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 & …
Integrated Architecture of Data Warehouse with
Business Intelligence Technologies
Ch Anwar ul Hassan, Rizwana Irfan, Munam Ali Shah
Department of Computer Science.
COMSATS Institute of Information technology Islamabad,
Pakistan
[email protected], [email protected], [email protected]
Abstract—Business Intelligence (BI) is the process to extract
information from data then get knowledge from that
information to take the decisions. This paper shows the
effectiveness of BI technologies with data warehouse, for
decision making. In this paper, we deploy the Integrated
Proposed Architecture (IPA) on W category hospital in
order to manage and monitor the data effectively for

analysis and decision making. The accuracy of IPA is 93%
in term of information analysis that is 6% better than
Traditional Data warehouse Architecture (TDA). The IPA is
also able to support dashboard management,
multidimensional data model, perform online analytical
processing, perform user authentication and generate
dynamic reports via BI technologies.
Keywords-Data warehousing, Business Intelligence, Data
Analysis, Architecture
I. INTRODUCTION
Process design and automation are progressively
increased to enhance the quality and productivity of the
organization. Every organization performed different
processes to maintain their operation data in
organizational databases. In the organizational databases,
the data is growing day by day. In every moment updated
data is available for processing. Integrated the available
data in a structured form and store in to a single
repository. On that repository we perform analytical
processing for the decision making so, the concept of Data
Warehouse (DW) has been evolved. Where data is
processed and then stored for future use.

Data warehouse address two main requirements of
business enterprises:
• Data integration
• Decision support system.
Multiple approaches are proposed to build Data
Warehouse (DW). Most common approaches are; top-
down, bottom-up, hybrid, and federated [1]. Before
selecting any approach to implement or build DW, it is
essential to determine the organizational standard. On the
base of determined standard, pick-up the suitable
architecture that satisfies the organization needs [2].
Business Intelligence (BI) technologies in data
warehousing environment provide flexibility to manage
and monitor the data also provide flexibility to perform
effectively analytical processing. Several BI architecture
are already proposed. In [3], Author perform Online
Analytical Processing (OLAP) and dashboard
management was done in [5]. These architectures are
varies according to their structures such as their processes,

components and relationships Data warehouses are built to
collect and integrate the data from heterogeneous sources
and then restore the data for analysis and decision making.
Nowadays, organizations required a well-organized
data storage and retrieval systems to perform analytical
processing and make successful business decisions. For
that organizational purpose, we are proposing an
integrated DWA to make effective business decision using
BI technologies. BI provides current, historical, predictive
and perspective view of business processes. We also
perform analytical processing, query reporting, data
mining, process mining, performance management
dashboards, and prescriptive and predictive analysis using
BI technologies. In this paper we are proposed integrated
Data warehouse Architecture with Business Intelligence
technologies. Using the Integrated Proposed Architecture
(IPA) various comprehensive analyses could be conducted
such as OLAP analysis, reporting, dashboard, slice and
dice.
Rest of the paper is organized as follow: section II
reflects the state of the art, section III problem
formulation. In section IV proposed framework is
described, section V analysis and results. Finally section

VI conclusion and future work.
II. RELATED WORK
Many researchers and practitioner proposed Data
Warehouse Architecture (DWA). In [1], [2] authors,
presented multiple DWA such as; single layer
architecture, multiple layers architecture and compared
them in the terms of efficiency, consistency, integrity and
accuracy.
Researchers also integrate BI and DW to provide
organizational operational platform for decision making.
In [3], Author proposed integrated approach to deploy
DW in BI environment to perform analytical processing.
Ghosh discussed OLAP, an integrated part of BI and
implemented proposed architecture in Fast Moving
Consumer Goods (FMCG) Company. Generate efficient
reports and performed query processing which
incorporates Data Marts (DM), DW and Virtual Data
Warehouse (VDW) [3].
From Educational data is growing rapidly, to manage
the academic records and data we need a well-organized
architecture. Architecture is proposed in Polytechnic

