network optimization to be achieved thereby allowing
for faster processing and storing of data collected from the
client’s end. Moreover, the patterns used by the client’s in
accessing as well as transmitting data are analyzed for better
operations of the service (Chang, Kuo, & Ramachandran, 2016).
Comment by Mark O'Connell: HIPAA Privacy is a major
consideration
Conclusion
Big data is the future for all industries as it offers the needed
insight and understanding of operations thereby allowing for
cloud computing services to progress their services. This is
demonstrated by the approach that is adopted by Health Cop.
The company will be sampling data with the main objective of
improving the network and overall system.
Comment by Mark O'Connell: ? That’s your objective?
To improve the network??
References
Chang, V., Kuo, Y. H., & Ramachandran, M. (2016). Cloud
computing adoption framework: A security framework for
business clouds. Future Generation Computer Systems, 57, 24-
41.
McCoy, T. H., & Perlis, R. H. (2018). Temporal trends and
characteristics of reportable health data breaches, 2010-2017.
Jama, 320(12), 1282-1284.
Peltier, T. R. (2016). Information Security Policies, Procedures,
and Standards: guidelines for effective information security
management. Auerbach Publications.
Rittinghouse, J. W., & Ransome, J. F. (2017). Cloud computing:
implementation, management, and security. CRC press.

5. Running head: NETWORK DESCRIPTION 1
NETWORK DESCRIPTION 6
NETWORK DESCRIPTION
Institution Affiliation
Student Name
Date
HEALTH-COP COMPANY
Network and Workflow Description
Data mining is a complex process that involves several
activities undertaken sequentially for the entire process to be
successful. As such, there are specific protocols that must be
followed in data mining. The desired goals and objectives are
the guiding principles upon which the type of data to be
analyzed is identified. The main goal for Health-Cop is to
establish links between diet composition and health issues.
More specifically, the company will focus on analysis of data
from various health facilities, websites, databases and health

6. journals. The analysis is intended to provide new forms of data
that can be interpreted to give meaningful patterns. To facilitate
the process of data mining, there are several aspects that must
be considered such as: statistics, clustering of data, rules of
association, data classification, visualization and the decision
tree.
Network Description
Health Cop company will set up is network system using both
the windows and Linux based operating system. The company
will have 10 desktop computers and 5 portable computers. The
10 desktop computers will be connected together via a metered
Wi-Fi service. The desktops will be the main engine of the
company. All the desktops will be configured with an algorithm
that constantly searches for specific keywords from various
databases. The portables computers will be connected to the
internet via modems. A modem is much safer since it limits the
connectivity to only the device being used. Internet connectivity
via modem is facilitated through local area networks (LAN),
through to the service providers, (Cui, et.al., 2016). Multiple
firewalls are set up within the company networks to sort out
undesired data traffic from the local network on the computer
devices.
The most suitable firewall for the network would be a layer 3
open systems interconnection (OSI) model, which guarantees
maximum security to the local network systems, (Greenberg,
et.al., 2016). This type of models is well designed to suit
communication within several computers in a standard network
system. The network router will generate IP addresses whose
packets will be used to launch communication between the
computers used. All the data mined through configured search
engine will be relayed to the devices whose IP addresses are
saved in the network. Since all computers will be assigns
specific IP addresses, resource sharing and data transfer will be
effectively done via the network systems.
The network architecture will be designed with two firewalls
configured into a two firewall demilitarized zone (DMZ). The

7. DMZ is located on a neutral level that serves as the linkage and
contact point between Health-Cop network systems and the
internet. This is very crucial for maintaining maximum network
security. This kind of security protocol also ensures that
company networks are not exposed to any threat that may be
launched via internet. Health-cop’s domain name servers will
remain secured and thus the process of translating IP addresses
will be much effective. Interpretation and translation of IP
addresses by the company DNS servers facilitates retrieval of
data from the internet.
The switch devices have been incorporated in the network
architecture since Health-cop networks will be configured on
the Windows server system. Since the system is configured with
layer 3 OSI, enterprise level uses must be used for the process
of packet routing to be successfully executed, (Shatri, et.al.,
2017). This facilitates the transfer of data packets to different
computers in the company system. Switches are much better
compared to hubs in a network because they only relay data
packets to a specific MAC address destination. Windows server
system also requires routers to be installed and configured in
the system to facilitate the application of virtual private
network (VPN) devices.
Figure 1 Network Architecture
The network incorporates a backbone to host the two switches
that have been designed in the network system. The backbone
also facilitates effective communication between multiple
devices operating within the Health-Cop network systems.
Work flow
The first stage of the data mining process will be data
collection. The search algorithms will be configured to detect

