Evaluate assumptions and premises used in developing marketing str.docx

Evaluate assumptions and premises used in developing
marketing strategy:
(add some introduction of 7-8 lines on marketing strategy)
1) Adjusted the sales force depending on when it increased in
the market and we used consumer shopping survey to
understand where the customers like to purchase (10-11lines)
2) Due to our high price in the market and the inflation
increasing… we didn’t change our prices and further decreased
by few cents……. But later as we were going into loss in this
inflation period, we increased our prices but kept them
reasonable compared to our competitors. (reasons and add few
more relevant points to make it big)
3) Advertising- initially we concentrated on aches and chest
congestion. Write about targeting audience
4) Brand relevance:
5) Consumer feedback.. coupons…
6) Attractive tagline:
7) Easy to find….(few lines)…. Changed the packing- got
positive reviews from customers
8) Introducing new products: a) Allround: aches, chest
congestion
b) Allroundplus: nasal congestion, aches, allergy symptoms,
runny nose(introduced in 4th year)
c) Allright
9) we took the decision to drop alcohol for children and young
adults…. Non-drowsy and can driving safe
10) introduced 12hr multi symptom relief: instead of using
tablet every 2hr or 4 hrs… it works for a long time.. less hectic,
easy to carry and for journeys.

Running head: INSERT FIRST 50 CHARACTERS OF TITLE 1
SAMPLE PAPER
Identifying the Best Practices in Strategic Management
Gertrude Steinbeck
ORG500 – Foundations of Effective Management
Colorado State University – Global Campus
Dr. Stephanie Allong
August 6, 2015
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IDENTIFYING THE BEST PRACTICES IN STRATEGIC 2
Identifying the Best Practices in Strategic Management
Strategic management and corporate sustainability are two
important dynamics of
modern-day organizations. It is important for organizational
leaders to have an understanding of
the theoretical applications of strategic management as a means
of addressing corporate
sustainability. The purpose of this paper is to provide
definitions and an understanding of
strategic management and corporate sustainability. An overview
of the Walgreen Company, the
organization of study, is also provided in order to understand
how the company has utilized
strategic management to implement sustainability initiatives for
long-term financial performance.
Strategic Management
The function of management is to plan, organize, lead, and
control the operations of an
organization (Robbins & Coulter, 2007) and includes strategic
management. Strategic
management is an approach in which organizations create a
competitive advantage, enhance

productivity, and establish long-term financial performance.
Chandler (as cited in Whittington,
2008) defines strategy as “the determination of the basic long-
term goals and objectives of an
enterprise, and the adoption of courses of action and the
allocation of resources necessary for
carrying out these goals” (p. 268). Similarly, Wheelen and
Hunger (2008) define strategic
management as the managerial decisions and actions of an
organization that achieve long-run
performance of the business, with benefits such as:
The Strategic Management Model (SMM) provides the
framework for integrating strategic
planning into an organization so that the aforementioned
benefits are realized.
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Strategic Management Model
Research indicates as the concept of strategic management
evolved, many
theoretical models were proposed. Ginter, Ruck, and Duncan
(1985) identify eight
elements of the normative strategic model: vision and mission;
objective setting; external
environmental scanning; internal environmental scanning;
strategic alternatives; strategy
selection; implementation; and control. Long (as cited in Ginter
et al., 1985) stated that
normative strategic management models are an “explicit,
intentional, planned and rational
approach” (p. 581) to management. Similar to Ginter et al.,
Wheelen and Hunger (2008)
established the SMM (see Figure 1) which includes four main
elements: environmental
scanning, strategy formulation, strategy implementation, and
evaluation and control.
Environmental scanning is the monitoring, evaluating, and
extracting of information from

the external and internal environments in order for management
to establish plans and
make decisions. Strategy formulation includes creating long-
term plans for the
organization, including the mission, objectives, strategies and
policies. Strategy
implementation is the process of executing policies and
strategies in order to achieve the
mission and objectives. Evaluation and control require
monitoring the performance of the
organization and adjusting the process as necessary in order to
achieve desired results
(Wheelen & Hunger, 2008).
The SMM assumes the organizational learning theory, which
states that an
organization adapts to the changing environment and uses
gathered knowledge to
improve the fit between itself and the environment. The SMM
also assumes the
organization be a learning organization in which the gathered
knowledge can be used to
change behavior and reflect new knowledge (Wheelen &
Hunger, 2008).
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Environmental
Scanning
Strategy
Formulation
Strategy
Implementation
Evaluation
and
Control
External:
Opportunities
Threats
Mission

Objectives
Strategies
Policies
Programs
Budgets
Procedures
Performance
Societal
Environmental
Task Environmental

Internal:
Strengths
Weaknesses
Structure
Culture
Resources
Figure 1. The strategic management model was adapted from
Strategic management and business policy
(11th ed.) by T. L. Wheelen, & J. D. Hunger, 2008, Upper
Saddle River, NJ: Pearson Prentice Hall.
Corporate Sustainability
In addition to enhancing financial performance through strategic
management,
organizational leaders have the responsibility of increasing
shareholder value through
corporate sustainability (Epstein, 2008). Corporate
sustainability is defined in a variety of
ways. Hollingworth (2009) described a sustainable organization
as “one that strives for
and achieves 360-organizational sustainability” (p. 1). The
author claimed an
organization is sustainable when it can endure, or maintain,

over a long-term without
permanently damaging or depleting resources including: the
organization itself; its human
resources (internal and external); the community/society/ethno-
sphere; and the planet’s
environment. He then claimed that if one of the four resources
is not sustainable, issues
with the remaining resources will eventually develop
(Hollingworth, 2009). Brundtland
(as cited in Epstein, 2008) described sustainability as the
economic development that
addresses the needs of the present generation without depleting
resources needed by
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1
2
3
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future generations Epstein (2008) adds to the definition from a
business perspective by
including corporate social responsibility. Epstein also states
that organizations have a
responsibility to stakeholders to improve management practices
in order to add value by
addressing corporate social, environmental and economic

impacts (Epstein, 2008).
Organizational leaders are the strategic decision makers of a
company and have a
responsibility to stakeholders (Wheelen & Hunger 2008).
Therefore, it is important to
have an understanding of why corporate sustainability is
important, and how the nine
principles of sustainability performance guide strategic
management.
Importance of Corporate Sustainability
In addition to making a profit, organizations have a
responsibility to society,
which includes addressing its economic, social, and
environmental impacts, otherwise
known as social responsibility. Friedman and Carroll had two
opposing views of
corporate social responsibility. Friedman argued that the sole
responsibility of business
was to use resources and activities that enhanced profits
(Wheelen & Hunger, 2008).
Carroll (1979) argued that social responsibility included much
more that making a profit;
he proposed businesses must include the economic, legal,
ethical and discretionary

categories of business performance.
lities include producing goods and
services to meet the
needs/wants of society in order to make a profit;
company is expected to
abide by;
vious two
statements, but also
include the norms and beliefs held by society;
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on by the
organization including voluntary activities and philanthropic
contributions
(Carroll, 1979).
The importance of corporate sustainability, therefore, is that an
organization is
responsible for financial performance, but it also has additional
responsibilities to
stakeholders and society in general.
The Nine Principles of Sustainability Performance
The nine principles, as presented by Epstein and Roy (2003)
(see Table 1), further
define sustainability, are measureable, and can easily be

incorporated into strategic
management (Epstein, 2008). These principles include ethics,
governance, transparency,
business relationships, financial return, community
involvement, value of products and
services, employment practices and protection of the
environment.
A table or figure should fit all on one
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Table 1
The Nine Principles of Sustainability Performance
1. Ethics The company establishes, promotes, monitors and
maintains ethical

standards and practices in dealing with all of the company
stakeholders.
2. Governance The company manages all of its resources
conscientiously and effectively,
recognizing the fiduciary duty of corporate boards and managers
to focus
on the interests of all company stakeholders.
3. Transparency The company provides timely disclosure of
information about its
products, services and activities, thus permitting stakeholders to
make
informed decisions.
4. Business
relationships
The company engages in fair-trading practices with suppliers,
distributors
and partners.
5. Financial return The company compensates providers of
capital with a competitive return
on investment and the protection of company assets.
6. Community
involvement/
economic
development

