The Success from Various Architectures

Abstract

This research has been carried out in order to identify, to what extent the implementation of a data warehouse will impact on an organization and which will be the success from various architectures.

Organizations typically gather information in order to assess the business environment. In order to gain sustainable competitive advantage, and may regard such intelligence as a valuable core competence in some instances. The main aims in this research to help people make "better" business decisions by giving them accurate, current, and relevant information to be available to access when they need it.

The report will research the importance of effectively managing an organization and the magnitude of implementing a data warehouse, as a knowledge management function, and in turn evaluate the key activities carried out by the business that can be done by the system.

An analysis of the key functionalities of a database system that would be best suited for the organization in order to model the system solution will be conducted, as in order for information systems to perform efficiently, they cannot only store data, but will be needed to conduct further activities such as processing of the data. To accomplish such activities with data stored within the information system, business logic of the organization that the system has been designed for will be incorporated into the system. This will allow for a number of tasks that were previously carried out by individuals, to be automated.

CHAPTER 1: INTRODUCTION

BACKGROUND OF THE PROBLEM

Many issues faced by companies are concerned with market saturation so a large amount of work has to be done to remain competitive. As the workload has increased so has the necessity to have our servers achieved work at their maximum level of performance. Data warehouses have been at the forefront of computer applications as a way for management, executives, and business users to effectively use organizational data for decision support and organizational planning. Further, data warehouses have been designed and built as separate technology entities from operational and transactional systems have become the primary repositories for performing business intelligence. This research will provides a broad understanding of data warehouse concepts and why data warehouse implementations have benefited many organizations.

The most important factors of implementing a data warehouse are speed and easy of access to information. An interactive database system offers much greater utility than using just the traditional form of area outreach. Connecting the various global offices of nonprofit organizations is one of the most time consuming processes that most companies face, and it will benefit by implementing a data warehouse which will create a global interaction of the organization.

By implementing this system as the company will be able to cut down on paperwork as all the information will be stored on the database. The company will be able to reach all workers and employees to provide twenty four hour accessibility to details stored within the database in their work spaces. By adopting this 'brick and click' method the organization is able to break into a new technological era. The company moves from its traditional methods of storing information and pursuing ground research using the paper based system. By adopting the pure 'click' method, the organization no longer requires human effort to carry out calculative work, which may include, identifying changing trends in the market, or creating report based on statistical findings, as these will all be automatic features of implementing a database system.

Adopting this system frees human labor from doing repetitive and meticulous work, as it allows the organization to use individuals who are passionate about creating changes in society and the wider community to be able to perform tasks that require their passion and dedication. Their efforts will be shifted from the mundane and routine workings of the typical 'office' setting and can be channeled to carry out more challenging and emotive work that is an immense asset to any organization.

RESERCH OBJECTIVES

The objectives of outsourcing itself must be carefully considered, and this report will attempt to indicate particular motives that an organization will include. This will allow for the report to cover regions of business development including the operational costs incurred by organizations, the inefficient operations of an organization without outsourcing and the redefined delivery of services, whilst ensuring improved efficiency and effectiveness and therefore services of an organization.

The aim of this dissertation is to suggest a data warehouse to storing data for later retrieval. This retrieval is almost always used to support decision-making in the organization. That is why many data warehouses are considered to be DSS (Decision-Support Systems) how best to deliver a data warehouse for an organization

The investigation will commence with the collection of the various details that the organization holds. The investigation will then determine through interviews and careful evaluation, the specifications that will identify the functionalities of the data warehouse.

The investigation will then require careful review of the specifications, after which an actual data warehouse will be constructed that will allow for the details of the beneficiaries of the organization to be stored, processed and retrieved. Finally, the dissertation as a whole will be evaluated according to the extent to which the specifications have been met and its usefulness and effectiveness to the organization.

• The objectives of this study are to, to collect and view day-to-day operations, but its prime task is facilitating tactical planning resulting from long-term data overviews.
• To identify the functionalities required of the system and construct and deliver a system, given the specifications of the organization.
• To produce a report detailing and evaluating the implementation and testing of the system as a whole, drawing conclusions, and stating the significance of the findings. The evaluation of the system will include its usefulness and effectiveness to the organisation.

INTRODUCTION TO DATA WAREHOUSE

In many organizations, users of data are often completely removed from the data sources. In current organizations many people only needed to read-access the data, but still need a quick access to large volumes of data. Such data often comes from various databases. Organizations have increasingly forcing applications in which current and historical data is comprehensively analyzed and explored in order to support high-level decision making. Because many of these analyses performed are predictable. In Data warehouses it's provide access to data for complex analysis, knowledge discovery and vast decision making.

According to Alex Berson and Stephen J. Smith a data warehouse is a database designed for analytical tasks using data from various applications with the following characteristics:

• Relatively small number of users with large amount of data
• Read-intensive
• Updated the issues periodically
• Data contains current and historical
• Contains a large number of tables
• Supports ad hoc, unstructured, heuristic queries

A data warehouse includes past data and it's refreshing according to careful choice of refresh policy, usually the amount of an increase. Data warehouse update is handled by the data warehouse and it will gain component that provides all necessary preprocessing. Depending on how large is data, data warehouses are divided into enterprise-wide data warehouses and division focused data marts. Data warehouses are enterprise wide, while data mart is normally targets on a set of the organization, such as a separate part and are more tightly focused. The data aim is limited to the focused target.