8. certain key words from the databases analyzed. The collected
data will then be stored on the network and into the computer
drives. Once the data is stored on the data, the data will be
subjected to screening and cleansing procedures on the network
systems. Afterwards, the data will be classified according to
different clusters and patterns identified through the analysis,
(Adamuz-Hinojosa, et.al., 2018).
Figure 2Network diagram
The analytics will also involve regression predictions and
outlier identification procedures. Finally, the data will be sorted
and the relevant data sent to the network optimization unit
while the irrelevant data will be directed back to the regression
n analysis. The relevant data will be interpreted in the network
optimization units and used to create patterns that explain the
links between a certain dietary behavior with a specific lifestyle
disease.
In conclusion, the company’s network is designed to source for
voluptuous data from various internet sources, databases and the
cloud, and come up with relevant patterns that would be used to
facilitate data analysis and reporting. The entire process must
follow the outlined protocol for success to be achieved.
References
Adamuz-Hinojosa, O., Ordonez-Luciana, J., Ameigeiras, P.,
Ramos-Munoz, J. J., Lopez, D., & Folgueira, J. (2018).
Automated network service scaling in nfv: Concepts,
mechanisms and scaling workflow. IEEE Communications
Magazine, 56(7), 162-169.
Cui, L., Yu, F. R., & Yan, Q. (2016). When big data meets
software-defined networking: SDN for big data and big data for
SDN. IEEE network, 30(1), 58-65.
Greenberg, A., Lahiri, P., Maltz, D. A., Patel, P. K., Sengupta,
S., Jain, N., & Kim, C. (2016). U.S. Patent No. 9,497,039.
Washington, DC: U.S. Patent and Trademark Office.
Shatri, V., Kurtaj, L., & Limani, I. (2017, May). Hardware-in-
the-loop architecture with MATLAB/Simulink and QuaRC for

9. rapid prototyping of CMAC neural network controller for ball-
and-beam plant. In 2017 40th International Convention on
Information and Communication Technology, Electronics and
Microelectronics (MIPRO) (pp. 1201-1206). IEEE.
Running head: BIG DATA APPROACH
6
BIG DATA APPROACH
Big Data Approach
Student's name
Professor's name
Institution
Course Title
Date:
As a result of the enormous amount of data required for
analysis, big data approach has become a necessity for health-
oriented organizations. This approach enables the companies to
perform analysis in a systematic way from data repositories.
The structure of storing data in a dataset is also an important
aspect. The approach offers better statistical power through the

10. tools it provides to organizations (Chen, Mao, & Liu, 2014).
The approach seeks to solve challenges about handling large
amounts of data. The challenges range from data analysis,
storage, capture, visualization as well as information privacy.
This paper will, therefore, seek to discuss the big data approach,
the origin of big data, methods of storing the data as well as the
format of the database that will be used.
Use of semi-custom applications.
Use of semi-custom applications will form a basis for the
handling big data. This technique will employ machine learning
and artificial intelligence. Use of artificial and machine
learning will significantly add value to the organization by
providing platforms for handling big data in an efficient manner
(Li, Li, Wang, Zhu, & Li, 2019). This technique will help in
shaping the data analytics mindset at the Health-cop company.
Customized applications will help the company convert model-
based recommendations of treatment into actual insights that
can be used in treatment of diabetes.
The rationale for using semi-customized applications
According to the prevailing circumstances at Health-cop
company, a semi-customized application would suit the
organization in a better way. Semi customized applications take
relatively short development time. Therefore, it takes a very
short time to deploy these applications. When a semi-
customized application is well constructed, they offer stability
by offering great reliability levels as well as more resilience
(Eapen, & Peterson, 2015). Semi-custom applications are more
flexible offering great service through an extended lifetime,
adaptability as well as their scalability. Lastly, semi-customized
applications offer better quality. Their package components
have robust performance levels. Moreover, they offer high-
quality standards due to their applicability in many
environments.
Source of Big Data
According to statistics by the world health organization,
the prevalence of diabetes disease is about 9% in the unites