The company fosters a mutually beneficial relationship between
the
corporation and community in which it is sensitive to the
culture, context
and needs of the community.
7. Value of
product and
services
The company respects the needs, desires and rights of its
customers and
strives to provide the highest levels of product and service
values.
8. Employment
practices
The company engages in human-resource management practices
that
promote personal and professional employee development,
diversity and
empowerment.
9. Protection of the
environment
The company strives to protect and restore the environment and
promote

sustainable development with products, processes, services and
other
activities.
Note. There should be a general note about the table here.
Adapted from “Improving
sustainability performance: Specifying, implementing and
measuring key principles” by M.
Epstein, & M. Roy, 2003, Journal of General Management,
29(1), pp.15-31.
Walgreens Company
Walgreens Company is a retail drugstore that is in the primary
business of prescription
and non-prescription drugs, and general merchandise including
beauty care, personal care,
household items, photofinishing, greeting cards, and seasonal
items (Reuters, 2010). More
recently, the organization diversified its offerings through
worksite healthcare facilities, home
care facilities, specialty pharmacies, and mail service
pharmacies (Walgreens Company, 2010).
When using a Table in your paper, make
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Table number. Then insert the title of the
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Walgreen Company established a strong organizational culture
focusing on consumer and
employee satisfaction. The mission of Walgreens is:
We will provide the most convenient access to consumer goods
and services . . .
and pharmacy, health and wellness services . . . in America. We
will earn the trust
of our customers and build shareholder value. We will treat
each other with

respect and dignity and do the same for all we serve. We will
offer employees of
all backgrounds a place to build a career. (Walgreens, 2010a,
para. 1)
Walgreens was established in 1901 by pharmacist Charles R.
Walgreen Sr. (Walgreens, 2010b).
Prior to establishing the company, Mr. Walgreen struggled with
the direction the pharmacy
industry was headed; the lack of quality customer service and
care for people concerned him.
Today, Walgreens is the largest drugstore chain in the United
States employing over 238,000
people. Sales in 2009 exceeded $63 billion, in which 65% of
sales were from prescriptions
drugs. The organization has expanded into all 50 states, as well
as the District of Colombia and
Puerto Rico, for a total of 7,496 stores and 350 Take Care
clinics (Walgreens Company, 2010,
para. 3).
Conclusion
Strategic management and corporate sustainability are two
important practices in today’s
competitive global environment. In order to effectively
implement strategic management in light

of corporate sustainability, leaders must have an understanding
of such concepts. This paper has
provided a background and understanding of strategic
management and corporate sustainability.
An overview and history of Walgreen Company was also
presented in order to identify best
practices in strategic management that enhance corporate
sustainability.
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References
Carroll, A. B. (1979). A three-dimensional conceptual model of
corporate performance. The
Academy of Management Review, 4(4), 497.
Collins, J. (2001). Good to great. New York, NY: HarperCollins
Publishers Inc.
Epstein, M. J. (2008). Making sustainability work. San
Francisco, CA: Greenleaf
Publishing Limited.
Epstein, M., & Roy, M. (2003). Improving sustainability
performance: Specifying, implementing
and measuring key principles. Journal of General Management,
29(1), 15-31.
French, S. (2009). Critiquing the language of strategic
management. The Journal of Management
Development, 28(1), 6-17. doi: 10.1108/02621710910923836
Ginter, P., Ruck, A., & Duncan, W. (1985). Planners’
perceptions of the strategic management
process. Journal of Management Studies, 22(6), 581-596.

Hollingworth, M. (2009, November/December). Building 360
organizational sustainability. Ivey
Business Journal, 73(6), 2.
Walgreens. (2010a). Mission statement. Retrieved from
http://news.walgreens.com/article_display.cfm?article_id=1042
Walgreens. (2010b). Our past. Retrieved from
http://www.walgreens.com/marketing/about/history/default.html
Reuters. (2010). Walgreen Co. Retrieved from
http://www.reuters.com/finance/stocks/companyProfile?symbol
=WAG.N
Robbins, S. P., & Coulter, M. (2007). Management (9th ed.).
Upper Saddle River, NJ: Pearson
Prentice Hall.
Walgreens Company. (2010). 2009 Annual report. Retrieved
from
List sources in
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from one website, you need
to be able to distinguish

what information came from
each web page. To do this,
you need to add the letters,
“a,” “b,” etc. to the year of
each citation.
http://investor.walgreens.com/annual.cfm
Wheelen, T. L., & Hunger, J. D. (2008). Strategic management
and business policy (11th ed.).
Upper Saddle River, NJ: Pearson Prentice Hall.
Whittington, R. (2008). Alfred Chandler, founder of strategy:
Lost tradition and renewed
inspiration. Business History Review, 82(2), 267-277.
Note: Level Headers 3, 4, and 5 are also used but much less
frequently. Click here for
more information on their format and use.
For more information on CSU-

Global APA requirements for
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References
Carroll, A. B. (1979). A three-dimensional conceptual model of
corporate performance. The
Academy of Management Review, 4(4), 497. [This is a journal
article citation. Articles
from the Library databases are based on print journals so the
citation will end with page
numbers.]
Collins, J. (2001). Good to great. New York, NY: HarperCollins
Publishers Inc. [This is a book
citation.]
Epstein, M. J. (2008). Making sustainability work. San

Francisco, CA: Greenleaf
Publishing Limited.
Epstein, M., & Roy, M. (2003). Improving sustainability
performance: Specifying, implementing
and measuring key principles. Journal of General Management,
29(1), 15-31.
French, S. (2009). Critiquing the language of strategic
management. The Journal of Management
Development, 28(1), 6-17. doi: 10.1108/02621710910923836
[This is a journal article
citation from a Library database. Include a doi number if
available.]
Ginter, P., Ruck, A., & Duncan, W. (1985). Planners’
perceptions of the strategic management
process. Journal of Management Studies, 22(6), 581-596.
Hollingworth, M. (2009, November/December). Building 360
organizational sustainability. Ivey
Business Journal Online. Retrieved from
http://www.iveybusinessjournal.com/article.asp?intArticle_ID=
868 [This is a journal that
is published online, so you would include the URL.]
Reuters. (2010). Walgreens Co. (WAG.N). Retrieved from

http://www.reuters.com/finance/stocks/companyProfile?symbol
=WAG.N
Walgreens. (2010a). Mission statement. Retrieved from
http://news.walgreens.com/article_display.cfm?article_id=1042
[This is a website citation
with a corporate author. If you retrieve information from
various pages of this particular
website, you need to cite each web page. However, because the
author and the year will
be exactly the same, the lowercase letters, “a,” “b,” etc. need to
be added to the year. The
in-text citation would be: (Walgreens, 2010a).]
Walgreens. (2010b). Our past. Retrieved from
http://www.walgreens.com/marketing/about/history/default.html
Running head: FINAL PORTFOLIO PROJECT1
FINAL PORTFOLIO PROJECT8Final Portfolio Project
xxxxxx
ITS - 831 Infotech Importance in Strategic Planning

University of the Cumberlands
Dr. Eric Hollis
March 14, 2020
Abstract
Large volumes of data have characterized the digital world. For
effective management of the organization, digital technology
should be used in the evaluation and analysis of the data. The
data has to be stored, which brings in the concept of data
warehousing, which is integral in the management of the
organization. Through different features or components,
efficiency is assured. The concept of green computing assists in
ensuring that organization is environmentally friendly as they
utilize the technologies in the management of the organizations.
This ensures that the organization is effective in its
undertakings as far as technology is concerned. A case in point
has addressed the final portfolio project in three respective
prompts, prompt one; data warehouse architecture. The second
prompt expounds on the concept of big data and instances on
how it is utilized. The final prompt details the concept of green
computing, especially on how organizations are pursuing the
same.

Introduction
The digital world has led to enormous developments in the
technological world, more so in the data arena. Organizations
have to use the information in management. The data is crucial,
making it be stored in a warehouse known as a data warehouse.
The voluminous data has led to the emergence of big data,
which is used to refer to structured and unstructured data. This
information is critical in the decision-making process. This
paper will evaluate the concepts of the data warehouse by
providing the different components of a data warehouse and
providing the trends in data warehousing. The discussion will
also assess the idea of big data and the demands it is placing on
the organization. Finally, the paper will provide an organization
that has utilized the concept of IT green computing.
Prompt one (Data Warehouse Architecture)
Data warehouses are information systems that contain historical
data from unique or diverse sources. It streamlines the
organization’s reporting and analysis procedures. The version is
unique.
Data warehouse architectures
Single-tier architecture. The architecture aims at minimizing the
size of data prevalent in a particular system. Mostly, the goal is
achieved through the elimination of unwanted data in the data
store. Generally, few firms use the technology. The second
category is the two-tier architecture, which separates the source
and the genuinely accessible data store. By virtue of being un-
extensible, the architecture is not applicable to many users. The
three-tier architecture is used in most platforms. It is composed
of upper, middle, and lower levels. Lower level-The database of
the data warehouse serves as a lower level.
Data warehouse Components
The data store depends on “RDBMS” server. An RDBMS server
is a focus information file composed of several vital elements
that make the state useful, reasonable, and available.
Database
The principal database calls for the establishment of conditions