An important issue in data warehousing is the quality control and the behavior of data. Although the data passes through a cleaning function during acquirement, quality and behavior remain important issue for the Database Administrator. The quality control and behavior of data includes naming and domain definitions.

The ultimate goal of a data warehouse system is to store historical information about a company's transactions, and present this information in a way that will allow business executives to make important decisions that is why data warehouse always being popular. However, this data may make up only a small part of the information that a company needs to operate, and its value may even be limited. In some situations, the end user will not have a strong interest in older processing data, and much of this data is made available in basic reports.

When data has been transfer into the organization's large data warehouse or into a smaller data marts designed for any business function, analytics can be performed to convert the primary data into formats that are useful for making decisions, where operational systems, which are targeted to conduct the transactions of the organization, ability to understanding the environment focuses on providing access to large amounts of data to assist the organization in making better business decisions. Hence, this is consist of a suite of software applications that enable business users, management, and executives of organizations to gain a better understanding of the data they have within their organizations, and gives them the information they need to make informed decisions.

CLASSIFICATION OF DATA WAREHOUSE

Although the size, complexity, and magnitude of data warehouses will be tailored to each organization's unique needs, requirements, schedule, budget constraints, available resources, and technology infrastructure, there are basically two types of data warehouses that organizations will build and maintain:

Enterprise Data Warehouse:

Strategic and broad in nature, the enterprise data warehouse is typically a large organization-wide implementation that crosses over every business function and includes data elements from every organizational unit and department. The enterprise data warehouse contains a broad range of related subject areas, and includes every data element that an organization needs to broadly analyze information. Data entities and fields from all organizational units and departments are all consolidated and converted into a single central repository. Business units such as sales, marketing, operations, accounting, and customer support will typically all be involved and will work with each other to centralize the analysis of all of their disparate data. Data will be converted into standard formats to improve methods of analysis across organizational units, enhanced organizational data quality, consistent results, and a broad overview organizational efficiency.

Enterprise data warehouse implementations tend to be quite large as they can contain data volumes involving hundreds of gigabytes. The implementations are usually technology driven, affect multiple organizational units and departments, and can have lengthy development schedules. The business impact of enterprise data warehouses tend to be high as multiple organizational units are affected and a broad perspective of the entire organization can be provided.

Data Mart:

Designed for more tactical and quick-strike purposes, the data mart is usually a repository of data for one business function or organizational unit in order to answer a specific set of business questions within relatively narrow confines. The data mart contains data feeds from a minimal number (usually 1 to 4) of source systems, and is focused a short development schedule and on a rapid implementation. Many times, a data mart will be the reporting and analytical system for a particular department within an organization, such as accounting, accounts receivable, sales, customer support, or marketing. These departments will design the data mart with enough data entities and fields to be able to analyze data for their own unit's needs.

Data mart implementations are typically focused on rapid implementation schedule, and data mart repositories typically have manageable volumes of data ranging from a few megabytes up to a hundred gigabytes. The implementations usually focus on solving an individual business issue and development schedules can be very short. Further, data marts can be sourced from an either an enterprise data warehouse or directly from an operational system. Data residing in data marts can be converted from existing transactional systems or analytical systems

THE TOOLS CAN BE USE IN DATA WAREHOUSE

Business intelligence software includes the tools that change large amount of data into information and provide a mechanism for making knowledgeable decisions.

Business Reports

Primarily focusing on display and organization of data, business reports are richly formatted methods to display data with rich presentation and within a structured layout. Business reports are typically developed by information technology personnel and/or knowledgeable business exports that understand the underlying database structure and can create templates for a repeatable method of retrieving data. These types of tools enable organizations to present data in a formal and logical manner, execute and publish data on a regular schedule, and create capable of coping well with variations in its operating environment with minimal damage listings of data. Further, business reports can be called upon and displayed from a variety of sources, and can be easily integrated into corporate client-server and web applications.

Query and Analysis

Used primarily by business users, query and analysis tools provide an application environment that enable interactive methods to query data, present data in purpose manner, and to find information on an as needed. These tools typically provide use of reasons with powerful query features. With minimal understanding of database structures, business users can rapidly generate queries of data and can analyze key indicators on demand. Most often, query and analysis tools include a versatile semantic layer that converts database conventions into business logic. Controlled and secure access to information is ensured while understanding of business data can be made in a timely manner.

Performance Management

With the use of dashboards, scorecards, and alerts, performance management tools provide a graphical interface and real-time methods to monitor organizational metrics and key performance indicators. These tools are primarily used by managers and executives, provide proactive insight into organizational efficiency, and enable instant visibility to critical data thresholds. The dashboards within performance management environments are intuitive, easily personalized, and can notify decision makers when data volumes approach and exceed accepted ranges and targets. Further, performance management enables team consensus as it typically includes tools for collaboration and workflow. Performance management provides a mechanism for notification of exception situations and optimally controlling the efficiency of the organization.

OLAP (Online Analytical Processing)

Dynamic in nature, OLAP tools provide a high degree of variability into the user interaction model and organize data into information that can be viewed from many perspectives. The main features of OLAP is the enablement of users to perform multi-dimensional processing and to query and view data in a variable number of view points. In the middle of these systems is a concept of an OLAP cube that consists of numeric facts, called measures, which are categorized by text values, called dimensions. Primary features of OLAP tools that differentiate them from other business intelligence tools include drill-up/drill-down analysis, reach-through analysis, data pivoting, and trending. OLAP tools provide advanced insight into past performance of an organization and enable a deep understanding of the reasons behind why prior events have occurred in the manner that they did occur.

