Abstract research is recommended to ensure the

Abstract

The advancement of
organizations and companies results to the production of massive data from
diverse ranges of the operational environments. Most of the organizations
encounter difficulties on how to manage and make use of real-time data for
decision making. Owing to this, the organization was required to use inadequate information to generate
reports and make decisions. In the
midst of this upheaval, data warehouse was implemented to manage enormous
volume of data from multidimensional operational sources and integrate into a
single repository for easy accessibility. Data warehouse supports organizations
in decision making and enhance the competitive advantage. Despite of the
valuable benefit of data warehouse, organizations have to give emphasis to the features
for effective implementation. Based on the evidence from the literature
review, both organizational
and technical factors need to be
considered prior in the development process to attain its success.
However, more research is
recommended to ensure the sustainable data warehouse in the organization. Hence, this paper presents a detailed analysis on the significant elements for the effective implementation of data
warehouse in the organization.

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Keywords: Data warehouse, ETL, Data warehouse
implementation, Data mart

Contents
1 INTRODUCTION.. 3
1.1 Data Warehouse Development. 3
1.2 Data Warehouse Development
Approaches. 4
1.3 Data Warehouse Implementation. 5
2 LITERATURE REVIEW… 5
3 RESULTS. 6
3.1 Organizational factors. 6
3.2 Technical factors. 7
4 DISCUSSION.. 8
5 CONCLUSION.. 9
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1 INTRODUCTION

A data warehouse (DW) is a pool of data produced to
support decision making; it is also a repository of current and historical data
of potential interest to managers throughout the organization (Sharda et al,
2014). It is characterized as subject oriented, integrated, time varying and
non-volatile. Data warehouse accumulates and integrates data from various
sources into a single repository. Such repository enables businesses to gather,
organize, interpret and use data to enhance transactional practices (Gupta and
Mumick, 2005). Moreover, data warehouse facilitates analytical processing
activities such as querying, reporting, data mining and Online Analytical
Processing (OLAP).

 

During the early 20th Century, most of the
business organizations were technologically advanced and generated large amount
of data from their transactional processes. Thus, they faced complications on managing
growing and fragmented data to produce
real-time reports for decision making (Sharda et al, 2014). In this sense, most
of the organizations emphasized on the effective use of data for real-business decision support (Carr, 2004). By then, the concept of data warehouse was proposed as the technology which can ensure
gathering of data from different sources of operations. Data warehouse
enables the organization to reduce the transaction cost, increases
effectiveness and retain competitive advantage (Zeng, Chiang, and Yen, 2003).
Moreover, it supports the organization to adapt to the current environment, learn from its past
experiences and position itself for the future (Ganczarski, 2006).  Thus,
data warehouse helps organization to manage strategic and real time information
for decision making (Cooper et al., 2000). 

1.1
Data Warehouse Development

Data warehouse development is the
process of defining, designing, testing, and implementing a data warehouse in
an organization. Manning (1999) proposed a data warehouse as an
architectural model which ensures the flow of data from the operational systems
and terminating in the decision support environments. Based on the architecture,
Sharda et al, (2014) divided data ware into two types of architectures;
three-tier architecture which contains operational systems (server), the data
warehouse, and the DSS/BI/BA engine (the application server) and the clients.
Also, two-tier architecture consists of data warehouse and DSS engine from
which both components run on the same hardware platform. Thus, it encounters
difficulties for data access in large data warehouses while; three-tier has a
capability of reducing the resource constraints owing to the separation of
functions. Moreover, the development process needs support from the management such as setting
reasonable time frames and budgets, and user’s involvement to enhance its success
(Sharda et al, 2014). In this conception, data warehousing requires the
integration of various tasks, components and coordinated effort of several
individuals (Kimball, 2006).

Extraction Transformation and Load (ETL) is an
integral component of the data warehousing process which supports reading data
from one or more databases, converting the extracted data from its previous
form into the required form and put the data into the data warehouse. Additionally,
a data warehouse requires online analytical processing (OLAP), data mining
capabilities, client side analysis tools which can handle gathering of data
from different sources to users. The
overall architecture of data warehouse is illustrated in Figure 1 which identifies
the main architecture components and the data flow throughout the system.

 

Figure
1. Architecture of a Data Warehouse (Humphries, Hawkins, & Dy, 1999)

1.2 Data Warehouse Development Approaches

Decision
support is the main goal for the advancement of data warehouse to business organizations.
For the effective decision making, each organization must employ one of the
development approaches according to the organization need.  Sharda et al (2014) identified two approaches;
first, top-down approach initiated by Bill Inmon. It is also known as the
enterprise-wide data warehouse (EDW) approach which uses traditional relational
database tools such as entity-relationship diagrams (ERD) for the development
needs of an enterprise-wide data warehouse. Second, bottom-up (data mart) approach
proposed by Ralph Kimball. It employs the dimensional modeling which starts
with tables.  A data mart is a
subject-oriented or department-oriented data warehouse and is built one at a
time.

However,
there is no approach which is supreme than the other since organizations are
different. Thus, an organization can decide on the approach according to user
demands, the enterprise’s business requirements, and the enterprise’s maturity
in managing its data resources (Sharda et al, 2014).

1.3 Data Warehouse Implementation

Implementing a data warehouse is a challenging process;
it requires much efforts, various resources and time. Also, the complexity is
due to the ability of enhancing standardized, enriched and integrated data for
real time information from several sources. Despite of competitive advantages
offered by data warehouse to organizations, data warehouse projects
need to be considered carefully to avoid risks which are more serious (Sharda et al,
2014). In this sense, data
warehouse implementation also needs a planned effort for success (Goldstein, 2005).
Accordingly, for an organization to maintain the data warehouse usefulness, effective
implementation has to be considered.  Hence, this paper aims to identify the elements for
successful implementation of a data warehouse in the organization by conducting
a systemic literature review.

2 LITERATURE REVIEW  

The literature review anticipated to study data
warehouse implementation state in the organization, with an emphasis on the
corresponding factors for the effective implementation. Searching for materials
was enhanced by databases from Uppsala University as well as Google Scholar. These
two databases always ensure the availability of reliable numerous resources. In
order to engage only the relevant literature, the author narrow searching from the
two sources by identifying only the peer reviewed literature within the subject.
Additionally, the newspaper articles and blog posts were excluded.

The author uses the term “Data warehouse
implementation” to search for literature in all databases. As a result, Uppsala
University database produces 219 results while Google
Scholar yields 215 results. Thus, the author investigates only literatures with
relevant titles in the first phase.  Afterward,
100 papers from both sources were identified for further review.

In the second phase, the author repeated the process
with a more analyzing approach and browses the whole literature and finally concentrating
on the abstract and conclusion to identify the relevance. By then, 50 papers
were selected to guide the last phase of the review.

Owing to time, in the final review phase; the author
decided to use only 22 literatures provided that all results were found by using
the same search term from the databases. Next, after a critical review of the
literature, the following findings in the next section were identified.