for data storage. RDMS innovation is used to update the
database (Vermeulen, 2018). This type of use is controlled by
the way conventional RDBMS systems are being improved for
data storage rather than value-based database preparation,
despite the fact. For example, specially specified queries,
multiple tables’ joins, and sums are critical assets that make
them difficult to execute. The data warehouse ships the
corresponding relational database for scalability.
Metadata
The name Metadata offers a sophisticated mechanical idea.
Anyway, it is straightforward. Used for designing, maintaining,
and managing data warehouses. Metadata does essential work by
showing the source, use, quality, and the essential features
associated with a set of the data in the data warehouse. In
addition, characterize how to modify and prepare the data. Meta
data is mostly associated with the data store as it provides the
distinct features of the data stored thereby defining the storage
attributes (Vermeulen, 2018).
Consulting and reporting tools
These tools are classified into different categories that is
reporting and query hosting tools. Reporting Tools can further
be divided into desktop reports (Java T points, 2020).
Application development tools:
In some cases, implicit scientific and graphical tools cannot
meet the organization’s system requirements. In these cases,
application development tools are used to create custom reports
(Vermeulen, 2018).
Data mining tools:
Data mining is a procedure for finding critical new
relationships, patterns, and trends through massive data mining.
Use a data-mining tool to program this procedure (Vermeulen,
2018).
OLAP tools:
These tools rely on the idea of a multi-dimensional database.
This allows users to explore data using complex, multi-
dimensional perspectives (Vermeulen, 2018).

Data warehouse bus
The element determines the data flow. The data store data
stream can be ordered in inbound, upstream, downstream,
outbound, and target order.
DataMart
The data store is the input layer used to send data to the user.
Manufacturing requires a certain amount of investment and
cash, which manifests itself as a potentially large data
warehouse. In any case, no standard meaning for a data bazaar
that varies from person to person (Vermeulen, 2018).
Prompt two (Big Data)
Big data describes large volumes of information, which may be
un-structured or structured that immerses companies in
everyday parks. This has nothing to do with measured data. The
specialization of the organization that stores the data is
important. Analyze big data to get insights that help an
organization make better business decisions and develop
strategic business initiatives (Zakir, J., Seymour, T., & Berg, K.
,2015). The term refers to data that is processed using
traditional methods or complex data. Demonstrations of access
and storage of large volumes of information for analysis have
existed for a long time. Definition of important data using
concept V:
Volume:
Organizations assemble information from a multiplicity of
sources, comprising of business exchanges, smart devices (IoT),
modern hardware, recordings, social media, and restrictions
therefrom (De Mauro, A., Greco, M., & Grimaldi, M., 2016).
Previously, storing of data was problematic, the invention of
cheap storage mediums such as Hadoop and data lakes has made
storage easier.
Velocity:

The Internet of Things have made compulsory for the flow of
information in firms to be astounding. This is measure of
effectiveness in the decision-making process of the firms.
Different technologies such as sensors, and smart meters are
increasing the urge to manage the voluminous of data in real-
time.
Diversity:
Data in the data stores may be in different formats such as
numeric data, recordings in audio or video formats. Texts and
other formats can also be used in the presentation of the
information in the stores
Variability:
Despite the increasing speed and variety of data, the data flow
is capricious. The data flow changes frequently and can change
very significantly. It is difficult, but businesses need to
understand how to monitor the load of Pinnacle data that is
active when something depends on social media, and sometimes,
and sometimes every day (De Mauro et al., 2016).
Veracity:
Veracity simples emphasizes on the quality of the data in the
stores. data originates from such a large variety of sources, it is
difficult to connect, adjust, purge, and modify data through
frames (De Mauro et al., 2016). Companies are in the need of
strong relationships, critical chains, and multiple data linkages.
Regardless of the need to have the strong relationship, it is very
possible for information to overwhelm an organization making
it to go out of control. The importance of big data is not related
to the amount of data, but what it does with it.
Before businesses do anything with big data, they need to
consider how data is sent to countless areas, sources,
frameworks, owners, and customers. There are five essential
steps to be responsible for this beautiful “data texture,”
incorporating traditional structured and unstructured and semi-
structured data (De Mauro et al., 2016).

Establish significant data procedures
At a critical level, big data systems are agreements to monitor
and improve how data is collected, stored, controlled, provided,
and used inside and outside the organization. Big data
techniques give way to commercial outcomes in large volumes
of data.
Recognize abundant data sources
The Internet of Things have been phenomenal in data breaches.
This is because of the increased connections, which lead to
security glitches. Transferring these devices from portable
devices, smart cars, clinical equipment, and equipment to IT
infrastructure is just the tip of the iceberg. You can accurately
decompose this big data in the specified way, and then choose
which data to save and which to save. Other sources of
information include suppliers, customers etc.
Access, manage, and storage of data
Today’s processing framework has the expected speed, strength,
and adaptability, so you can quickly come up with enormous
sums and significant data types. In addition to robust access,
organizations also need a way to embed data, ensure data
quality, manage and store data, and organize data for analysis.
Decompose the data.
Resolve databased decisions
All monitored and trusted data creates trust in analytics and
decision-making. To take seriously, companies need to stick to
full significant data estimates, work in a data-driven way, and
determine decisions that rely on tests introduced by big data
rather than intuition. Organization that are data driven have
succeeded in their operations. The organizations are working
more effectively, they are not surprisingly progressive from an
operational perspective, and are becoming more profitable.
Prompt three (Green computing)
Green computing is the use of computers and related assets in
an environmentally friendly way. This includes the introduction
of low-power central processing unit (CPUs), servers,

peripherals, and the legal processing of electronic waste. Green
computing is the use of computers and their assets in an
environmentally friendly and environmentally friendly way. It
is also characterized by a study of the design, manufacture /
manufacture, use and disposal of computer equipment in such a
way as to reduce its environmental impact. Green computing is
the use of computers and their assets in an environmentally
friendly and environmentally friendly way (Computer and
Computing, 2015). Green computing, also known as green
innovation, is the use of green PCs and related assets. These
methods include the implementation of low-power central
processing units (CPUs), servers and peripherals, as well as
reduced asset utilization and legal disposal of electronic waste
(electronic waste). Perhaps the earliest green computing method
in the United States was the deliberate Energy Star labeling
program, which was created by the Environmental Protection
Agency (EPA) in 1992 to improve the energy efficiency of
various types of equipment. The ENERGY STAR brand has
become a typical sight, especially in display cases for PCs and
notebooks. Europe and Asia have the same plan. A government
order is “yes,” but it is only part of the overall green calculation
(Star, 2010). Change the working habits of PC users and
organizations to limit their negative impact on the global
situation. Organizations can ensure that there are “green” by:
turning off the CPU and all peripherals when inactive. Turn on /
off peripheral devices, such as laser printers, as needed.
Use a fluid gemstone display screen (LCD) instead of a cathode
ray tube (CRT) display.
Use a notebook PC instead of a PC at every conceivable point.
Use power management features to remove hard drives and
programs that appear after a few minutes of waiting. Restrict
the use of paper and adequately reuse wastepaper. Dispose of e-
waste according to government, state, and local guidelines.
Green computing means achieving economics and improving the
use of computing devices. Green IT tests combine
environmentally friendly building tests, energy-efficient

computers and the development of more advanced recycling and
recycling technologies. An accompanying approach is used to
advance the concept of green computing at all potential levels.
Organization should be ecological sensitive through updating
their systems instead of buying new ones or reusing. Use
extended sleep mode or sleep mode while away from your PC.
Buy an energy-efficient scratchpad PC instead of a PC. Activate
power management features to control power usage. Take
appropriate action policies for the safe disposal of e-waste. One
should shut down the computers after completing daily tasks.
Another strategy is the refilling of printer cartridges instead of
purchasing new cartridges. Update your current device instead
of buying another PC.
Conclusion
Data warehousing is crucial in an organization. Through the
various features such as the decision-making platform, the
technology assists in the management. The concept has been
critical in the world of big data, which consists of structured
and unstructured data. In the contemporary world, organizations
that are data driven have excelled in their operation as they
have effectively employed the techniques of data warehousing
effectively. The information is also safely stored and easily
accessible. Data protection is crucial in the era of cyber-crimes;
there are different layers that protect this information. Data
warehousing assist in harmonizing information from different
sources for instance the customers and the suppliers. The digital
era has also been characterized with mass production of
technological devices, which leads to pollution. There comes

the need for “green computing. Any filed has to be environment
conserving. Technology through the concept of green computing
has been crucial in Energy Star, which has been advocating for
the concept. People have to employ different strategies to
reduce the environmental pollution caused by technology.
References
Computing, A., & Computing, G. (2015). Torque resource
manager. online] http://www. adaptive computing.com.
De Mauro, A., Greco, M., & Grimaldi, M. (2016). A formal
definition of Big Data based on its essential features. Library
Review.
Java T Point, (2020) Components or Building Blocks of Data
Warehouse https://www.javatpoint.com/data-warehouse-
components
Star, E. (2010). Energy Star®. Program Requirements for
Residential. https://www.energystar.gov/
Vermeulen, A. F. (2018). Data Science Technology Stack. In
Practical Data Science (pp. 1-13). Apress, Berkeley, CA.
Zakir, J., Seymour, T., & Berg, K. (2015). BIG DATA
ANALYTICS. Issues in Information Systems, 16(2).
Data Warehouse Architecture and Design
Mohammad Rifaiea Keivan Kianmehrb Reda Alhajjb Mick

