Study on smart startup business in preventive medicine using business model canvas

ĐẠI HỌC QUỐC GIA HÀ NỘI
KHOA QUẢN TRỊ VÀ KINH DOANH
---------------------

DƯƠNG CÔNG ĐỨC

STUDY ON SMART - STARTUP BUSINESS IN PREVENTIVE
MEDICINE USING BUSINESS MODEL CANVAS

NGHIÊN CỨU MÔ HÌNH KINH DOANH KHỞI NGHIÊP
THÔNG MINH TRONG LĨNH VỰC Y TẾ DỰ PHÒNG SỬ DỤNG
MÔ HÌNH CANVAS

LUẬN VĂN THẠC SĨ QUẢN TRỊ KINH DOANH

HÀ NỘI - 2019


ĐẠI HỌC QUỐC GIA HÀ NỘI
KHOA QUẢN TRỊ VÀ KINH DOANH
---------------------

DƯƠNG CÔNG ĐỨC

STUDY ON SMART - STARTUP BUSINESS IN PREVENTIVE
MEDICINE USING BUSINESS MODEL CANVAS

NGHIÊN CỨU MÔ HÌNH KINH DOANH KHỞI NGHIÊP
THÔNG MINH TRONG LĨNH VỰC Y TẾ DỰ PHÒNG SỬ DỤNG
MÔ HÌNH CANVAS

Chuyên ngành: Quản trị kinh doanh
Mã số: 60 34 01 02
LUẬN VĂN THẠC SĨ QUẢN TRỊ KINH DOANH

NGƯỜI HƯỚNG DẪN KHOA HỌC: TS. PHẠM VĂN HỒNG

HÀ NỘI - 2019


COMMITMENT
I hereby declare that my thesis titled ―Study on Smart - startup business in
preventive medicine using Business Model Canvas‖ is my own work. The
work has not been presented elsewhere for assessment.
Author

Duong Cong Duc

i


APPRECIATION
I own debt of gratitude to many people who helped to complete this
master thesis. First and foremost, I would like to give a special thanks to my
instructor, Dr. Pham Van Hong, whose supervision helped to guide me during
the elaboration of the thesis and understand better the topics I should develop.
My deep thanks in specific to Hanoi School of Business and
Management, Hanoi National University for the best learning environment for
me to get more knowledge and improve professional skills.
Finally, I would like to thank my friends and family for all the support,
feedback and encouragement transmitted during these last months which were

fundamental to help me keep emotionally stable and enthusiastic about my work.
Author

Duong Cong Duc

ii


TABLE OF CONTENTS
COMMITMENT ................................................................................................ i
APPRECIATION .............................................................................................. ii
LIST OF ABBREVIATIONS ........................................................................... v
LIST OF TABLES ........................................................................................... vi
LIST OF FIGURES......................................................................................... vii
PREFACE ......................................................................................................... 1
CHAPTER 1: THEORICAL AND RESEARCH MODEL .............................. 7
1.1. Startup & Smart – Startup .......................................................................... 7
1.1.1. Startup concept ..................................................................................... 7
1.1.2. Overview of Smart – Startup business ................................................. 7
1.1.3. Overview about Data Warehouse System in Smart – Startup ............. 9
1.1.4. Smart – Startup‘s field of operation ................................................... 17
1.2. Business Model Canvas ........................................................................... 20
1.2.1. Concept of Business Model ............................................................... 20
1.2.2. Background of Business Model Canvas ............................................ 22
CHAPTER 2: REASEARCH METHODOLOGY ......................................... 31
2.1. Desk Review (secondary data) ................................................................. 31
2.2. Primary data ............................................................................................. 31
2.3. Expert Interview ....................................................................................... 33
2.4. Focus Group ............................................................................................. 33
2.5. Location and time of study....................................................................... 34

CHAPTER 3: REALITIES OF SMART- STARTUP BUSINESS IN
PREVENTIVE MEDICINE............................................................................ 35
3.1. Preventive Medicine Industry in Vietnam ............................................... 35
3.1.1. Current situation of preventive medicine industry in Vietnam ......... 35
3.1.2. Vietnamese people health status and the disease pattern .................. 38
iii