Institute of Leiria for undergraduate Informatics
Proceedings of the 24th International Conference on
Automation & Computing, Newcastle University,
Newcastle upon Tyne, UK, 6-7 September 2018
Engineering degree program to manage educational data
that demonstrate the proficiency of data warehouse [4].
An EduBI framework is also introduced in [5] to collect
educational data from several sources and restore into
single Educational Data Warehouse (EDW) to generate
reports. However, when we get the data into single
respiratory. We can apply multiple data mining techniques
to retrieve the desire results. These results would be used
to find the deficiencies in educational systems and
improved the educational structure. In [6], Author
proposed performance dashboard for Romanian
universities to improve the education quality and integrate
advancement in the scientific and management process of
universities. Dashboards display effective information that
would help for better decision making. However when
developing the performance management dashboard for

universities, DW infrastructure in sense of security and
query system must be perspective and well structured.
Multiple research areas still need to explore in data
warehousing, in this regards researcher also conduct
multiple survey and identify these categories; DW
architecture, DW security, DW design, DW evolution for
future research [7].
Security of the data is also one of the important issues
in DW. In this regards authors proposed different security
frameworks, security models and performed vulnerability
checks to handle the security issues in data
warehousing[8]–[10]. These security measures are
performed in terms of hardware, internet security,
specifically for DW environment [11], [12]. We are
adding authentication layer in our proposed architecture to
handle the security issues.
From last decades, researchers proposed various
solutions to maintain and analyse data effectively in DW.
Authors evolve the DW architecture, their components,
and analysis tool. Proposed architecture oriented, model-
driven approaches [13], [14].

In DW components, ETL is one of the important
component which are intensely influenced by the
changing and evolution of the business requirements and
their complexity. Many researchers and practitioner work
on ETL to make it more effective such that authors
introduced BPML, spatial ETL using Geokettle, Domain-
specific language (DSL) for ETL, Generic transformation
from star schemas to big data or for generating ETL
conceptual schemas, translating physical schema, star
scheme into logical NoSQL schema, , NoSQL Graph-
oriented model [15]–[18]. These are the approaches that
are used to analyse the data more effectively for future
business decisions.
DW analysis tool like OPLA are also optimized to
efficiently extract the valuable knowledge and analyse the
information. In this regards authors introduces Velocity
OLAP, Incremental OLAP, OLPA graphs, OLAP cubes
for multi-dimensional data analysis and complex query
optimization [19]–[25]. In our proposed architecture, we
are using BI technologies to reduce the efforts of
integration the external tool. However, DW is still
exploring field to maintain the data more precisely, in
order to accurately analyse the information. Summary of

literature review is describe in Table 1.
TABLE I. CONCISE SUMMARY OF LITERATURE IN THE
FIELD OF
DATA WAREHOUSING
References Issue discussed/work done
[1]
Proposed different DWA, However never integrate
BI technologies
[2]
Proposed Layered DWA not integrated BI
technologies to optimize performance,
[3]
Integrated DWA, not perform real-time analysis, data
security issues
[4]
Focus on dimension modeling, using SQL and
Graphically language, specific for educational
purpose

[5] Cognos BI Tool, EDWA, not made real-time analysis
[6]
Dashboard Management, Specific for university
portal, performance management
[8] Data warehouse security issues
[9]
Vulnerability check and security models are
proposed
[10] DW security framework
[11] DW hardware, system, internet security measures
[12] Security approaches for DW environment
[13] Architecture approach for DW testing
[14] Model driven approach for DW requirement
[15] Spatial DW using Geokettle
[16] BPML, DSL for ETL