J. Ridleya
aSchool of Informatics, Bradford University, West Yorkshire,
UK
bDept of Computer Science, University of Calgary, Calgary,
Alberta, Canada
Abstract
A data warehouse is attractive as the main repository of an
organization's historical data and is optimized for reporting
and analysis. In this paper, we present a data warehouse the
process of data warehouse architecture development and
design. We highlight the different aspects to be considered
in building a data warehouse. These range from data store
characteristics to data modeling and the principles to be
considered for effective data warehouse architecture.
1. Introduction
Business communities all across organizations are becoming
increasingly dependent on their ability to quickly access,
easily use, effectively share and efficiently maintain, quality
and timely, business information which they need to help
achieve success in their business objectives. Meeting these
needs is the basis of the business requirements for the
creation and implementation of a data warehouse
environment which will contain, and enable easy access to,
all the required business information. These requirements
include business user needs for: 1) More consistent, quality
information on all aspects of the company's business; 2)
Greater capability to work with information directly, and
therefore quickly satisfy varying informational
requirements; 3) A clear and concise capability to
determine, and understand in their terms, what information

is available and how to access it; 4) Less dependency on IT
professionals; 5) Increased ability to access and work with
enterprise data; 6) Increased ability to create and share
enterprise data; 7) The ability to add value to data when
producing information for analysis or decision making.
Data warehousing processes are used to design and
develop data repositories for efficient enterprise reporting
and decision support systems; data warehouse design and
development already attracted the attention of several
researchers, e.g., [1, 2, 4, 5, 6, 8, 9, 11, 12, 14, 15]. Sen and
Sinha conducted data warehouse related comparative
analysis [13]. Kimball states that a DW is a queryable
presentation for enterprise data and that this presentation
must not be based on an entity-relation model [6,7]. Data
warehouses have become a very important aspect of data
management for businesses. There is no de facto standard
for data warehousing techniques but the basic methods and
processes outlined by Kimball [7], Chaudhuri and Dayal are
an excellent place to start [2].
This paper presents the requirements for a data
warehouse architecture that meets the above enumerated
needs effectively. The main motivation for choosing to build
a data warehouse is to enable users to report on tactical and
strategic information. In other words, the enterprise data
warehouse (see Figure 1) must have a robust, flexible,
adaptable and scalable design and data architecture. This
data architecture is essentially the enterprise's data
infrastructure which maintains data on important historical
and current business information. The data is structured in
an easy to use and access manner for servicing the direct
and immediate analysis and decision support needs of
business users at all levels of the enterprise using methods
and techniques considerably different from those used by

existing transactional production applications for
maintaining and accessing transactional data.
The rest of this paper is organized as follows. Section 2
covers data warehouse design. Section 3 presents data
warehouse data stores. Section 4 describes the data model.
Section 5 outlines enterprise data warehouse design
principles. Section 6 is summary and conclusions.
2. Data Warehouse Construction
Enterprise data warehouse (EDW) data originates from a
variety of different sources. These could include: 1) The
EDW database needs to be designed and integrated in a way
which will eliminate many of the inconsistencies which
have evolved over the years in many of the legacy system
operational databases and local application data stores. 2)
Metadata (technical and business information about the
data) is an integral component of a robust Data Warehouse
infrastructure. Without this information, it will be extremely
difficult for both administrators of the Data Warehouse and
users of the data to know and understand the data means and
its appropriate usage. Metadata is also vital for the
administrators for change management and impact analysis.
3) A metadata repository (see Figure 2) is required to
maintain descriptive information of all available data in the
information warehouse. The structure of the metadata
enables business users with easy retrieval and access to the
required information in a manner which is easily understood
in business terms.
The data quality of these data stores should be managed
by a process of certification, by the owners of the data, to
assure all interested users that the data has met the minimum
threshold levels of acceptable quality. Important factors of
quality, which need to be monitored, include timeliness and
completeness of the data stored in the data warehouse.

Performance indicators are required to enable monitoring.
Some important design characteristics of information
warehouse data-stores which distinguish them from existing
production operational data stores include: 1) None Volatile:
IEEE IRI 2008, July 13-15, 2008, Las Vegas, Nevada, USA
978-1-4244-2660-7/08/$25.00 ©2008 IEEE
58
Real time updates occur to selective data warehouse data
stores. Most data stores are refreshed in batch, not less than
every 24 hours. Time consistent context of data across
different sources need to be maintained. 2) Time Variant: A
3 to 7 year time horizon for maintaining data is normal for
the information warehouse. The 7 year retention is typically
driven by regulatory requirements for the retention of data.
The data is periodic and maintained as a series of snapshots,
taken as of some moment in time. The key structure of data
tables must contain some element of time. 3) Granularized
structure: Data is maintained at various levels of granularity
and summarization. Frequently access data can be pre-
jointed and summarized to enable quick turnaround on
queries and reports. Detailed and atomic level data will be
maintained alongside summarized and pre-calculated data.
New approaches to data storage are evolving such as “multi-
temperature” data storage to minimize costs associated with
maintaining large and multi-year business data. The concept
behind ‘multi-temperature’ data storage strategies is to
optimize data access for more frequently used data and
isolating infrequently accessed data.
EDW minimizes the need to maintain historical

information within the operational application data stores.
Operational data-bases in the production environment will
only maintain historic information if it is absolutely required
for processing in “transaction-based” production
applications. Otherwise, all historical data beyond "current
value" will be maintained in the EDW data stores for access
and use by business users for informational analysis and
reporting purposes. Costs for storing history data will be
optimized by using tables containing different levels of
summarization.
A successful approach in migrating towards an
effectively architected enterprise warehouse environment is
the one which requires much greater levels of involvement
from business users than those typically required in the
development of operational based applications in
production. The best approach involves designing and
building the warehouse data environment one increment at a
time. This way, technical and business community staff can
work closely together through a process of continuous
iteration, to design and implement each component of the
warehouse until the structure and content of the data, in each
component, meets the satisfaction of the business.
The starting point for the migration is the creation of an
EDW data model. Initially the model will include the
definition and confirmation of subject areas (business and
application specific) and high-level list of entities for the
information warehouse data model. This level of the model
will help to chunk out the planned warehouse data
environment into components prioritized by business
requirements, specific needs of business user groups, and
the readiness of the users to move ahead with this initiative.
The design of each enterprise warehouse component will
involve a number of transformation and refinement
activities to the related areas of the EDW.

Once the design is complete, and agreed upon by the
business users, the tables will be generated and populated in
small increments. This will allow users to immediately test
the data and report their satisfaction or request for changes.
Data Management standards and guidelines need to be
established and maintained for ensuring the quality and
integrity of the data in the enterprise warehouse. Procedures
and guidelines also need to be established for handling data
stewardship, data sharing and change management for data
stores within the information warehouse environment.
Figure 1 General enterprise data warehouse
Fi
gure 2 Metadata repository contents
Figure 3 A high level distinction between levels of EDW
3. Enterprise Data Warehouse Data Stores
The design, construction and effective implementation of an
EDW represents a significant variation from the structure
and design of the operational database tables maintained in
the existing operational environments. The structure of the
warehouse will consist of data stores categorized into two
different levels (see Figure 3). Each level is distinguished by
the need to either share the data store across the enterprise
or share the data within a business-unit.
Significant differences exist between the properties and
characteristics of operational data-stores in production

59
environments and those of the EDW data-stores. These stem
from differences in the intended storage and usage of data in
the two environments. Operational data stores are typically
transaction orientated, detailed, and accurate at the moment
of access. The data warehouses data stores are analytical
and reporting orientated; they may include summarized or
refined data and snapshots of data over defined periods of
time.
The following points describe the key characteristics of
corporate-wide shared EDW data stores:
EDW is a collection of shared data-stores which are
subject oriented, integrated, and nonvolatile and time
variant
EDW data-stores are organized within major data subject
areas. These are defined in the Enterprise Data Model and
could typically include areas of common business interest
such as customer, product, arrangements etc.
Integration of the EDW data-stores eliminates many of
the inconsistencies which have evolved over many years
from the many different designs of applications developed
and implemented. Examples of inconsistencies include
encoding, naming conventions, physical attributes, etc.
Integration occurs when data passes from the application
oriented operational environment to the data warehouse.
For each operational application, routines are developed
and run to eliminate data inconsistencies between
individual application data-stores before added to EDW.