Data mining

Using advanced statistical techniques models and algorithms and in a manner of data search capabilities, data mining applications discover patterns and relationships in large volumes of data and predict future results. These tools identify trends within data that go beyond simple analysis, by the past performance to forecast potential outcomes, and identify key attributes of business processes and target opportunities. The discovery orientation of these tools provides answers to questions that have not been asked and demonstrates correlation strength between data elements. Further, the predictive features of these tools enable organizations to exploit useful patterns in massive data volumes.

BENEFITS THAT GIVES USING A DATA WAREHOUSE

Benefits will be realized and values will gained from at successful implementation of a data warehouse and its related business information environment are numerous and considerably valued. These benefits will more than justify the financial investment and resource that organizations will make. The finishing of the construction of a data warehouse, organizations will see immediate and long-term positive progress. In addition to a high return on investment, organizations will benefit from important business decisions, timely access to data, consistency of data, and increased system performance.

Return on Investment (ROI)

ROI will refers to the amount of increased the income or decreased expense an organization will have from any given use of money. Implementations of data warehouses and analytical applications have been found to provide considerably cost savings for organizations and have positive affects towards an organization's financial "bottom line." According to a 2002 International Data Corporation (IDC) study "The Financial Impact of Business Analytics", analytics projects have been achieving a substantial impact on an organization' financial state. The study found that business analytics implementations have generated a median five-year return on investment of 112% with a mean payback of 1.6 years. Of the organizations included in the study, 54% have had a return on investment of 101% or more.

Enhanced Business Decisions

Decisions that affect the strategy and operations of and organizations will be based on profits and will be backed up with evidence and data within the organization. Managers and executive will be freed from making their decisions on their own personal knowledge, intuition, and "gut feelings". Moreover, decision makers will be will informed as they will be able to query actual organizational data, and will retrieved highly organized information to their personal needs.

Timely Access to Data

Organizations will have access to data from many different ways as they need it, will spent little time in the retrieval process, and will questioned and analyze data as they need to scheduled routines, system can set up within the data warehouse environment to access data from the source systems and transform the data into a format that useful for query and analysis. Decision makers can access data from one place and will no longer need to compile data from multiple locations. Further, business users will be able to check data directly with less information technology support. The use of query and analysis tools will shift from the members of the technology department directly to the business users. The waiting time for information technology professionals to develop reports and queries will be timeless, as the business users will generate reports and queries on their own.

Consistency of Data

Data will be retrieved from many source systems and converted into a standard format. Data formats in the various organizational units and departments will be standardized throughout the enterprise, and the inconsistent data within the disparate operational systems will be removed. In addition, all organizational such as sales, marketing, and operations, will use the same data to locate as the source for their individual queries and analysis. Thus each of these individual organizational units will produce results that are available with the other organizational units within the company.

System Performance

Data warehousing environments are built and organized with speed of data retrieval and analysis as its main focus. This structure is to increase for storing large amount of data and being able to query in easy manner. The systems are designed differently from operational systems which focus on processing transactions. In contract, the data warehouse is built for analysis and retrieval of data rather than efficient creation and modification of data. Further, the data warehouse allows for a large system to be taken off the operational environment, and effectively distributes system load across the companies' technology.

INTRODUCTION TO DATA WAREHOUSE ARCHTECTUERS

THE MAIN FIVE ARCHITECTUERS

The data warehousing literature provides discussions and examples of a variety of architectures. For the research, we investigated five:

1. Independent data marts
2. Data mart bus architecture with linked dimensional data marts
3. Hub and spoke
4. Centralized data warehouse (no dependent data marts)
5. Federated.

(Ariyachandra T, Watson J, (2006)

Independent Data Marts

It is usual for organizational units to develop their own data marts. These data marts are independent to one another, and while they may meet the needs for which they were created, they do not provide "a single version of the truth." They typically have inconsistent data definitions and use different dimensions and measures (i.e., non-conformed) that make it difficult to analyze data across the marts. Figure 1 shows the architecture for independent data marts.

Data Mart Bus Architecture with Linked Dimensional Data Marts

A business requirements analysis for a specific business process such as orders, deliveries, customer calls, or billing is the foundation for this architecture. The first mart is built for a single business process using dimensions and measures (i.e., conformed dimensions and conformed facts) that will be used with other marts. Additional marts are developed using these conformed dimensions, which results in logically integrated marts and an enterprise view of the data. Atomic and summarized data are maintained in the marts and are organized in a star schema to provide a dimensional view of the data. This architecture is illustrated in Figure 2.

Hub and Spoke Architecture

An extensive enterprise-level analysis of data requirements provides the basis for this architecture. Attention is also focused on building a scalable and maintainable infrastructure. Using the enterprise view of the data, the architecture is developed in an iterative manner, subject area by subject area. Atomic level data is maintained in the warehouse in 3rd normal form. Dependent data marts are created that source data from the warehouse. The dependent data marts may be developed for departmental, functional area, or special purposes (e.g., data mining) and may have normalized, denormalized, or summarized/atomic dimensional data structures based on user needs. Most users query the dependent data marts. Figure 3 shows this architecture.