3.2. Build Canvas Model for SMART STARTUP ......................................... 40
3.2.1. Customer Segments ........................................................................... 40
3.2.2. Key partners ....................................................................................... 41
3.2.3. Value prosition: .................................................................................. 42
3.2.4. Channels ............................................................................................. 43
3.2.5. Revenue stream: ................................................................................. 43
3.2.6. Customer Relationship ....................................................................... 46
3.2.7. Key activities...................................................................................... 46
3.2.8. Key resources ..................................................................................... 47
3.2.9. Cost structure ..................................................................................... 54
3.3. Smart startup analysis of financial performance ...................................... 56
CHAPTER 4: RECOMMENDATIONS ......................................................... 60
4.1. Governance guidelines ............................................................................ 60
4.2. An integrated system ................................................................................ 61
4.2.1. An integrated information system...................................................... 61
4.2.2. Benefits of the integrated system ....................................................... 62
4.3. Modeling the quality of services provided by providers of preventive
medicine services ............................................................................................ 63
4.4. Training employees to make coordinated, cross-functional working staff .. 63
CONCLUSION ............................................................................................... 64
REFERENCES ................................................................................................ 65
APPENDIX A ................................................................................................. 70

APPENDIX B ................................................................................................. 73

iv


LIST OF ABBREVIATIONS
BI

Business Intelligence

CDCs

Non- Communicable diseases

CDs

Communicable diseases

DW

Data Warehouse

EPI

Expanded Immunization Program

ER

Entity Relationship

ETL

Extraction, Transformation, and Loading

GDPM

General Department of Preventive
Medicine

IT

Information Technology

MRI

Magnetic Resonance Imaging

MVP

Minimum viable product

NEPI

Nationcal Expanded Immunization Program

NGOs

Non Government Organizations

OLAP

Online Analytical processing

OLTP

Online Transaction Process

SMART

Smart Startup

SMIS

Preventive medicine information systems

SSIS

Integration services

SSRS

Reporting services

VAS

Value- added services

v

LIST OF TABLES
Table 1.1: Business model canvas elements and descriptions ........................ 24
Table 2.1: Interview description ..................................................................... 33
Table 3.1 : Revenue Implementation Roadmap: ............................................. 43
Table 3. 2: Revenue utility for mobile subscribers ......................................... 44
Table 3.3: Online application revenue on mobile ........................................... 44
Table 3.4: Software rental revenue ................................................................. 45
Table 3.5 : Total revenue ................................................................................ 45
Table 3.6: Initial investment ........................................................................... 55
Table 3.7: Annual operation cost .................................................................... 55
Table 3.8: Target market volumne and expected revenue .............................. 56
Table 3.9: Assumptions ................................................................................... 57
Table 3.10: Target market share after 5 years ................................................. 57
Table 3.11: Initial investment ......................................................................... 58
Table 3.12: Annual operation cost .................................................................. 59
Table 3.13: Expectes Business effectiveness (5 years) .................................. 59

vi


LIST OF FIGURES
Figure 1.3: DW Development Lifecycle (DWLC) Model (Demarest, 2008). 12
Figure 3.1: Preventative health chart .............................................................. 40
Figure 3.2: DW Architecture Design of SMART STARTUP startup ............ 54

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PREFACE
1. The rationale of the study

The healthcare industry in Vietnam is at a very important juncture. It is
poised on the brink of a transformation, as it adopts new approaches,
including technology, to help consumers manage their health in a better
manner. Preventive medicine is becoming an area of focus in most countries,
and Vietnam is no different.
Due to hectic schedules and lifestyle choices, healthcare takes a
backseat for many people. This results in the development of conditions that
can be debilitating if not fatal unless they are monitored in a timely manner.
Stressful and unhealthy lifestyles have led to a significant increase in the
incidence of diseases. Given how skewed the doctor-patient ratio is in
Vietnam, taking care of one's health becomes even more important. This is
where preventive medicine becomes relevant and Vietnam has an
unprecedented opportunity to become a healthy nation with the ability to take
the right preventive steps.
As consumers continue to gain more awareness and access to
healthcare information over the internet or via alternate mediums, the need for
preventive medicine management system to establish shared data warehouse
and supply value added services is obvious. However, launching a new
enterprise—whether it is a medical - tech start-up, a small business has
always been a hit-or-miss proposition. But recently an important
countervailing force has emerged, one that can make the process of starting a
company less risky. It is Business Model Canvas, which is a strategic
management template model, which helps to create or improve business
strategy. The business model canvas is a great tool to build a business model
in a straightforward, structured way for a startup, which in turn will lead to
1