EDW data-stores are non-volatile as compared to the
operational environment data-stores. EDW data is loaded
in a series of batch updates and accessed on-line or in
batch. No real-time update to this data occurs in the EDW
environment. In contrast, operational data is regularly
accessed, manipulated and updated a record at a time.
An important distinguishing characteristic of EDW data is
that it is time variant. This shows up in several ways:
The time horizon for the data warehouse is significantly
longer than that of operational systems. A 1 to 2 year
time horizon is normal for operational systems based on
the average lifecycle of transactions; a 3 to 7 year time
horizon of data is normal for the EDW.
Operational databases contain "current value" data -
data whose accuracy is valid as of the moment of
access. As such, current value data can be updated. By
contrast, EDW data is a series of consistent snapshots,
taken as of some moment in time such as end-of-day,
end-of-week, and end-of-month as required enabling
analytics about the business.
The key structure of operational data may or may not
contain some element of time, such as year; month
days, etc. The key structure of the data warehouse
always contains some element of time.
There are significant differences in the levels of detail of
data within the data-stores of the two different
environments:
1. Operational data-stores contain complete levels of
detail of the most current data as captured in the
specific transaction of each operational application.
2. EDW contains data structured at various levels of

detail. This could include an older level of detailed data
(usually in bulk archival storage), a current level of
detailed data, a level of lightly summarized data, and a
level of highly summarized data. Usually a significant
amount of transformation of data occurs as data is
moved from detailed operational level to various levels
of summarized detail in information warehouse level.
3.1 Business Unit Data Stores
EDW consists of data stores both at the enterprise and the
business unit levels. The following include high-level
guidelines for the positioning, development and
implementation of business unit data-stores:
Business unit level data stores (data marts) will be
modeled, implemented, maintained jointly with the
business unit. This includes those business users who are
specifically interested and have the primary need for the
data in the business unit data mart.
The business unit also assumes the role of the data
stewardship of this data. They will be primarily
responsible for "certifying" the integrity and quality of the
data if it is needed for sharing by other business units
across the enterprise.
These business unit data stores will generally have limited
interface needs with other business unit data marts. They
will physically reside on the same technology platform
alongside the EDW and other business unit data marts.
The capability of access from other data marts is available
for authorized users from other business units.
A single enterprise data-model would contain all shared
and non-shared data marts, although strict rules of "de-
coupling" would be employed to ensure operational

independence of various entity types within the model.
3.2 Historical Data
One of the major impacts of establishing and implementing
EDW will be in the handling of historical data. Currently,
the operational data stores in the production environment
handle and maintain both "current value" and historical data
based on the specific applications for which the data store
serves. With the implementation of an effectively designed
EDW, the need to maintain historical data in the operational
application data stores will change.
Historic information should only be maintained in the
operational data-bases to a limited degree, if it is absolutely
necessary for the processing of any production applications
which have been built for updating, accessing or using
transaction based operational data.
In all other cases, historic information and other related
derived information will be maintained in appropriately
designed data-bases within the Data Warehouse
Environment. This is especially the case for historic data
needed for analytical, data mining and reporting purposes.
The purging of data in the Data Warehouse Environment is
determined by the need of the enterprise to maintain history
and regulatory specifications for the retention of data.
At first glance, this may appear to significantly increase
the volume of, and hence the cost to maintain a large
amount of data in the Data Warehouse Environment.
Although, the volume will increase, the costs can be
60

significantly minimized by using effective designs through
the use of different levels of summarization and the
elimination of data duplication generated by disparate
applications.
4. Enterprise Data Model
An important starting point for designing, building and
implementing an EDW is the design and construction of an
appropriate Data-Model for this environment.
However, the creation and construction of an adequate
Data-Model for the Data Warehouse will require the
adoption of new or changed techniques from the Classical
Data-Modeling techniques which have been generally used
for modeling the Operational Environments.
Classical data modeling techniques make no distinctions
between operational and informational/analytical
environments. These techniques merely try to gather and
synthesize the informational needs of the organization
resulting in an Enterprise Corporate Data Model which
adequately covers the operational data needs of the
enterprise, but which does not capture the structural needs
of data which will be stored in the EDW. Another classic
difference is the extensive number of additional data
relationships that are to be considered for analytic purposes.
Enterprise Data Model forms the foundation of the
enterprise’s existing and ‘to-be’ data architecture. They
represent the existing operational data needs of the
enterprise as well provide a template for the integration of
new subject matter data across the enterprise. It is specific in
its scope for representing the structural data requirements of
the data warehouse based on the characteristics of
informational/analytical data.
The Enterprise Data Model for the operational

environment requires extensive transformations and further
refinements if it is to effectively represent the data
requirements of the Data Warehouse. Before proceeding
with the design and construction of the Data Warehouse
data model it is important to understand, and take into
consideration, the following three different levels of data-
models:
The conceptual data model. This is typically called the
entity relationship model (ERD). This level determines
the models "Subject Area" and defines the “what” entities
(at the highest level) belong in each of these areas. The
level also establishes the "scope of integration" which
defines the boundaries of the data model. This scope must
be agreed by the data architect, management and the
ultimate user of the data, before the modeling process
commences. This is the level of the enterprise ERD,
which is a composite of many individual ERDs that
reflect the different views of people across the enterprise.
The logical data model. This level further expands on
the detail within the subject areas and high-level entities
defined in the high-level data model. Very rarely are mid
level models developed at once. The mid level data model
for one major subject area is expanded, then the mid level
model is fleshed out, and so forth. Constructing the
logical data model is the first step towards the data-base
design for the project application.
The physical data model. This is created from the logical
data model merely by extending the logical ERD to
include keys and physical characteristics of the model.
This is the level at which most of the transformation takes
place for refining the ERD and constructing the Data
Warehouse physical model.
The Enterprise Data Model is a very good place to start

the process of building a Data Warehouse. However, there is
some amount of work that needs to be done on this model in
order for it to be readied for the building of the Data
Warehouse. A certain amount of transformation must occur
to create the Enterprise Warehouse Data Model from. The
activities in the transformation are outlined next.
The removal of purely application specific operational
data;
The addition of an element of time to the key structure of
the Data Warehouse if one is not already present;
The addition of appropriate derived data;
The transformation of data relationships into data
artifacts. Artifacts are a way of capturing snapshots of
relationships between entities which change over time.
Accommodating the different levels of granularity found
in the data warehouse;
Merging like data from different tables together;
Creation of arrays of data. Arrays are created by stringing
together multiple occurrences of any given entity in the
operational data store, and creating only one record in
Data Warehouse. This reduces the amount of indexing
required to retrieve multiple occurrences of the same
entity, and can significantly reduce the cost for data
summarizing and accessing for informational reporting.
The separation of data attributes according to their
stability characteristics. This is the act of grouping
attributes of data together based on their propensity for
change.
This list clearly indicates that a significant, carefully
planned and coordinated, effort must be undertaken in order

to effectively "re-fine" Enterprise Data Model for
constructing the Data warehouse Data Model.
An important premise, which cannot be overlooked, is
that the Enterprise Data Model must be current and up-to-
date before proceeding with its refinement. If the Enterprise
Data Model does not adequately represent the most current
business needs and requirements a lot of wasted work may
go into constructing the Data Warehouse Data Model.
5. Enterprise Data Warehouse Principles
The primary objective of the EDW is to create an integrated
and standardized enterprise data foundation which facilitates
improved analytics and reporting leading to better decision
making and problem solving capabilities. It must be flexible
to enable knowledge workers to ask new questions and
ponder new and different approaches to address new needs
and requirements, thereby uncovering new customer needs
and changing business dynamics. The underlying data
management infrastructure provides for:
A holistic integrated and consistent view of the
enterprise’s data.
61
A consolidated single target for directing intuitive
business user access tools and technologies, making data
available to the business in a timely and cost-effective
manner.
To achieve the objective for building the EDW requires
that the data contained in it to be, not only of high quality
but also reliably and consistently interpreted by Business
users. The guiding principles in this section attempt to

outline those characteristics which should be incorporated
into the EDW in order to achieve that vision. It highlights
the importance of common standards and practices in the
development of the various components of the EDW as well
as some of the awaiting gaps and pitfalls.
There is another objective of an EDW which has been
largely adopted by the majority of corporations. This
objective is that the EDW provides a framework and
environment for the capture, retention and reporting of
operational transactional data of the corporation. This
implies that rather than each application being individually
responsible for the archival of its data, the EDW would, in
addition to providing for analytics and reporting of this data,
also provide a "warehousing service" for those applications
with all of the controls, security and retrieval capabilities
necessary to meet the strategic, tactical and operational
business requirements. The EDW will satisfy the audit and
governmental responsibilities of the corporation for the
retention of this data.
The Mission of the Data Warehouse is: “To provide the
business with a standardized, consistent, timely and accurate
information to improve the effectiveness, efficiency and
timeliness of business insights and decisions.”
The Mission of the Information Management team
supporting the warehouse environment is: “Ensure
operational excellence in the management of the
organization’s Information Assets. Maximize the value,
usefulness, accessibility and security of Information.
Efficiently architect, build and support Information