11. states of America. Considering these statistics, this number of
people is large. Going further to consider the daily data required
to be fetched each day in monitoring disease in each patient, the
data collected each day is enormous. The cloud platform will
offer daily data collection from patients through the use of
artificial intelligence in collaboration of sensor-based networks
(Aazam, et al, 2014). The internet of things will provide support
for the collection of data through miniaturized sensors. These
miniaturized sensors will then be controlled through artificial
intelligence. Since the cloud platform uses the software as a
service technique. Each patient in the Health-cop database will
have their portals that they can access services from any
environment. Machine learning techniques will help in
identifying patients that require urgent help. Considering all
these actions that are performed on the cloud platform, big data
will be generated as a result.
Storage of Data
From the proposed architectures of data storage done
before, data storage will be handled through cloud storage
facilities. The company aims to implement a cloud data
repository. The cloud platform will provide one to many
replications. One to many replications will provide data
reliability as a failure of one storage node will not affect the
operations in the company. It will also help in consolidating
data from all remote locations, therefore, enabling an analysis
of data at a central point (Jiang, et al, 2014). Storage will
depend on high-speed transmissions of data from the patient's
local location to the cloud storage. This will enable continuous
synchronization of data in the database and therefore enabling
data in the database to be up to date. This will enhance its
reliability and therefore giving a clear reflection of analytics.
Storage in the database will also be supported by high-speed
data acceleration. Cloud storage will enable the semi-
customized data-intensive health support application to collect
data from the sensor sources and pass it over to the cloud
(Sookhak, 2015). Data obtained will be stored by using data

12. segmentation methods. Several segments that will range
according to the type of diabetes disease on is suffering from
will be enhanced. This will enable easier querying and
analyzing data from the database.
Database Formats
Modern technologies have come up with formats that enable
easier storage of biodata. Among the formats, is the Next
Generation Sequencing. Health cop company intends to use this
database format due to its suitability to storing biodata
(Banerjee, & Sheth, 2017). Additionally, the database format is
of an advantage as it will help in providing useful data mining
techniques as well as machine learning techniques that will help
in inputting data into specific data types and formats. The main
agenda towards choosing this format is to enable Health-cop
company store and analyze the data more efficiently
Conclusion
Considering the factors in play at the Health-cop company,
semi-custom applications will help the company achieve its
objectives in handling big data. The Next-generation sequencing
database format will enable the company to store biodata more
efficiently.
References
Aazam, M., Khan, I., Alsaffar, A. A., & Huh, E. N. (2014,
January). Cloud of Things: Integrating the Internet of Things
and cloud computing and the issues involved. In Proceedings of
2014 11th International Bhurban Conference on Applied
Sciences & Technology (IBCAST) Islamabad, Pakistan, 14th-
18th January 2014 (pp. 414-419). IEEE.
Banerjee, T., & Sheth, A. (2017). Iot quality control for data
and application needs. IEEE Intelligent Systems, 32(2), 68-73.
Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey.
Mobile networks and applications, 19(2), 171-209.
Eapen, Z. J., & Peterson, E. D. (2015). Can mobile health

13. applications facilitate meaningful behaviour change?: time for
answers. Jama, 314(12), 1236-1237.
Jiang, L., Da Xu, L., Cai, H., Jiang, Z., Bu, F., & Xu, B. (2014).
An IoT-oriented data storage framework in the cloud computing
platform. IEEE Transactions on Industrial Informatics, 10(2),
1443-1451.
Li, Y., Li, G., Wang, T., Zhu, Y., & Li, X. (2019).
Semicustomized Design Framework of Container
Accommodation for Migrant Construction Workers. Journal of
Construction Engineering and Management, 145(4), 04019014.
Sookhak, M. (2015). Dynamic remote data auditing for securing
big data storage in cloud computing (Doctoral dissertation,
University of Malaya).
Cloud Computing
Cloud Computing
Institution Affiliation
Student Name
Date:

14. Health-cop company is a start -up company will offer data
analytics services to various companies. The company helps
heath facilities through proper service delivery to their clients
through support of data analysis. The company aims at
improving its services through the adoption of innovative
technologies. In a review, the company aims at implementing a
cloud platform to act as data heaven that will support for data
storage as well as predictive data analysis. The company intends
to use the software as a service approach in the cloud computing
environment (Tsai, Bai, & Huang, 2014). Fetching of data will
be performed through the support of the internet of things. This
paper will, therefore, seek to identify how the company intends
to use the cloud platform in its operations.
Goals and Objectives
The company aims to be a leading provider of predictive
data analytics services across the united states. In its goal to
become a diverse company in its service providence, the
company aims at earning profits from its wide range of services
it offers. The main aim is to provide organizations with data
handling support. In this view, the company aims at providing
real-time streams of data analytics. The software as a service
platform will enable the company to have accessibility from
many places in the nation (Khoshafian, 2016). In its mandate,
the organization aims to provide the safest and the most
dependable data facilities that the clients can have confidence
in. The company aims at providing scheduled as well as random
reports on the various predictive data analysis dockets it is
tasked with. The company provides for this intending to provide
a robust structure that will enable its client organization to
perform health information analysis that will enable them to
attain a competitive advantage in privately owned hospitals.
Content Security Policy
Considering the fact that Health-cop company intends to
use a cloud platform in data storage and handling as well as
offering support for data analytics, there will be a need to have
a policy that will ensure the security of data. The expected

15. security is enabled through the website that will be used to
access data from the cloud platform. The content security
policy will help in detection as well as prevention of certain
types of attacks that target cloud platforms (Patil, & Frederik,
2016). A content security policy will be capable of efficiently
handling forms of attacks such as cross-site scripting, browser
hijacking, form jacking as well as ad injecting. The company
plans to have a regularly updated inventory of the first- and
third-party domains, lists of whitelisted domains and a method
of alerting violations of the content security policy. The
company also aims to have a regular update of the policy in
order to ensure that it meets data security standards.
Organizational Structure
Health-cop Company is headed by a chief executive officer
who is in charge of coordinating different departments in the
organization. In his mandate, the chief executive officer is in
charge of fostering a good relationship between the company
and the target client companies. Through his influence, he
makes approval of innovative technology such as the current
impending cloud computing platform. Under the chief
executive officer, lies a business manager and a functional
manager. The business manager ensures that the company is
strategically positioned to perform business (Goetsch, & Davis,
2014). The functional manager coordinates activities that lay
down the structure of the business. He is in charge of
coordinating information technology issues. The three top
bosses are mandated to sit in board meetings that discuss the
reports of the business. There are other supervisors who are I
charge of other smaller departments in the company.
Target Market
The company target all the health facilities across in the state.
The intention of targeting these companies is that they are in a
position to purchase the data storage and analytics plans the
company offers. The company will provide the predictive data
analysis services to companies that are in need to perform
digitized and more efficient market analysis (Liu, 2014). The

16. idea is to enable these companies to identify market niches as
well as to attain competitive advantages. The company will
target these companies through specialized plans that will
enable favourable conditions that are economically viable.
Market Niche
With the many chronic diseases in the country, health
organizations are increasingly having the need to predict the
prevalence of these diseases. Health-cop services will provide
the much-needed reports to health facilities. These will help
health facilities draw plans on how to curb as well as prevent
diseases.
Budget Estimation
With the infrastructure that comes with the cloud
computing platform, it is will be necessary to have a special
room where architectural equipment will be placed. The cost of
building a physically secure room will be incurred. There will
also be a cost incurred in buying a domain that will be used to
access cloud resources. A server will also be procured in order
to support the large network of organizations that will be linked
to the company’s cloud platform. Installing the technology will
also require an investment in financial resources in acquiring
skilled personnel to install and maintain the cloud platform. In
addition, training of human resource personnel in the company
will incur some cost.
Conclusion
A review of current marketing trends in many
organizations indicates that data handling an analysis are key
components of every organization. Every organization strives to
ensure that they can grasp market requirements that gives them
a niche in the market. For these reasons, Health-cop Company’s
cloud computing project fits the market requirements of data
handling and analysis.
References
Goetsch, D. L., & Davis, S. B. (2014). Quality management for
organizational excellence. Upper Saddle River, NJ: Pearson.

17. Khoshafian, S. (2016). Service oriented enterprises. Auerbach
Publications.
Liu, Y. (2014). Big data and predictive business analytics. The
Journal of Business Forecasting, 33(4), 40.
Patil, K., & Frederik, B. (2016). A Measurement Study of the
Content Security Policy on Real-World Applications. IJ
Network Security, 18(2), 383-392.
Tsai, W., Bai, X., & Huang, Y. (2014). Software-as-a-service
(SaaS): perspectives and challenges. Science China Information
Sciences, 57(5), 1-15.
Running head: PROJECT PROPOSAL 1
PROJECT PROPOSAL 4
PROJECT PROPOSAL
Institution Affiliation
Student Name
Data
Start-up Proposal: HEALTH-COP COMPANY
Predicting When and Where Lifestyle & Dietetic Related Health
Issues Are Most Likely to Occur.