insights about the customers that business serves, what value propositions are
offered through what channels, and how a startup will make money.

Understanding the breakthroughs of Business Model Canvas in the
context of establishing a startup business, along with practical knowledge in
medical field, the author aimed to study more about Business Model Canvas
in developing preventive medicine management system to establish shared
data warehouse and supply value added services. Therefore, the author
selected the topic " Smart - startup business in preventive medicine using
Business Model Canvas" as the subject of research for thesis of master.
2. Literature Review
In 2011 Eric Ries wrote a book called ―The Lean Startup‖ in which he
documented inexorable logical and lean concepts applicable to start-up
businesses. The Lean Startup methodology and Business Model Canvas is
now considered as a lifebouy for a startup in order to cut up wastes, make up
of its resources and build a proper financial plan.
• Blank, S. (2013) The Four Steps to the Epiphany
Blank was the pioneer in the field and the one who introduced the concept
customer development describing the process for how entrepreneurs should
test and refine business hypotheses through customer conversations. His
book, ―The Four Steps to the Epiphany‖ from 2013, in which he describes the
process of customer development has become a must read for Silicon Valley
entrepreneurs and is highly mentioned in the community.
• Ries, E. (2011) The Lean Startup: How Constant Innovation Creates
Radically Successful Businesses, Penguin Group, London.
Ries is a former student of Blank and has popularized the concept Lean
Startup in his blog and subsequent book ―The Lean Startup‖ from 2008.
He has received a lot of attention with this book and it was therefore
natural to include him in the framework.
2


• Nathan, F and Paul, A (2011) : Nail It then Scale It: The

Entrepreneur's

Guide

to

Creating

and

Managing

Breakthrough

Innovation: The lean startup book to help entrepreneurs launch a highgrowth business
Furr and Ahlstrom has gained a lot of attention in the field recently for
their book ―Nail It Then Scale It‖ from 2011. They provide prescriptive
and hands-on tips to the entrepreneur. they are a good complement to
Blank and Ries, and they are also respected in the Lean Startup
Methodology community.
• Brant, C and Patrick, V (2010):

The Entrepreneur's Guide to

Customer Development: A cheat sheet to The Four Steps to the Epiphany
Brant Cooper and Patrick Vlaskovits have received a lot of attention for
their book ―The Entrepreneur‘s Guide to Customer Development‖. It is
based on the work of Ries and Blank.
• Ash, M. (2012): Running Lean: Iterate from Plan A to a Plan That
Works

Ash Maurya has written the book ―Running Lean‖. In this inspiring
book, Ash Maurya take readers through an exacting strategy for achieving
a "product/market fit" for your fledgling venture, based on his own
experience in building a wide array of products from high-tech to no-tech.
Throughout, he builds on the ideas and concepts of several innovative
methodologies, including the Lean Startup, Customer Development, and
bootstrapping.
In Vietnam, the Lean Startup principles are not yet widely understood
and very little research has been conducted on this topic.
• Phan Anh, Bui (2012), Lean Startup research and application
Schlumberger Vietnam