[17]
Transition of conceptual schema into NoSQL logical
schema
[18]
Introduces transformation to big data from star
schemas
[19] Introduced VelocityOLAP
[20]
Automatically creation of DW structure and OLAP
cube
[21] Introduced OLAP on graph data
[22]
HaCube: Map reducer for efficient OLAP cube
materialization and view
[23] Effectiveness of DW in e-Governance
[24]
Query optimization to analyze multi-dimensional

data
[25] Introduced IncrementalOLAP
III. PROPOSED ARCHITECTURE
In recent days decision makers demand effective
knowledge representative and decision support systems, to
take effective business decisions. DW with BI is the
ultimate way to design the effective structure for decision
makers. In DWA ETL, reporting and analytical tools
provides an incorporated atmosphere for making business
decisions and can effectively measure, monitor and
manage business data.
By the proposed architecture we perform;
• Real-time data analysis.
• GUI Based, Less query typing effort (Easy to
analyze for non-technical person).
• More quality data achieved in short time

(Chances of better decision increased).
• Simple architecture (Flexible Model).
• Quick response, efficient view and quality
results.
In this section, we describe the proposed integrated
architecture and concentrate on data quality and flow of
information in the system. Architecture consist up on
these components; data sources, data integration, data
warehouse, data abstraction model, performance
management and end users Figure 1.
Figure 1. Integrated Proposed Data Warehouse Architecture
A. Data Source
Nowadays, data is growing at every moment. To
transform the data into required information and obtain
the desire knowledge from that information, we need well
organized and structured form of data. Data sources
contains structured, unstructured and semi-structured form
of data the reason is that the data is collected from

heterogeneous sources. We need to convert that data into a
structured data for better analysis to take effective and
timely decision.
These different formats of data can be attain from two
types of sources;
• Internal data source
• External data source
Internal data sources are the data that is taken and
sustained by organizational operational systems, data
inside the organization, data that linked to the business
operations. Data that is initiated outside the organization
refer as external data sources. This type of data can be
extracted from external sources such as market
organizations, Internet, business partners, governments.
These data are related to the market, technology,
competitors, and environment.
B. Data Integration
In Data Integration, ETL is performed. ETL contain
three main processes;

• Extraction
• Transformation
• Loading
Extraction is the process to collect or extract the
relevant data from data sources as described above. The
data collected from data sources like internal and external
data sources are redundant, inconsistent, incomplete and
not integrated. Then the extracted data is sent to the data
staging area. It is the temporary data storage area, where
the data is stored to avoid the data extraction need again in
case of any problem. After extraction, data will moved to
transformation and cleansing process. Transformation is
the process of transforming or converting data into
consistent format, according to the business rules that is
used for analytical processing. Standardizing data
definition and describing business logic for data mapping
also includes in data transformation. When data
transformed and cleansed, then stored in staging area
(temporary data storage) to avoid the data transformation
necessity again in case when data loading event fail.

Loading is the final phase of ETL process. In loading, the
data are loaded into target repository from staging area.
C. Data Warehouse:
Data collected from heterogeneous sources are
integrated and converted into structured format then
loaded that data into a single respiratory. This respiratory
is called data warehouse. Inmon defines data warehouse as
a subject-oriented, integrated, time-variant, and non-
volatile collection of data in support of management’s
decision-making process”.
D. Data Abstraction Model
Data abstraction model introduced the best practice in
business intelligence (BI) to take better business decision.
Through data abstraction, we achieve more accurate and
secure data. It can be classified in three layer;
These different formats of data can be attain from two
types of sources;
• Application Layer

• Business Layer
• Physical Layer
The Application Layer provide the platform to the data
consumers to consume the data obtained from business
layer. The Business Layer is established on the standard to
describing significant business entities such as products
and customers. In business layer, we define the data model
and their relations. Typically data modeler work with
experts and data providers define a set of logical view of
the data that represent the business entities. These views
are reusable components for multiple users or consumer in
application layer. Integrated the data sources in physical
layer, into abstraction level. Value-added tasks such as
value formatting, name aliasing, derived columns, and
data type casting, and data quality checks are also
described in physical layer [26]
E. Performance Management
Performance Management facilitate executives to
measure, manage and monitor organization performance
more efficiently and effectively. In Performance
Management, we identify and monitor the performance