Centralized Data Warehouse (No Dependent Data Marts)

This architecture is similar to the hub and spoke architecture except that there are no dependent data marts. The warehouse contains atomic level data, some summarized data, and logical dimensional views of the data. Queries and applications access data from both the relational data and the dimensional views. This architecture is typically a logical rather than a physical implementation of the hub and spoke architecture; see Figure 4.

Federated

This architecture leaves existing decision support structures (e.g., operational systems, data marts, and data warehouses) in place. Based on business requirements, data is accessed from these sources. The data is either logically or physically integrated using shared keys, global metadata, distributed queries, and other methods. This architecture is advocated as a practical solution for firms that have a preexisting, complex decision support environment and do not want to rebuild. This architecture is shown in Figure 5.

Literature Review

Introduction

As Information Technology has continued to grow, so has the extensive electronic information available and therefore the use of internet applications in our daily lives. Khosrowpour (2006) used the example of the evolution of music as one of the earliest and most successful products to undergo the transformation into digitalization. While arguing the importance of adopting an electronic method that allows for advantages in storage, transfer, delivery and accessibility of information, Khosrowpour identified the advantages of implementing a system that used a common format along with a central location for the storage of documents. By integrating these together, a new system was introduced which promised the easy generation and transfer of different documents between different consultants within the organisation, therefore creating an improvement in collaboration between team members within the establishment.

The terms of data warehouse dates back to the late 1980s when IBM researchers Barry Devlin and Paul Murphy invented the concept of "business data warehouse", it was generally, the data warehouse idea was to plan and provide an architectural model for the main structure of data for operational systems that will give more decision support environment. The main issue "warehouse" concept is that trying to solve the various issues associated with its flow mainly, the highest cost associated with them. In an absence of the data warehouse architecture, an incredible amount of redundancy was required to support massive decision support environments. In larger to the organizations it was typically for multiple decisions support environments to operate them independently,

According to William H. Inmon, a data warehouse is a "subject-oriented, integrated, time-varying, non-volatile collection of data in support of the management's decision-making process" (Inmon, 2005, p. 32).

A data warehouse is a centralized repository that stores data from multiple information sources and transforms them into a common, multidimensional data model for efficient querying and analysis. A data warehouse has the ability to address a wide variety of phenomena. A faculty member may ask, "How can I modify my instruction to help my students learn to write more effective essays?" A manager in the Department of Human Resources may ask, "What kind of training or orientation is necessary for new employees?" Each of these questions requires more information about the situation in order to conduct research. This information originates from the data warehouse.

The non-profit firm has been established to help cater to a social cause, where the business objectives include, raising awareness of moral stances that can positively affect the community. Non-profit organisations have come about to make profits not for simply commercial investment purposes, but for a 'greater good'. These firms provide for a community that is unable to provide for itself and caters to society by filling a social gap that exists in society that is not acknowledged by all individuals. Businesses, both commercial and not for profit, have identified new techniques to reach new audiences. This has allowed businesses to identify new markets using technology that allows organisations access into the household of individuals through the internet. As well as reaching new audiences, businesses have found advantages to the organisation itself by completing processes that were previously labour intensive for staff.

For an extensive research on the subject, major literatures on Business Intelligence, DataWarehousing, and DataMining would be reviewed. Literatures on best practices and guidelines used in the BI projects are reviewed. Furthermore, documentation of major IT rollouts on the government sector is also reviewed. Some of the indicative literature review is given below:

(Garner R,2005)This articles looks into the importance of choosing the right consultant for the Business Intelligence project. To truly add value to a BI implementation, the solution providers must also be deeply familiar with domain-specific best practices allowing them to counsel clients on the metrics and scenarios they need to examine. This brings an opportunity for niche-focused solution providers to partner with other systems integrators keen to enter new markets. Bi can be used a lot of different types of businesses. The nature of business has a direct impact on what data need to be collected and processed to gather intelligence.

This article discusses the (Arun S, Satish P,2005) Data warehousing has been cited as the highest-priority post-millennium project of more than half of IT executives. A large number of data warehousing methodologies and tools are available to support the growing market. However, with so many methodologies to choose from, a major concern for many firms is which one to employ in a given data warehousing project. This article reviews and compares several prominent data warehousing methodologies based on a common set of attributes. (Solomon M,2005)

The objective of this article is to provide some guidelines regarding the criticalquestions that must be asked, some risks that should be weighed, and some processes that can be followed to help ensure a successful data warehouse (DW) implementation. Based on extensive field experience, the author provides guidelines to help managers avoid common pitfalls in enterprise-level data warehousing projects. One can anticipate some of the critical aspects of the DW project tasks to help ensure success. Given the size and scope of the enterprise level DW initiative, failure to anticipate these issues will greatly increase the risks of a negative fate.

Conventional wisdom holds that having a management champion with a tightly focused (data mart) design and restrictive tools will lead to success. In this case study, it is observed that the reverse situation can be just as successful. Chenoweth T,Corral K, Demirkan H,(2006) If the users see the potential of the data warehouse to deliver value to the organization, they can be the champions and convince management to adopt the technology. Similarly, because of its simplicity, the data mart approach is frequently recommended as the preferred approach. Providing what the users want and need is more important. If users understand both the technology and the organization's business processes, a single data repository may actually be more satisfying for them.