Solution
s”
The EDW is an integrated data foundation offered as a
service by Information Technology groups to all business
units within corporations. All objects implemented within
the EDW (i.e. architecture, models, programs, databases,
software) must adhere to data and technical standards and be
developed consistent with approved procedures.
5.1 Technology and Data Principles
It has been said and written at ad-nauseam that change is the
only constant in business. Additionally, the number of
information and knowledge worker is growing. The
business needs for improved knowledge of the customer,
operational insights, compliance and regulatory
requirements drive an increase in the volume and variety
and periodicity of data stored in an EDW. The number and
complexity of queries are on an exponential trajectory
commensurate with increase in data and users. The EDW,
therefore, must have the technical capabilities and
characteristics which support the aforementioned increase

and requirements. The success of the EDW and optimal
business value of the EDW is dependent on some technical
principles: extensibility, scalability, and availability.
The design and creation of databases within the scope
EDW will follow the enterprise-defined practices for
database development and implementation. The population
of databases within the scope of EDW will be done using
enterprise standard tools. The extract-transform-load
programs will follow accepted guidelines and procedures
regardless of the source of the data. All databases within the
scope of EDW will be data- modeled in compliance
enterprise data standards. An operating manual for the
databases within the scope of EDW will be established. The
operating manual will define such rules as: the frequency of
update/refresh, availability and data retention.
The metadata for each object contained in the EDW will
contain not only information about the data structures and
business rules, but also information about the specific data
in the database (i.e. data source, data target, data quality,
summarization rules, data vintage & retention, etc.).
Any data transformation, mapping, scrubbing or

summarization must occur prior to the placing of the data in
the EDW. Direct updating of databases within the scope
EDW by any means other than the documented update
procedure is prohibited.
5.2 Guiding Principles
This section defines the guiding principles applied to the
organization’s data architecture strategy.
Data will be captured accurately and completely at the
point of contact.
Meta Data will need to be integrated across the
organization, so that it allows end users to communicate
more effectively with IT and allow for increased
efficiency in reporting processes.
Regardless of where we store data within the
organization, the data must be consistent throughout the
organization. Data must have enterprise-wide integrity.
An enterprise strategy will be developed to manage data,
information and knowledge assets.
Develop a data quality strategy that addresses the
information needs of the business.

A clear and consistent definition of data will be supported
through the creation of an enterprise-wide data model.
Corporate data standards will be implemented to
eliminate redundancy and enhance data integrity.
Data is owned by the organization and a data steward and
a data custodian will be assigned to it.
Information accessibility and security will be determined
by data stewards.
The data steward needs to clearly articulate data
classification, entitlement, data definition, rules, security
and privacy.
5.3 Data Usage
In an organization, data is gathered, exchanged and shared.
It produces analytical information, which is managed to
produce knowledge. Leading organizations acknowledge,
support and fully leverage their data assets. Data assets are
grouped into 3 types, based on their purpose:
62

Data: which supports business processes; it is
system/process relevant.
Information: which supports the analysis, reporting and
decision-making; information is created by aggregating
and summarizing data. It has common, user
understandable definition.
Knowledge: which provides the decision support and
learning/discovery; knowledge is created by synthesizing
and categorizing information. It is self-describing. (i.e.
Business Intelligence Data)
In any organization, data has to be assessed and analyzed
based on its usage. Technical infrastructure supports data
retention for future business needs. As the usage changes,
the architecture should be reviewed and possibly revised.
5.4 Data Quality
Information is an important asset that everyone in the
organization has responsibility to maintain and improve.
The relevant policies related to data quality are given next:

Data Accuracy: Individuals who enter, update or delete
data are responsible for quality and accuracy of the data.
Data Consistency & Integrity: Regardless of where data is
stored within an organization, the same data must be
consistent throughout the organization. Data must have
enterprise-wide integrity.
Data Capture: Data will be captured in electronic form as
close to the source of origin as possible.
Data Exchange: Once captured, data will be stored and
exchanged using electronic means to avoid manual
transcription and re-entry.
For improved data quality, an organization needs to:
Consider other users of the data and the business value of
that information to the organization as a whole.
Have the information supplier (e.g. the customer) validate
the information.
Use data items from information systems in the manner
for which they were intended. For example, comment
fields should not be used for storing codes. The system

should be changed to match business needs.
Measure the quality of data and establish programs to
improve that quality.
Enhance the Data Stewardship function to monitor and
improve data quality.
The resulting support for data quality will enhance the
accuracy, completeness and timeliness of data. This
enhanced data quality is a critical building block for future
initiatives. A robust data governance foundation can
complement other initiatives, such as simplifying ongoing
conversions to new applications, extending the life-span of
key legacy applications, effective business partnerships and
enhancing shared service support.
6. Summary and Conclusions
The development of robust enterprise data warehouse
architecture is necessary to eliminate redundancy and to
avoid possible inconsistency in the data stores of an
organization. A data warehouse provides for better issuing
of integrated reports that otherwise might require combining
data spanning different applications within the organization.

A successful data warehouse architecture and design should
consider and incorporate different principles and rules as
extensively described in this paper. Currently, we are
concentrating on how the process described in this paper
could be successfully applied in practice.
References
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63

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/CasqueItalic
/Castellar
/CastellarMT
/CastleT-Bold
/CastleT-Book
/CastleT-Ligh
/CastleT-Ultr
/CataneoBT-Bold
/CataneoBT-Light
/CataneoBT-Regular
/CataneoBT-RegularSwash
/Cathedral-
/CaxtonBT-Bold
/CaxtonBT-BoldItalic
/CaxtonBT-Book
/CaxtonBT-BookItalic
/CaxtonBT-Light
/CaxtonBT-LightItalic
/Celebrity-
/Centaur
/Centaur-Italic

/Century
/Century725BT-Black
/Century725BT-Bold
/Century725BT-BoldCondensed
/Century725BT-Italic
/Century725BT-Roman
/Century725BT-RomanCondensed
/Century731BT-BoldA
/Century731BT-BoldItalicA
/Century731BT-ItalicA
/Century731BT-RomanA
/Century751BT-ItalicB
/Century751BT-RomanB
/Century-Bold
/Century-BoldItalic
/Century-Book
/Century-BookItalic
/CenturyExpandedBT-Bold
/CenturyExpandedBT-BoldItalic
/CenturyExpandedBT-Italic
/CenturyExpandedBT-Roman
/CenturyGothic
/CenturyGothic-Bold
/CenturyGothic-BoldItalic
/CenturyGothic-Italic

/Century-Light
/Century-LightItalic
/CenturyOldStyle-Bold
/CenturyOldstyleBT-Bold
/CenturyOldstyleBT-Italic
/CenturyOldstyleBT-Roman
/CenturyOldStyle-Italic
/CenturyOldStyle-Regular
/CenturySchoolbook
/CenturySchoolbook-Bold
/CenturySchoolbook-BoldItalic
/CenturySchoolbookBT-Bold
/CenturySchoolbookBT-BoldCond
/CenturySchoolbookBT-BoldItalic
/CenturySchoolbookBT-Italic
/CenturySchoolbookBT-Monospace
/CenturySchoolbookBT-Roman
/CenturySchoolbook-Italic
/CenturySchoolbook-Regular
/Century-Ultra
/Century-UltraItalic
/Cerebral-
/Cezanne
/CGOmega
/CgOmega

/CGOmega-Bold
/CgOmega-Bold
/CGOmega-BoldItalic
/CgOmega-BoldItalic
/CGOmega-Italic
/CgOmega-Italic
/CGTimes
/CgTimes
/CGTimes-Bold
/CgTimes-Bold
/CGTimes-BoldItalic
/CgTimes-BoldItalic
/CGTimes-Italic
/CgTimes-Italic
/Chachie
/Chalk
/Chalkboard
/ChalkWide
/Chancellor-
/Chaparral-Display
/CharlesworthBold
/Charlesworth-Bold
/Charlesworth-Normal
/CharlesworthPlain
/CharlotteSansBoldPlain