18. Introduction
Health-cop company is a data mining company that predicts
health trends and possible illnesses that could be witnessed in
the near future. The company will mainly focus on data mining
and analytics to establish links between diet composition and
health issues in society, (Larose, 2015). The data to be used in
the predictive analytics will mainly be obtained from hospital
databases, nutrition and dietetics websites, health journals as
well as information shared through social media platforms.
Health-cop company intends to predict such issues before they
can become tough to manage.
Goals & Objectives
The main goal is to become a leader in health predictive
analytics in the health sector, improve the level of preparedness
for various health issues, and earn a profit from running the
business. Health-cop’s main objective is to identify certain
lifestyle and dietetic related illnesses that are most likely to be
experienced within a certain region in the near future. The
company will analyze purchases from food stores and groceries
and also analyze the various meals ordered for from various
food joints. The company also aims at providing consolidated
reports on diet composition of various people from various
regions based on data obtained from websites and social media
platforms.
Organizational Structure
The company will be headed by a chief executive officer who
will be in charge of overseeing all operations. A seven-member
board of directors will be selected among data analytics
professionals to undertake the duties of policy formulation and
implementation. Health-cop will have a data mining division,
analytics division, IT department, as well as a human resource
and customer relations departments; each headed by a
departmental manager. An independent division to deal with
business modeling and statistical database creation will receive
data from the analytics division. This division will create
various projections that will be used to make predictions about

19. specific illnesses.
Target Market
The company targets to sell its information to health
departments at various levels of governments. The company will
also provide its analysis to various hospitals for an agreed fee.
Health-cop will also sell its findings to private health care
institutions especially nutritionists and pharmaceutical
organizations. The existing competitors in the market offer
predictive analytics for chronic diseases unrelated to dietetics,
(Sepah, et.al., 2015). Health-cop will majorly focus on lifestyle
and dietetics related illnesses that are easily preventable thus
the company will be unique in the market. The major illnesses
that the company will analyze and report on are diabetics,
obesity, and osteoporosis.
Budgetary Estimation
The start-up will require planning and preparation finances to
facilitate sufficient research before launching the company.
Costs will also be incurred to secure strategically positioned
premises for the company. Acquisition of digital equipment
such as computers and network cables as well as the installation
of internet services will require sufficient funding, (Shah, et.al.,
2018). Other operational expenses that are expected include
salaries and wages for the company’s staff and marketing of the
company and its services in the market.
Conclusion
In recent years, lifestyle-related illnesses have become an issue
for many people in the world, (Peirson, et.al., 2015). The main
factors that contribute to the increased incidence of such
illnesses are changes in lifestyle and dietary behavior. The
reported cases of diabetes, obesity, and osteoporosis have
significantly shot up in recent times. This can all be attributed
to the changes in diet behavior. A preventive analytical
algorithm would be most suitable to manage these illnesses. A
computer algorithm programmed to analyze what is being
consumed in various regions and link the food substance to a
certain lifestyle-related disease would be very important,

20. (Razzak, et.al., 2019). This would facilitate early detection and
application of preventive measures.
References
Larose, D. T. (2015). Data mining and predictive analytics.
John Wiley & Sons.
Peirson, L., Fitzpatrick-Lewis, D., Morrison, K., Ciliska, D.,
Kenny, M., Ali, M. U., & Raina, P. (2015). Prevention of
overweight and obesity in children and youth: a systematic
review and meta-analysis. CMAJ open, 3(1), E23.
Razzak, M. I., Imran, M., & Xu, G. (2019). Big data analytics
for preventive medicine. Neural Computing and Applications, 1-
35.
Sepah, S. C., Jiang, L., & Peters, A. L. (2015). Long-term
outcomes of a Web-based diabetes prevention program: 2-year
results of a single-arm longitudinal study. Journal of medical
Internet research, 17(4), e92.
Shah, N. D., Sternberg, E. W., & Kent, D. M. (2018). Big data
and predictive analytics: recalibrating
expectations. Jama, 320(1), 27-28.