The article presents information on data mining and the business opportunities that it could generate. Warren T, (2005) Data warehousing techniques can be lead to improve organizations profits and goals. Its popularity is also increasing because the tools are better, more available, cheaper and easier to use. Many data warehouse/business intelligence (DW/BI) projects aren't sure how to get started with data mining. This will presents a business based approach that might be will helpful to successfully add data mining to their DW/BI system. The data mining process should begin with an understanding of major business opportunities. This is a more focused version of the overall DW/BI requirements collecting process. It will Identify and prioritize a list of opportunities that it can have a significant business impact. After the main data mining models are builted, the three tasks in the next major phase involve preparing data, for developing alternative models and comparing their accuracy, and validating the final model.

Data warehouse as a Management Function

Data warehouse are mainly used as a management function in terms of knowledge management. This is used in order to identify and represent as well as generate and distribute knowledge to create awareness and educate the personnel of the organisation. Jackson, et al (2003) defines the role of a database and its contribution to business management within an organisation. They explain the advantages of a database system include its allowance for a business to capture problems and identify solutions to these without any barriers such as geographical barriers.

Data analysis is explained to be an example of a management function that can be made simple. By analysing statistical data or the monetary movement of funds, reports can be created on demand with the help of a database system along with providing a cross sectional analysis of the information. The human resources required by the management are no longer required for the generation or write up of reports. Data warehouse allow for an automatic generation of reports with precise calculations according to the specification of the organisation, also ensuring that there is no room for human error and give data up to five years.

Data warehouses is an analytical software systems that store data converted from transactional, legacy, or external systems, applications, and sources. These data warehouses provide a repository for managers and decision makers to easily and rapidly extract information to answer questions about their organizations.

Data warehouses typically provide the principal source for management information within an organization and provide critical support of decision making and information analysis. Fundamentally data warehouses can be defined as a central repository of data that integrates organizational data collected from various disparate corporate systems. The data warehouse is organized and optimized for retrieval and analysis of data and provides managers and executives a single view of the truth. By converting operational and transactional data into enterprise information, the data warehouse enables optimal decision making.

Further, the data warehouse allows for organizational barriers to be broken, as distributed information is consolidated from various sources. Well-built data warehouses include coordination, architecture, and periodic migration of data from transactional and operational systems into environment optimized for business intelligence, decision support, and informational processing.

In summary, data warehouses store and analyze data converted from software systems within the operational environment, and are the basis of analyzing data within the business intelligence environment.

Findings about Architecture Selection

The factors that affect the selection of a particular architecture, however, depend on what the architecture is. In the case of independent data marts, when there are constraints on resources, the view of the warehouse is limited in scope (e.g., a subunit solution), and the perceived IT skills in-house are low, the independent data mart architecture is likely to be selected. When there is a high need to share and integrate information across organizational units, an urgent need for the data warehouse, low constraints on the availability of resources, and sponsorship at high organizational levels, the bus architecture is an attractive choice. When there is a high need for information integration among organizational units, the warehouse is viewed as being strategic, and the perceived ability of the in-house IT staff is high, the hub and spoke/centralized warehouse is a common choice.

Of particular interest to many people is why some companies select the bus over the hub and spoke/centralized architecture. The bus architecture may be the architecture of choice when there is a high need for information flow between organizational units, the urgency of need for a data warehouse is high, and the viewing these warehouse prior to implementing is more limited to scope.

Findings about the Success of the Architectures

This study suggests why there are agreements and disagreements over which architecture is best. The findings show conclusively that independent data marts are weaker than the alternatives in terms of information quality, system quality, individual impacts, and organizational impacts. This is consistent with the conventional wisdom. Though not as weak, the federated architecture tends to score relatively low on the success metrics. This is not surprising. A federated architecture must "make do" with an existing decision support infrastructure and to some extent has to live with its weaknesses.

Perhaps the most interesting study finding is how similar the bus, hub and spoke, and centralized architectures scored on the information and system quality and the individual and organizational metrics. It helps explain why these competing architectures have survived over time - they are equally successful for their intended purposes! Based on these metrics, no single architecture is dominant.

There are statistically significant differences, however, in terms of development time and cost. The hub and spoke takes the longest time to initially develop and is the most costly to initially develop and maintain. The other architectures tend to be similar in terms of development time and cost. Interestingly, when all of the success metrics are considered, the bus and centralized architectures tend to be the most alike.

The similarity of the success of the bus, hub and spoke, and centralized architectures is perhaps not all that surprising. Much like the development methodologies have converged, so too have the architectures. When the researchers were developing the descriptions of the architectures, the experts would sometimes question a description and point us to an early writing. When we compared that description to more recent writings, it became apparent that the proponents of the various architectures have often evolved their thinking over time, with the architectures becoming more alike. For example, the hub and spoke architecture may include dimensional data marts, which is at the heart of the bus architecture. This evolution is appropriate and good for the industry, but it is also a possible reason that the scores on the success metrics are similar.

Overall, this study found good news for the data warehousing community. The scores on the success metrics for the most common architectures - the bus, hub and spoke, and centralized are uniformly high. Few warehouses are potentially in trouble and companies are experiencing overall success with the architectures they have implemented. These findings bode well for the future of data warehousing.