/CharlotteSansBookItalicPlain
/CharlotteSansBookPlain
/CharlotteSansMediumPlain
/CharterBT-Black
/CharterBT-BlackItalic
/CharterBT-Bold
/CharterBT-BoldItalic
/CharterBT-Italic
/CharterBT-Roman
/ChartreuseParsonsSH
/Cheltenham-Bold
/Cheltenham-BoldItalic
/Cheltenham-Book
/Cheltenham-BookItalic
/CheltenhamBT-Bold
/CheltenhamBT-BoldCondensed
/CheltenhamBT-BoldCondItalic
/CheltenhamBT-BoldExtraCondensed
/CheltenhamBT-BoldHeadline
/CheltenhamBT-BoldItalic
/CheltenhamBT-BoldItalicHeadline
/CheltenhamBT-Italic
/CheltenhamBT-Roman
/Cheltenham-HandtooledBdIt
/Cheltenham-HandtooledBold

/CheltenhamITCbyBT-Bold
/CheltenhamITCbyBT-BoldItalic
/CheltenhamITCbyBT-Book
/CheltenhamITCbyBT-BookItalic
/Cheltenham-Light
/Cheltenham-LightItalic
/CheltenhamThree
/CheltenhamThreeBold
/CheltenhamThreeBoldItalic
/CheltenhamThreeItalic
/Cheltenham-Ultra
/Cheltenham-UltraItalic
/Cheq
/Chesterfield
/ChesterfieldCondensed
/Chicago
/Chick
/ChildishAlpha
/Child's-Play
/ChillerPlain
/Chiller-Regular
/ChinacatThin
/ChiselD
/Chubble
/CircleD

/City-Bold
/City-BoldItalic
/CityComD-Medi
/CityD-Bold
/CityD-Ligh
/CityD-Medi
/City-Light
/City-LightItalic
/City-Medium
/City-MediumItalic
/Civilian-
/ClarendonBT-Black
/ClarendonBT-Bold
/ClarendonBT-BoldCondensed
/ClarendonBT-Heavy
/ClarendonBT-Light
/ClarendonBT-Roman
/ClarendonBT-RomanCondensed
/ClarendonCondensedBold
/Clarendon-Condensed-Bold
/ClassicalGaramondBT-Bold
/ClassicalGaramondBT-BoldItalic
/ClassicalGaramondBT-Italic
/ClassicalGaramondBT-Roman
/Clearface-Black

/Clearface-BlackItalic
/Clearface-Bold
/Clearface-BoldItalic
/ClearfaceGot-Bold
/ClearfaceGothic
/ClearfaceGothicBold
/ClearfaceGothicLH-Black
/ClearfaceGothicLH-Bold
/ClearfaceGothicLH-Light
/ClearfaceGothicLH-Medium
/ClearfaceGothicLH-Roman
/ClearfaceGot-Regu
/Clearface-Heavy
/Clearface-HeavyItalic
/Clearface-Regular
/Clearface-RegularItalic
/Cleaxg-Bold
/Cleaxg--DemiBold
/Cleaxg-Light
/Cleaxg-Medium
/Cleaxgothic-ExtraBold
/Cleaxg-Regular
/Cleaxg-Xlight
/CloisterBlackBT-Regular
/CloisterOpenFaceBT-Regular

/Cmb10
/cmb10
/Cmbsy10
/cmbsy10
/cmbsy5
/Cmbsy6
/cmbsy6
/Cmbsy7
/cmbsy7
/Cmbsy8
/cmbsy8
/Cmbsy9
/cmbsy9
/Cmbx10
/cmbx10
/Cmbx12
/cmbx12
/Cmbx5
/cmbx5
/Cmbx6
/cmbx6
/Cmbx7
/cmbx7
/Cmbx8
/cmbx8

/Cmbx9
/cmbx9
/Cmbxsl10
/cmbxsl10
/Cmbxti10
/cmbxti10
/Cmcsc10
/cmcsc10
/Cmcsc8
/cmcsc8
/Cmcsc9
/cmcsc9
/Cmdunh10
/cmdunh10
/Cmex10
/cmex10
/Cmex7
/cmex7
/Cmex8
/cmex8
/Cmex9
/cmex9
/Cmff10
/cmff10
/Cmfi10

/cmfi10
/Cmfib8
/cmfib8
/Cminch
/cminch
/Cmitt10
/cmitt10
/Cmmi10
/cmmi10
/Cmmi12
/cmmi12
/Cmmi5
/cmmi5
/Cmmi6
/cmmi6
/Cmmi7
/cmmi7
/Cmmi8
/cmmi8
/Cmmi9
/cmmi9
/Cmmib10
/cmmib10
/cmmib5
/Cmmib6

/cmmib6
/Cmmib7
/cmmib7
/Cmmib8
/cmmib8
/Cmmib9
/cmmib9
/Cmr10
/cmr10
/Cmr12
/cmr12
/Cmr17
/cmr17
/Cmr5
/cmr5
/Cmr6
/cmr6
/Cmr7
/cmr7
/Cmr8
/cmr8
/Cmr9
/cmr9
/Cmsl10
/cmsl10

/Cmsl12
/cmsl12
/Cmsl8
/cmsl8
/Cmsl9
/cmsl9
/Cmsltt10
/cmsltt10
/Cmss10
/cmss10
/Cmss12
/cmss12
/Cmss17
/cmss17
/Cmss8
/cmss8
/Cmss9
/cmss9
/Cmssbx10
/cmssbx10
/Cmssdc10
/cmssdc10
/Cmssi10
/cmssi10
/Cmssi12

/cmssi12
/Cmssi17
/cmssi17
/Cmssi8
/cmssi8
/Cmssi9
/cmssi9
/Cmssq8
/cmssq8
/Cmssqi8
/cmssqi8
/Cmsy10
/cmsy10
/Cmsy5
/cmsy5
/Cmsy6
/cmsy6
/Cmsy7
/cmsy7
/Cmsy8
/cmsy8
/Cmsy9
/cmsy9
/Cmtcsc10
/cmtcsc10

/Cmtex10
/cmtex10
/Cmtex8
/cmtex8
/Cmtex9
/cmtex9
/Cmti10
/cmti10
/Cmti12
/cmti12
/Cmti7
/cmti7
/Cmti8
/cmti8
/Cmti9
/cmti9
/Cmtt10
/cmtt10
/Cmtt12
/cmtt12
/Cmtt8
/cmtt8
/Cmtt9
/cmtt9
/Cmu10

/cmu10
/Cmvtt10
/cmvtt10
/CocktailBubbly
/Collegiate-Normal
/ColonnaMT
/ColorFont
/Colossalis-Bold
/Columbia-Bold
/Columbia-DemiBold
/Columbia-ExtraBold
/Columbia-Light
/Columbia-Medium
/Columbia-Regular
/Columbia-Xlight
/Comet
/ComicSansMS
/ComicSansMS-Bold
/ComixHeavy
/ComixHeavyItalic
/ComixHighlight
/ComixHighlightItalic
/ComixItalic
/ComixRegular
/CommercialScriptBT-Regular

/CommonBullets
/CompactaBT-Black
/CompactaBT-Bold
/CompactaBT-BoldItalic
/CompactaBT-Italic
/CompactaBT-Light
/CompactaBT-Roman
/Confetti
/Consolas
/Consolas-Bold
/Consolas-BoldItalic
/Consolas-Italic
/Constantia
/Constantia-Bold
/Constantia-BoldItalic
/Constantia-Italic
/Constantia-Regular
/CooperBlack
/CooperBlack-Italic
/CooperBT-Black
/CooperBT-BlackHeadline
/CooperBT-BlackItalic
/CooperBT-BlackItalicHeadline
/CooperBT-BlackOutline
/CooperBT-Bold

/CooperBT-BoldItalic
/CooperBT-Light
/CooperBT-LightItalic
/CooperBT-Medium
/CooperBT-MediumItalic
/CopperplateGothic-Bold
/CopperplateGothicBT-Bold
/CopperplateGothicBT-BoldCond
/CopperplateGothicBT-Heavy
/CopperplateGothicBT-Roman
/CopperplateGothicBT-RomanCond
/CopperplateGothic-Light
/Copperplate-GothicThirty-OneABAItalic
/Copperplate-ThirtyOneAB
/Copperplate-ThirtyOneBC
/Copperplate-ThirtyThreeBC
/Copperplate-TwentyNineAB
/Corbel
/Corbel-Bold
/Corbel-BoldItalic
/Corbel-Italic
/CordiaNew
/CordiaNew-Bold
/CordiaNew-BoldItalic
/CordiaNew-Italic

/CordiaUPC
/CordiaUPC-Bold
/CordiaUPC-BoldItalic
/CordiaUPC-Italic
/CoronetItalic
/CorvinusSkyline
/Cosmic-Normal
/CosmicPlain
/CosmicTwo-Normal
/CosmicTwoPlain
/Cottage-Normal
/CottagePlain
/CountdownD
/CountrysideNormal
/Courier
/Courier10PitchBT-Bold
/Courier10PitchBT-BoldItalic
/Courier10PitchBT-Italic
/Courier10PitchBT-Roman
/Courier-Bold
/Courier-BoldOblique
/CourierNewPS-BoldItalicMT
/CourierNewPS-BoldMT
/CourierNewPS-ItalicMT
/CourierNewPSMT