metrics and focus on indicators to perform further analysis
at the appropriate detail level. Performance indicator are
related to the organizational objective and strategies.
Monitor the individual and organizational performance
according to these strategies to understand the current
status of business. We also performed ad-hoc query,
reporting, online analytical processing, data visualization
and dashboard management in performance management
area. In few scenarios, web portal is eliminated we
directly communicate to performance management instead
of web portal. The end user consists of tools that display
information for different users in different formats.
F. Authentication Server
Authentication layer is used to secure the information
form unauthorized access. Information security is the
practice to prevent the information from disruption,
disclosure, recording, modification, and destruction.
IV. TRADITIONAL DATA WAREHOUSE
ARCHITECTURE

Traditional Data warehouse Architecture (TDA)
Figure. 2 is taken from [27] in order to compare the
proposed architecture to the traditional architecture. TDA
is described in [1], [2], and [27]. In TDA, OLAP server is
used for analytics. Data abstraction model, Performance
management and Authentication server are not in TDA.
Figure 2. Traditional Data warehouse Architecture [27]
V. IMPLEMENATATION/DEPLOYMENT
We are implementing the Integrated Proposed
Architecture (IPA) and Traditional data warehouse (TDA)
architecture on medical data of W Category hospital to
show the capability of architecture components and
effectiveness of the IPA. IPA plays a significant role by
optimizing the time to perform current and historical data
analysis on medical record.
The medical data of the hospital, which we used for
analysis are in heterogeneous formats. So first we
performed data integration; extract the data from multiple
resources, then transform and clean the data to convert it

in uniform format at the end load the data into data
warehouse.
Now through data abstraction, we communicate to the
data warehouse and design the business models, multi-
dimensional model as shown in IPA to support analysis.
According to these business models as shown in Figure 3.
Figure 3. Multidimenstional Design
We utilized the data in data abstraction model (as data
abstraction is a three-layer process). When the final data is
loaded on data abstraction model continue to the
performance management step (accordance to business
requirements). In performance management, we perform
such as dashboard management, analysis, reporting,
OLAP, slice and dice using BI technologies. Web portal
utilize to display the desired results according to the
demand of user as shown in Figure. 4.
Figure 4. Dashboard Management

Pneumonia, Acute Myocardial infarction, stroke,
pacemaker, cholecystectomy, carotid Endarterectomy, PCI
and PTCA, Cardiac Surgery. These are the different
patient quality measurements. To measure and analyze the
patient data, using IPA we analyze the patient data on a
single click. As in Figure 4 the data of “Pneumonia”
disease is analyzed and described. From the last year data
we analyze that mostly patient in W category hospital
suffer Pneumonia in the month of March.
Pneumonia is a common illness in all part of the
world. It’s a major cause of death among all age groups,
insufficient treatment of Pneumonia leads to an 11 times
higher death rate.
Authentication layer is used for authentication purpose
and also to assign the specific privileges to different users
to maintain the data security.
VI. RESULT ANALYSIS
In result and analysis, we are comparing our
architecture with Traditional Data warehouse Architecture

(TDA) and shows the effectiveness of our proposed
architecture. TDA takes more time for analysis and also
not efficiently and accurately analyze the information that
affects the business decision. We are comparing
architecture in three different aspects. How much
complete and accurate information in aspects of query
performance and analysis, how much system is scalable,
flexible, manage daily load and backup recovery and also
their impact on organization for decision making.
We evaluate the TDA and IPA on three parameters;
Effort, Efficiency/ Response time and Accuracy.
In case of effort, we involved 30 employees to perform
comparatively analysis on the efforts involved to
implement or operate the IPA vs TDA. Each employee
like the proposed architecture except 2, 3 employees.
These 2, 3 employees are those who have already familiar
with ETL. Our Proposed architecture reduce 90% efforts
comparing to TDA. The reason is that proposed
architecture TDA is on development mode (integrate
external tool for analysis e.g. generating reports) and IPA
is on clicked based, easy to understand and don’t need any
external tool for analysis.as shown in Figure 5.