Financial aid data warehouse application is a web application that provides a unified, consistent data query environment and also to provides an easy-to-use tool for financial aid office to query student financial data with minimum maintenance overhead. Populating data warehouse and querying data warehouse are implemented as two separated modules. Now financial aid office can query data only in this data warehouse without going across different databases and the financial aid office staff are satisfied with intuitive and simple web user interface.

Using the frameworks makes this application easier to maintain than without using framework. The future work list will make the application more robust and more reliable.

One of the interesting research topics arose from this project is how to reduce the technologies used in the web-application. Another interesting topic is to develop some kind of debug tool for the web application since even commercial web server such as WebLogic has no debugging assistant tool for the developer.

Many things I learned from this project. The most important is no matter what software development model used for project, the requirements analysis and design should take time to go really deep and mature before jumping into coding. Without thoroughly gathering and analyzing the requirements and detailed design, no one will be able to develop robust, reliable and error-free software.

CHAPTER 3: RESEARCH METHODOLOGY

INTRODUCTION

Research method gives details about gaining the needed information, and it is target is to design a study. Research methods test the hypotheses of interest, defining possible answer to research question and make available the information needed for decision making. Malhotra, (1996) The research is divided into three distinguish issues. In the first issue is that researcher will clarify the determining of study areas in the data warehouse. The Second issue is that secondary data analysis using published explaining and analysing and determining factors that by which a data warehouse can lead to more target marketing with a best architecture. The third issue is applying practise in the company, research methodology to survey the analysing of organization target in any country. This research approach is going to support defining of the factors and architecture that affect market by using data warehouse.

Data Warehouse Design

The financial aid data warehouse used snowflake schema by combining two star schemas - term fact schema and financial aid schema. One of the most important designs of the financial aid data warehouse is the data warehouse acquisition module. It includes extracting data from all the data sources, cleaning the data and storing the data in the data warehouse. Cleaning data is much more important than extracting data and loading data because the data quality. The functionalities of cleaning data including:

• Convert to common data names and definitions
• Establish default values for missing data
• Compare same data set from different data sources
• Identify and record both incorrect and inconsistent data based on the criteria provided by the customers.
• Email super user the recorded data for further investigation.

Another important design of the financial aid data warehouse is the data warehouse management which includes:

• Update financial office data warehouse
• Audit and reporting financial aid data warehouse usage and status
• Purge data
• Security and priority management

Some of the DW characteristics are given below;

• It is a database that is maintained separately from organization's operational databases.
• It allows for integration of various application systems & it supports information processing by consolidating historical data.
• User interface aimed at decision-makers.
• It contains large amount of data.
• It is updated infrequently but periodically updates are required to keep the warehouse meaningful and dynamic.
• It is subject-oriented.
• It is non-volatile.
• Data is longer-lived. Transaction systems may retain data only until processing is complete, whereas data warehouses may retain data for years.
• 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.

The main roles in a company that will use a DW solution are

• Top executives and decision makers
• Middle/operational managers
• Knowledge workers
• Non-technical business related individuals

The main advantages of using a DW solution are summarized in the list below

• High query performance
• Does not interfere with local processing at sources
• Information copied at warehouse (can modify, summarize, restructure, etc.)
• Potential high Return on Investment
• Competitive advantage
• Increase productivity of corporate decision makers

As discussed above, a DW solution has many advantages and benefits to an organization. Also implementing a DW solution solves some business problems, it may bring some new self-owned problems mentioned below

• Underestimation of resources for data loading
• Hidden problems with source systems
• Required data not captured
• Increased end-user demands
• High maintenance
• Long duration projects
• Complexity of integration
• Data homogenization
• High demand for resources
• Data ownership

Requirements for Data Warehouse Database Management Systems

In the implementation of a DW solution, many technical points must be considered.

While an OLTP database management systems (DBMS) must only consider transaction processing performance (which is basically; a transaction must be completed in the minimum time; without deadlocks; and with support of thousands of transactions per second)

The relational DBMS (RDBMS) suitable for data warehousing has the following requirements

• Load per formance: Data warehouses need incremental loading of data periodically so the load process performance should be like gigabytes of data per hour.
• Load processing: Data conversion, filtering, indexing and reformatting may be necessary during loading data into the data warehouse. This process should be executed as a single unit of work.
• Data quality management: The warehouse must ensure consistency and referential integrity despite various data sources and big data size. The measure of success for a data warehouse is the ability to satisfy business needs.
• Query Per formance: Complex queries must complete in acceptable periods.
• Terabyte scalability: The data warehouse RDBMS should not have any database size limitations and should provide recovery mechanisms.
• Mass user scalability: The data warehouse RDBMS should be able to support hundreds of concurrent users.
• Warehouse administration: Easy-to-use and flexible administrative tools should exists for data warehouse administration.
• Advanced query functionality: The data warehouse RDBMS should supply advanced analytical operations to enable end-users perform advanced calculations and analysis.

RESEARCH QUESTIONS

The research was to answer two questions:

1. Which factor can lead companies to select particular data warehouse for target marketing and
2. How data warehouse successful in various architectures?