3

in


Phan Anh did his research in Schulumber Vietnam, a company in oil and
gas field on Lean Manufacturer to give recommendation to improve
manufacturing process of Schlumberger Vietnam
• Nguyen Thanh Minh (2013), Lean production and application to
Vietnamese enterprises
Thanh Minh studied Lean Startup Methodology and experiences of
foreign companies to give opinion and suggestion for Vietnamese
enterprises
• Ngo My Tran and Vo Minh Tri (2016) :Application of management
tools to enhance efficient efficiency in Universities of Can Tho
This research is aimed at identifying all types of wastes existed in the
divisions of Can Tho University and then proposing an action plan to save
costs and improve working efficiency in these divisions. Expert interviews

and a survery of 93 staff members were conducted. The results indentified
10 types of wastes existing in these units including wastes of facilities and
equipment, waste of labor, waste of not harnessing the creativity of staff
members, waste of time, waste of defects, waste of transport, waste of
motion, waste of information disconnection, waste of extra activity and
was

te of extra input. Based on these results, an action plan with the

application of 5S model of lean manufacturing to tackle tangible wastes
(such as facilities and equipment) was proposed to remove existing wastes
in these divisions. Some solutions on personnel and training were also
proposed to support the implementation of the action plan.
Although the Business Model Canvas model has been studied in many
scientific works, the application of this model to a startup business in the
field of preventive medicine in Vietnam is completely a noverty which
have not been studied before. Thus, based on previous studies, the thesis
focuses on developing Business Model Canvas for start-ups in the field of
4


preventive medicine, a new topic in the field of preventive medicine. The
theme is to develop a blueprint for the development of a new business
startup based on the trend of Internet of Things.
3. Research Objectives
 Objective of study:
To build an overall Cloud computing solution in Preventive
Medicine starts with Immunization Information Systems which consists of
a solution to computerize all community health workers‘ job and a portal to
help people track their family‘s immunization doses and registry to receive

Notification SMS.
4. Mission of study:
­

To understand Business Model Canvas in launching a new product and

how they work
­

To write a business plan that follows Business Model Canvas, an

adaptation of Business Model Canvas created in the Lean Startup spirit.
­

To introduce competitive marketing strategies and recommendations

that are useful for Smart - startup business in preventive medicine.
5. Research Questions
This thesis studies Business Model Canvas in developing a business strategy
for preventive medicine management system. Moreover, this study is
expected to provide appropriate business plan when analyzing opportunities,
challenges and competitive strategy solving the following questions:
- What are the key elements in Business Model Canvas Plan for preventive
medicine management system?
- What are the detail Business plan recommend for building Smart - startup
business providing preventive medicine management system to establish
shared data warehouse and supply value added services?

5

6. Object and Scope of Study
 Object of study
Build the strategy to develop Smart- startuup business providing
preventive medicine management system to establish shared data
warehouse and supply value added services using lean startup
methodology.
 Scope of study:
­ Research place: Vietnam preventive healthcare market.
­ Time to expedite: from 2014 to 2017. All data are collected and
analyzed from 2014 to 2017.
7. Thesis Structure:
The thesis consists of 4 chapters:
Chapter 1: Theorical and Research Model
Chapter 2: Reasearch Methodology
Chapter 3: Realities of smart- startup business in preventive medicine
Chapter 4: Recommendations

6


CHAPTER 1: THEORICAL AND RESEARCH MODEL
1.1. Startup & Smart – Startup
1.1.1. Startup concept
Nowadays, it is not difficult to realize a real emergence of
entrepreneurial spirit and more startup activities than ever before. More and
more people are looking for starting their own business rather than working
for someone else to satisfy their own need and desire.
According to Ries (2011), Startup is a human institution designed to
create new products and services under conditions of extreme uncertainty.

Or Dave McClure, an entrepreneur and angel investor who founded the
business accelerator 500 Startups, said:
“A startup is a company that is confused about what its product is, who
its customers are and how to make money”
Indeed, startup is a term used to describe a business working to make
products or provide services to solve contemporary problems or serve current
demands as the solution is not clear and the success is not guaranteed.
1.1.2. Overview of Smart – Startup business
Smart – Startup is a start-up with an idea of building an overall Cloud
computing solution in Preventive Medicine which consists of a solution to
computerize all community health workers‘ job and a portal to help people
track their family‘s immunization doses, to search on health education
programs, and apply for early diagnosis of disease by screening and registry
to receive Notification SMS. This system will have a web and a mobile
application version so that end user can log in and use everywhere and
everytime. SMS services is supplied in kind of Bulk SMS with Brandname of
sender (eg: YTDP Ha Noi) instead of showing mobile phone number so as to
improve trust.
7