Figure 5. Efforts Rquired for DW
Our proposed integrated data warehouse architecture is
more efficient than traditional architectures. IPA
performed ETL and analysis, using the BI technologies.
So, for ETL and analysis IPA always take less time
compared to one on which first you need to perform ETL
process then use some external tool for analysis. In order
to check the efficiency or response time, we analyze 1GB
data on both architectures. Our IPA respond fast enough,
just in 122 sec and TDA in 431sec (7min and 11 sec) as
described in Figure 6.
Figure 6. Data analysis w.r.t time
Our results shows that the proposed architecture
performed 6 % better than traditional architecture as
results as shown in Figure 7. Proposed architecture
performed 93% accurately analysis as compared to
traditional it gives 87% accuracy. We also performed ad-
hoc query, reporting, online analytical processing, data
visualization and dashboard management through
proposed architecture.

Figure 7. Accuracy in Informaction Analysis
VII. CONCLUSION
The proposed architecture integrate the DW and BI
technologies in order to support analysis, reporting and
decision making. This architecture deployed in W-
category hospital to manage and monitor the health
records to perform analysis.
In future, we deploy IPA in cloud environment and
integrate it with various hospitals to maintain health
records and confirming patient privacy. These health
records will be useful in future to diagnose the diseases by
some common symptoms also identify the specific
number of patients in certain region which was affected by
certain disease.
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10%
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0%
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E

ff
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…
DECISION YEAR 0
-According to the Dashboard, in market update (info° related to
the company and its environment) it’s written that prices
increased an average of 2.9% compared to an inflation rate of
3.1% -> we can therefore increase our prices
-Advertising part: We know that advertising is a very important.
We spent $25.3 million on Allround advertising campaign last
year. But knowing that our company is well established a low
quality advertising will not affect the brand we can decreased
our budget in adversting by changing our agency from BMW
(which quality but charges 15% commission on media
placements) to LLC agency which is low price charges 5% on
media placement). We know that it shouldn’t impact the
consumer behavior.
-We are currently spending $25.3 million and our competitors
23 -> so we can decrease consequently.

-This give us the opportunity to allocate more of our resources
into other fields to such as Digital advertising which overall
increases company promotion.
-We know our digital advertising strategy sometimes get
neglected, however we plan to focus more on that area by
increasing the budget and trying a real department.
-According to the Dashboard (market update) Mass
merchandisers sales showed the strongest growth this period
with an increase of 6.4% -> we have to increase our input into
mass merchandiser as they have the strongest growth in the
period.
Increase Mass merchandiser (currently 26 -> 32)
-We can decrease our budget in Convenienve which has a low
growth rate and where we already are the company that spend
the highest budget ($36.7) -> decrease to $31 (just above
Besthelp).
-We are not going to change Chain drugstores because the
consumers are used to buy products there more often than
elsewhere. But can can reduce Indep drugstores because
according to the shoppinh habits survey consumers don’t buy
products in indep drugstore (to 9) often. Decrease.
-We can also increase the Gorcery channel which represents the

2nd highest growth within the market.
-We also have to focus on retail sales, because they grew by
$62.7 million.
These two channels would hep us to be more present on the
market.
-According to the survey about symptoms reported: Aches,
Chest congestion, and cough -> need to focus on these 3.
-For now we keep our intial product Allround which is a 4hr
multi symptom reliefand one of the most effective for cold
symptoms.
-On the market share based manufacturer sales: we have 41.3%
of the share in the cold market -> our direct competitor is
Besthel who presently has 28.9% of the shares -> focus on this
competitor.
Promotion: We can increase our cons and trade promotion:
currently $8.4 to $10.
We plan to increase our promotion allowance budget because we
have one of the lowest budget (14%), increase to 15% as
Besthelp who is our direct competitor.
-coupons : digital too.

Evaluate assumptions and premises used in developing marketing str.docx

Evaluate assumptions and premises used in developing marketing str.docx

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