RESEARCH APPROACHES

Deductive Research

Deductive research has a directly relationship with positivist and quantitative research. This research should give us analytical perspective of the difficulty. Deductive research includes developing opinion or theory. This idea or theory will be tested by collection of data. When the researcher finds a data which is in researcher opinion, It will support researcher to developing and evaluation of new ideas. Gratton, Jones, (2004). Deductive research mainly offers firstly builds up a theory and proved by collective data. So deductive theory is the best research approaches for determining your theory in the literature review. Saunders et al. (2003). The researcher already selected this method and it will be detailing in the findings chapter.

Inductive Research

Opposite of deductive research, it starts with particular data, which are then helped to create common interpretation idea or theory. Engel, Schutt, (2005). This method has not limitation for developing ideas for the beginning. Saunders et al. (2003)

RESEARCH TECHNIQUES

The number of methods of research techniques can be group under two chapters, one of them is qualitative, other is quantitative research.

Quantitative Data

Hague, (2002) pointed out that quantitative data generally collects through quantitative research which includes investigating three main steps; moreover it uses numbers and indicates graphs and tables.

• Market measurements( Market and sector size, market share, penetration degree,
• Costumer profiles or segmentation
• Attitudinal data

Another theory Dey (1993) said that we use quantitative data during the everyday activities, for example, cooking, shopping, and travelling and for economical occasions. When we ask ourselves how long? How often? How much? How many? Answer should be quantitative data. It takes 40 minutes to cooking and spent A£5 for shopping.

In order to gain quantitative data, the researcher distributed the questioners to some executive managers.

3.5.2 Qualitative Data

Strauss and Corbin, (1998) clarified that quantitative data mainly based on understand cultural values and social behaviour and it ignores statistical and other quantitative methods. Generally qualitative researchers investigate to understand people feelings to doing interviews. In order to gain the qualitative data, the researchers meet a personal interview with department manager of Organizations.

METHODS OF DATA COLLECTION

Primary Data

Davis, (2000) clarified that primary data is that have information from original resource for particular aim. Generally primary data use when the researcher is notable to find data required in secondary research. Primary data involves surveys, questionnaires, observation, experiments and interview etc.

Primary Data from Organisations

Primary sources needed to complete the objective of this researcher aim obtained from questionnaire, interview. The researcher used primary data for clearly understanding Ulker firm international market entry strategy adaptation in the European market.

1. Questionnaire

According to Gillham, (2000) questionnaire is one of the best ways to obtain sufficient information from people. The key point is that you have to be clear, what information you need it? Therefore questionnaire should be focus on research area. Moreover questionnaire divided into two parts one of them is semi-structured questionnaire other is structured questionnaire. Semi-structured is consider about multiple and open questions, structured questionnaire based on simple-specific closed questions. Below information indicates us for what way must follow for good enough questionnaire design.

Questionnaire Design

Specify the information needed,

Specify the type of interview method,

Determine the content of individual questions,

Design the questions to overcome the respondent's inability and unwillingness to answer,

Decide on the question structure,

Determine the question wording.

Arrange the questions in proper order,

Identify the farm and layout,

Reproduce the questionnaire,

Eliminate bugs by presentation. (Malhotra, 1996)

The questionnaire is based on the research with two executive managers of Dialog Company in Srilanka. Moreover, one IT managers of Microsoft are included. The questionnaires were designed by the researcher with part open questions and part close questions. (See the Appendix)

2. Interview

Blaxter, Hughes and Tight, (2006) determined that interview technique includes discussing issue or questionnaire among people. This technique is very beneficial to use for easy communication with internet and telephone. Internet should be easy and cheap way for communication as well as a focusing group which is number of people meet on the internet at the same time. In the face to face interview, researcher must take a note with type recorder or paper-pencil during interview with people.

The researcher arranged a personnel interview with the IT manager in Dialog telecommunication. The interview was hold informally; the interview approximately took 45 minutes, the researcher preceded by briefly explanation of this dissertation. The researcher took the paper-pencil notes during the interview. Location was in the general office of Dialog. In order to gain more accurate data the researcher made up few telephone interviews with three senior staff in the Dialog. They work different departments. One of senior staff works in the international department, others who work marketing department. Telephone interview took about 15 munities of each employee per call, the researcher preceded by a briefly explanation of this dissertation, wore the important points on the paper while the researcher had the interviewing.

Secondary Data

Patzer, (1995) described that secondary data provides to collect data or information from any person, books, media and so on. Secondary data gives researcher good idea for evaluating and reanalyzing your purpose. Nowadays secondary data also uses for market research because it is easy to access resources.

Secondary Data from Dialog:

In this project, the researcher did a case study of Dialog. First of all, researcher collected data from Dialog website and general articles about Dialog. There were distinguish sources of primary and secondary research; most of them collected from academic articles, related website, books, and journal.

These enabled the researcher to understand and evaluate the industry. Moreover, it also enable researcher realize the difficulties of market entry decision for companies and general problem faced.

METHODS OF DATA ANALYSIS

During this dissertation data obtained from questionnaires, interviews. This research covers specific Data warehouse process. The managers of Dialog will use as a source of primary data. These data will be combined by the researcher for the research itself and enable to researcher to understand the industry. In addition, it also enable to researcher to realize the importance of market strategy on the company. The main point here is to establish correct structure and tell to the supervisor how the data is analyzed and designed successfully. Finally, questionnaires and interviews were managed for the purpose of the research. In the next chapter, blending interview and questionnaire information will help to the researcher for the main points of the research. On the other hand, the researcher will look at the crucial spots to enlighten the supervisor for the research contents. Yet, but this research will also be helpful for the following researches.