Our company name: Smart
Mission statement: taking care of people is at the heart of everything
we do. Caring counts.
Company goal and objectives: number one technology company in
providing medicine services to all the people by combining expert – creation –
technology
Products and services:
•

The Preventive medicine information systems (SMIS) starts with

issuing an ID number for a baby right in 24 hours after being borned and has
the Hepatitis B first dose instead of waiting till having a birth registration
certificate. The ID number owner then will be informed with healthcare
programs and early diagnosis of disease. When deploying all over the nation,
it generates a national database in Immuzation and Vaccination that we can
exploit and provide value-added services (VAS) relating to preventive
medicine fields to the people starting with Immunization Notification SMS.
•

The SMIS are confidential, web-based, ID-based management,

computerized databases that record all immunization doses and preventive
medicine procedures administered by participating providers to persons
residing within a given geopolitical area.
•

In the long term, this product will evolve into an ecosystem not

only for Immunization but all other Preventive Medicine fields like
Communicable diseases (CDs), Non – communicable diseases (CDCs),
nutrion, school health, early diagnosis of disease …. All base on the idea of
having a ID in the centre and other management systems all around then
provide VAS such as Immunization remind, notification SMS, Communicable
diseases alert,… for people.

8

1.1.3. Overview about Data Warehouse System in Smart – Startup
1.1.3.1. Data Warehouse Concepts
Data warehousing is the process of collecting data to be stored in a
managed database in which the data are subject-oriented and integrated, time
variant, and nonvolatile for the support of decision-making. Data from the
different operations of a corporation are reconciled and stored in a central
repository (a data warehouse) from where analysts extract information that
enables better decision making.
Data can then be aggregated or parsed, and sliced and diced as needed in
order to provide information. There are two main authors that are known in the
world of data warehouse design, their approaches to some area of the data
warehousing are different; William Inmon and Ralph Kimball. The approach by
Inmon is top down design while that of Kimball is bottom up design. Most of the
practitioners of Data warehouse subscribe to either of the two approaches.
A Data Warehouse is a subject-oriented, integrated, time-variant, nonvolatile collection of data used in support of decision making processes.
―Subject Oriented‖ means that a data warehouse focuses on the high-level
entities of the business and the data are organized according to subject.
―Integrated‖ means that the data are stored in consistent formats,
naming conventions, inmeasurement of variables, encoding structures,
physical attributes of data, or domain constraints. For example, whereas an
organization may have four or fiveunique coding schemes for ethnicity, in a
data warehouse there is only one coding scheme.
―Time-variant‖ means warehouses provide access to a greater volume
of more detailed information over a longer period and that the data are
associated with a point in time such as month, quarter, or year. Warehouse
data are non-volatile in that data that enter the database are rarely, if ever,
changed once they are entered into the warehouse. The data in the warehouse
9

are read-only; updates or refresh of the data occur on a periodic, incremental
or full refresh basis. Finally,‖nonvolatile‖ means that the data do not change.
Data warehouse is the conglomerate of all data marts within the
enterprise. Information is always stored in the dimensional model. Kimball
views data warehousing as a constituency of data marts. Data marts are
focused on delivering businessobjectives for departments in the organization.
And the data warehouse is a conformed dimension of the data marts. The data
warehouse is the sum of all the data marts, each representing a business
process in organization by a means of a star schema, or a family of star
schemas of different granularity.
Some of the Data Warehouse characteristics to include:
• It is subject-oriented.
• It is non-volatile.
• It allows for integration of various application systems.
• It supports information processing by consolidating historical data.
• Data is stored in a format that is structured for querying and analysis.
• Data is summarized. DWs usually do not keep as much detail as
transaction-oriented systems.
1.1.3.2. The Data Warehouse Data Model
There are three levels in data modeling process: High-level modeling
(called the ERD, entity relationship level) which features entities, attributes
and relationships, Mid-level modeling (called the data item set) which is data
set by department, and Low-level modeling (called the physical model)
optimize for performance.
After the high-level data model is created, the next level is
established—the midlevel model. For each major subject area, or entity,
identified in the high level data model, a midlevel model is created. Each area
is subsequently developed into its own midlevel model.
10