/Courier-Oblique
/CourierStd
/CourierStd-Bold
/CourierStd-BoldOblique
/CourierStd-Oblique
/Cow-Spots
/Cow-SpotsItalic
/CrazyCreaturesBold
/CrazyCreaturesBold-Italic
/CrazyCreaturesItalic
/CrazyCreaturesNormal
/Crillee
/CrilleeBT-Italic
/Critter
/CroissantD
/Croobie
/CurlzMT
/Cushing-Bold
/Cushing-BoldItalic
/Cushing-Book
/Cushing-BookItalic
/Cushing-Heavy
/Cushing-HeavyItalic
/CushingITCbyBT-Heavy
/CushingITCbyBT-HeavyItalic

/Cushing-Medium
/Cushing-MediumItalic
/CutiesHand
/Cutout
/CzarBold
/Czar-Bold
/CzarBoldItalic
/Czar-BoldItalic
/CzarItalic
/Czar-Italic
/CzarNormal
/Czar-Normal
/DalmationByZane
/DancinPlain
/Dauphin-Normal
/DauphinPlain
/DavidaBoldBT-Regular
/David-Bold
/David-Reg
/DavidTransparent
/DearTeacher-Normal
/Decorated035BT-Regular
/DellaRobbiaBT-Bold
/DellaRobbiaBT-Roman
/Desdemona

/DeVinneBT-Italic
/DeVinneBT-ItalicText
/DeVinneBT-Roman
/DeVinneBT-Text
/DexGothicD
/DextorD
/DextorOutD
/DFCalligraphicOrnaments
/DFCalligraphicOrnamentsLET
/DFDiversions
/DFDiversionsLET
/DFDiversities
/DFDiversitiesLET
/DiaconiaOldStyle
/DiaconiaOldStyleBlack
/DiaconiaOldStyleHeavy
/DiaconiaOldStyleHeavyItalic
/DiaconiaOldStyleItalic
/DiaconiaOldStyleSmallCaps
/Digiface
/DigitalICG
/DilleniaUPC
/DilleniaUPCBold
/DilleniaUPCBoldItalic
/DilleniaUPCItalic

/DinosaurJrPlane
/DirectionsMT
/DirectoryExpress
/DiscMakersFont1105
/Diskus
/DiskusD-Medi
/DisneyPrint
/DisneySimple
/DomBoldBT-Regular
/DomCasual
/DomCasualBT-Regular
/DomDiagonalBT-Bold
/DomDiagonalBT-Regular
/Donald
/DonCasualRegularSWFTE
/DorchesterScriptMT
/Dotum
/DotumChe
/DriftType
/DucDeBerry
/Dupree
/DupreeLight
/Dustine
/DustineItalic
/DustineSolid

/DustineSolidItalic
/Dutch766BT-BoldA
/Dutch766BT-ItalicA
/Dutch766BT-RomanA
/Dutch801BT-Bold
/Dutch801BT-BoldItalic
/Dutch801BT-ExtraBold
/Dutch801BT-ExtraBoldItalic
/Dutch801BT-Italic
/Dutch801BT-ItalicHeadline
/Dutch801BT-Roman
/Dutch801BT-RomanHeadline
/Dutch801BT-SemiBold
/Dutch801BT-SemiBoldItalic
/Dutch809BT-BoldC
/Dutch809BT-ItalicC
/Dutch809BT-RomanC
/Dutch811BT-BoldD
/Dutch811BT-BoldItalicD
/Dutch811BT-ItalicD
/Dutch811BT-RomanD
/Dutch823BT-BoldB
/Dutch823BT-BoldItalicB
/Dutch823BT-ItalicB
/Dutch823BT-RomanB

/EastBlocICG-Closed
/EastBlocICG-ClosedAlt
/EastBlocICG-Open
/EastBlocICG-OpenAlt
/EckmannD
/Edda
/Editor-
/Editor-Bold
/Editor-BoldOblique
/Editor-Oblique
/EdwardianScriptITC
/Egyptian505BT-Bold
/Egyptian505BT-Light
/Egyptian505BT-Medium
/Egyptian505BT-Roman
/Egyptian710BT-RegularA
/Eklektic-Normal
/EklekticPlain
/Elegance-
/ElegantGaramondBT-Bold
/ElegantGaramondBT-Italic
/ElegantGaramondBT-Roman
/Elephant-Italic
/Elephant-Regular
/EmbassyBT-Regular

/Emboss
/Emboss-Normal
/EmpireBT-Regular
/EmpireRoman
/EnglischeSchT-Bold
/EnglischeSchT-DemiBold
/EnglischeSchT-Regu
/English111AdagioBT-Regular
/English111PrestoBT-Regular
/English111VivaceBT-Regular
/English157BT-Regular
/EngraversGothicBT-Regular
/EngraversMT
/EngraversOldEnglishBT-Bold
/EngraversOldEnglishBT-Regular
/EngraversRomanBT-Bold
/EngraversRomanBT-Regular
/EnviroD
/ErasContour
/ErasITC-Bold
/ErasITCbyBT-Bold
/ErasITCbyBT-Book
/ErasITCbyBT-Demi
/ErasITCbyBT-Light
/ErasITCbyBT-Medium

/ErasITCbyBT-Ultra
/ErasITC-Demi
/ErasITC-Light
/ErasITC-Medium
/ErasITC-Ultra
/ErgoeBold
/ErgoeBoldItalic
/ErgoeItalic
/ErgoeMediumItalic
/ErieBlackBold
/ErieBlackPlain
/ErieBold
/EriePlain
/EspecialKay
/Esprit-Black
/Esprit-BlackItalic
/Esprit-Bold
/Esprit-BoldItalic
/Esprit-Book
/Esprit-BookItalic
/Esprit-Medium
/Esprit-MediumItalic
/EstrangeloEdessa
/Euclid
/Euclid-Bold

/Euclid-BoldItalic
/EuclidExtra
/EuclidExtra-Bold
/EuclidFraktur
/EuclidFraktur-Bold
/Euclid-Italic
/EuclidMathOne
/EuclidMathOne-Bold
/EuclidMathTwo
/EuclidMathTwo-Bold
/EuclidSymbol
/EuclidSymbol-Bold
/EuclidSymbol-BoldItalic
/EuclidSymbol-Italic
/EucrosiaUPC
/EucrosiaUPCBold
/EucrosiaUPCBoldItalic
/EucrosiaUPCItalic
/Euex10
/euex10
/Euex7
/euex7
/Euex8
/euex8
/Euex9

/euex9
/Eufb10
/eufb10
/Eufb5
/eufb5
/Eufb6
/eufb6
/Eufb7
/eufb7
/Eufb8
/eufb8
/Eufb9
/eufb9
/Eufm10
/eufm10
/Eufm5
/eufm5
/Eufm6
/eufm6
/Eufm7
/eufm7
/Eufm8
/eufm8
/Eufm9
/eufm9

/EuphemiaUCAS-Bold
/EuphemiaUCAS-Italic
/Eurb10
/eurb10
/Eurb5
/eurb5
/Eurb6
/eurb6
/Eurb7
/eurb7
/Eurb8
/eurb8
/Eurb9
/eurb9
/Eurm10
/eurm10
/Eurm5
/eurm5
/Eurm6
/eurm6
/Eurm7
/eurm7
/Eurm8
/eurm8
/Eurm9

/eurm9
/EuroMono-Bold
/EuroMono-BoldItalic
/EuroMono-Italic
/EuroMono-Regular
/Eurostile
/EurostileBold
/Eurostile-Bold
/Eurostile-BoldCondensed
/Eurostile-BoldExtendedTwo
/Eurostile-BoldOblique
/Eurostile-Condensed
/Eurostile-Demi
/Eurostile-DemiOblique
/Eurostile-ExtendedTwo
/Eurostile-Oblique
/EurostileRegular
/Eusb10
/eusb10
/Eusb5
/eusb5
/Eusb6
/eusb6
/Eusb7
/eusb7

/Eusb8
/eusb8
/Eusb9
/eusb9
/Eusm10
/eusm10
/Eusm5
/eusm5
/Eusm6
/eusm6
/Eusm7
/eusm7
/Eusm8
/eusm8
/Eusm9
/eusm9
/EwieD
/Exotic350BT-Bold
/Exotic350BT-DemiBold
/Exotic350BT-Light
/Exotica-
/Exotica-Bold
/ExpertEliteNormal
/Expo-Normal
/ExPonto-Regular

Evaluate assumptions and premises used in developing marketing str.docx

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