The physical data model is created from the midlevel data model just
by extending the midlevel data model to include keys and physical
characteristics of the model. At this point, the physical data model looks like a
series of tables, sometimes called relational tables.
There are things that can be used to differentiate the DW from an ordinary
archive database which can easily become a dumping ground. Data is conformed
(Data elements are conformed so that the definitions of "customer" or "revenue"
mean the same thing no matter where the originated), Data is historical (view of
the business at a particular point in time), Data is shared (Can be queried or
otherwise accessed has little value), Data is comprehensive (Can be captured and
consolidated from multiple systems).
1.1.3.3. DW Modeling Techniques
Database warehouse modeling is the process of building a model for the
data in order to store in the DW. There are two data modeling techniques that
are relevant in a data warehousing environment are Entity Relationship (ER)
modeling and dimensional modeling.
ER modeling produces a data model of the specific area of interest,
using two basic concepts: entities and the relationships between those entities.
Detailed ER models also contain attributes, which can be properties of either
the entities or the relationships.
Dimensional modeling uses three basic concepts: measures, facts, and
dimensions. Dimensional modeling is powerful in representing the
requirements of the business user in the context of database tables. Measures
are numeric values that are can be added and calculated.
1.1.3.4. Developing Data Warehouse
Planning the developing and deployment of a standard data warehouse
should be taken as an IT project, hence what made IT project fail applies also
applies when developing data warehouse; thus the need for Project Planning
11

and following the system development life cycle. There is the need for careful
planning, requirements specification, design, prototyping and implementation.
The cyclical model entails five stages which are described below

Design

Enhance

Prototype

Operate

Deplay

Figure 1.3: DW Development Lifecycle (DWLC) Model (Demarest, 2008)
Where the Design stage takes information from both available data
inventories and analyst requirements and analytical needs, of robust data
models and turns it into data marts and intelligent information. The Prototype
deployment stage, where group of opinion-makers and certain end-user
clientele, are brought in contact with a working model of the data warehouse
or data mart design, suitable for actual use. The purpose of prototyping shifts,
as the design team moves back and forth between design and prototype.
Deploy stage is the stage of formalization of user-approved prototype for
actual production use. The Operation is the dayto-day maintenance of the data
warehouse or mart, the data delivery services and client tools that provide
analysts with their access to warehouse and the management of ongoing
extraction, transformation and loading processes that keep the warehouse
current with respect to the authoritative transactional source systems.

12


Enhancement

stage

is

where

external

business

conditions

change

discontinuously, or organizations themselves undergo discontinuous changes
enhancement moves seamlessly back into fundamental design, if the initial
design and implementation did not meet requirements.
1.1.3.5. Business Intelligence Concepts
Initially, BI was coined as a collective term for data analysis tools.
Meanwhile, the understanding broadened towards BI as an encompassment of
all components of an integrated decision support infrastructure. In BI systems,
data from OLTP is combined with analytical front ends to ―present complex
and competitive information to planners and decision makers‖.
A central component of BI systems is the data warehouse (DW), which
integrates data OLTP for analytical tasks.

From the managerial approach, BI is seen as a process in which data
from within and out the organisation are consolidated and integrated in order to
generate information that would facilitate quick and effective decision-making.
The role of BI here is to create an informational environment and process by
which operational data gathered from transactional systems and external sources
can be analyzed and to reveal the ―strategic‖ business dimensions. From this
perspective emerge concepts such as ―intelligent company‖: one that uses BI to
make faster and smarter decisions than its competitors.
―Intelligence‖ means reducing a huge volume of data into knowledge
through a process of filtering, analyzing and reporting information.
The technological approach presents BI as a set of tools that supports
the storage and analysis of information. The focus is not on the process itself,
but on the technologies that allow the recording, recovering, manipulation and
analysis of information.
Whether managerial or technological, there is one shared idea among
all these studies: (1) the core of BI is information gathering, analysis and use
13


and (2) the goal is to support the decision making process, helping the
company‘s strategy. Taking into account the scarce literature, we looked for
other areas that could help us reach a more comprehensive understanding of BI.
We find contributions in three distinct topics: information planning,
balanced score card and competitive intelligence. Here are some benefits that
business intelligence offers and how they can help the entertainment industry
to make and distribute creative substance and stay aloft of the game:
• Product profitability: How much profit does a particular item
contribute? How does item‘s profit break down across business units, media
and distribution channels? What are the specific costs and expenses associated
with producing the item? What percent of revenue or profit do they represent?

• Customer and market analysis: What are the key demographic
characteristics of customers by product? Which other products do they tend to
buy? Does the data indicate that an underserved market segment has greater
revenue potential?
• Channel analysis: Which channels reach what types of consumers?
How profitable is each channel? How will channels be affected by changing
technologies, as well as the emergence of new channels?
• Forecasting and planning: What are the market potential of a new
product, and how much investment should be made? How will a new release
perform and what will its profit contribution be? What level of supply will
adequately meet demand?
The result – employees can now access detailed sales data from around
the world, which was previously not possible, and they are also able to run
sophisticated self-service reports that provide granularity and a near real-time
view into sales performance, ultimately helping these users make informed
decisions that drive the results of the business. In addition to sales data, media

14


companies can measure marketing and promotion effectiveness and monitor
corporate performance and results.
BI not only converts raw data into intelligent information, but also
allows business users to access the right information at the right time and able
to transform it into smart decisions.
Medical companies with its business processes based on such intelligent
information can disrupt its competitor‘s moves, strategize a sustainable
competitive edge, tap into new customer bases, retain existing customer bases,
increase operational efficiencies and be better prepared for the future.
1.1.3.6. Data Warehousing versus Online Transactional Processing (OLTP)

Data warehouse are also known as Online Analytical processing
(OLAP) system because they serve managers and knowledge workers in the
field of data analysis. Online transaction processing (OLTP) systems or
operational systems are those information systems that support the daily
processing that an organizational does. OLTP system‘s main purpose is to
capture information about economic activities of an organization. On might
argue that the purpose of OLTP system is to get data into computers, whereas
the purpose of data warehouse is to get data or information out of computers
OLTP system is customer-oriented as opposed to a data warehouse that
is market-oriented. It is a bit difficult to combine data warehousing (OLAP)
and OLTP capabilities in one system. The dimensional data design model
used in data warehouse is much more effective for querying that the relational
model used in OLTP systems. Furthermore, data warehouses may use more
than one database as a data source. The dimensional design is not suitable for
OLTP systems mainly due to redundancy and loss of referential integrity of
the data. Organisations choose to have two separate information systems, one
OLTP and One OLAP system.

15


1.1.3.7. Data Warehouse Design Concepts
The design of the database depends on the approaches of the father of data
warehouse developers. The two-design processes are referred to as Top-down
process, as described by Bill Inmon and Bottom-up as described by Ralph
Kimball. These are explained in detail below.
Top-Down Model
These was Introduced by Bill Inmon, The process begins with an
Extraction, Transformation, and Loading (ETL) process working from legacy
and/or external data sources. Extraction transformation, process data from

these sources and output it to a centralized Data Staging Area. Following this,
data and metadata are loaded into the Enterprise Data Warehouse and the
centralized metadata repository. Once these are constituted, Data Marts are
created from summarized data warehouse data and metadata. In the top-down
model, integration between the data warehouse and the data marts is
automatic as long as the discipline of constituting data marts as subsets of the
data warehouse is maintained.
Bottom-Up Model
The central idea in Bottom-up model is to construct the data warehouse
incrementally over time from independently developed data marts. The
process begins with ETL for one or more data marts. No common data staging
area is required. There is generally a separate area for each data mart. There
may not even be standardization on the ETL tool.
For the purpose of the thesis, Bottom-up model approach would be
adopted in Smart - Startup which is the Kimball‘s development lifecycle, this
states with one data mart (e.g. Sales) later on further data mart are added.
Data flows from sources into data marts, then into the data warehouse. It is
also implemented in stages (faster) Due to the time constraint and project
limitation, it is easier to complete a process for a subset of a company based
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