RESEARCH LIMITATIONS

The researcher has been limited by the available data. The researcher was faced number of problems. The researcher occasionally not replies mail from managers because sometimes they were on the journey for trade agreement. Private and personnel interview delayed before the meeting data. Also journey cost London to Srilanka and Srilanka to London was expensive for researcher budget moreover several times to tried to contact with manager on the phone, telephone call cost also high in London.

Figure 11 shows the percentages of companies that are using the various architectures. The most predominant is the hub and spoke, with 39 percent, followed by the bus architecture with 26 percent. Slightly over 17 percent of the companies have implemented a centralized data warehouse. Only a little over 12 percent of the companies report having independent data marts as their architecture, but this may underestimate the actual percentage in the real world population as a whole since independent data marts were not the focus of the study. The number of responses for independent data marts, however, is sufficiently high to allow meaningful comparisons with the other architectures. Very few companies report having a federated architecture (4%), and because of the small number of respondents, any comparisons with the other architectures must be done carefully because of the small sample size.

A Proposed Architecture Selection Model

Based on this research, an overall selection model can be proposed that describes how companies choose an architecture; see Figure 19. It takes the various selection factors and organizes them into a causal-flow model. The selection factors in the proposed model represent factors that emerged as having a significant influence on architecture selection based on advanced statistical analyses using multinomial logistics regression. In this model, the need for information interdependence between organizational units (i.e., horizontal information interdependence) and the nature of end user tasks (i.e., task routineness) combine to create the information requirements for the data warehouse. The information processing requirements and the source of sponsorship then combine to determine the view of the data warehouse; that is, whether perhaps the warehouse is a point solution for at particular department's needs or is an enabler for supporting strategic business objectives. The perceived ability on IT staff, the availability of the resources and urgency of need to a data warehouse as facilitating conditions for the selection of a particular architecture. And finally, the view of the warehouse and the facilitating conditions influence the architecture selection decision. This proposed model still needs to be tested, but it is consistent with this study's findings.

The Factors that Affect the Selection of an Architecture

No two organizations are the same, and consequently, companies may differ on their architecture selection decisions. There isn't a single architecture that is best for all situations and companies. If it were that simple, there wouldn't be disagreements over architecture selection.

From the literature and the experts, eleven factors were identified that potentially affect the architecture selection decision. Some of the factors relate to rational theory, such as the information processing theory of the firm, while others are based on social/political theories, such as power and politics. Below are the factors that were included in this study.

The survey respondents answered: Please indicate the importance of each of the following factors on the selection of your data warehouse architecture. A seven-point scale was used for the responses, with 1 being not important and 7 being very important. The importance factors were described as:

1. Information interdependence between organizational units: The need to share information among organizational units.
2. Upper management's information needs: Upper management's needs for information from lower organizational levels.
3. Urgency of need for a data warehouse: The extent to which there was an urgent need to build the data warehouse.
4. Nature of end user tasks: The extent to which users' jobs required non-routine data analyses.
5. Constraints on resources: The availability of resources (IT personnel, business unit personnel, and monetary resources) for building the data warehouse.
6. Strategic view of the warehouse prior to implementation: The extent t which implementing a data warehouse was viewed as important to supporting strategic objectives.
7. Compatibility with existing systems: The extent to which the data warehouse architecture was compatible with existing systems.
8. Perceived ability of the in-house IT staff: The perceived ability of the in-house IT staff in terms technical skills, experiences, and confidence in developing a data warehouse.
9. Technical issues: The extent to which technical issues affected the data Warehouse architecture.
10. Expert influence: The influence from sources of data warehouse expertise.

• • Information Interdependence between Organizational Units

There is a high level of information interdependence when the work of one organizational unit is dependent upon information from one or more other organizational units. In this situation, the ability to share consistent, integrated information is important. It is likely that firms with high information interdependence select an enterprise-wide architecture.

• • Upper Management's Information Needs

In order to carry out their job responsibilities, senior management often requires information from lower organizational levels. It may need to monitor progress on meeting company goals, drill down into areas of interest, aggregate lower-level data, and be confident that the company is in compliance with regulations such as the Sarbanes-Oxley Act. To the extent that this capability is important, so too is having an architecture that supports it.

• • Urgency of Need for a Data Warehouse

An organization can have an urgent need for a data warehouse (or a data mart) and the urgency of the business need may dictate a fast implementation. Some architectures are more quickly implemented than others, which can influence the architecture that is selected.

• • Nature of End User Tasks

Some users perform non-routine tasks. Structured queries and reports are insufficient for their needs. They have to analyze data in novel ways. These users require an architecture that provides enterprise-wide data that can be analyzed "on the fly" in creative ways.

• • Constraints on Resources

Some data warehouse architectures require more resources to develop and operate than others. As a result, the availability of IT personnel, business unit personnel, and monetary resources can impact the selection of the architecture.

• • View of the Data Warehouse Prior to Implementation

Organizations differ in their view or plans for the warehouse (or mart). Some may perceive it as part of their strategic plans while other organizations may not. As a result, it may be developed to provide a "point solution" to a particular business unit's need, it may be a decision support infrastructure project to support a range of applications, or it may be a critical enabler to support a company's strategic business objectives. Depending on the view of the warehouse, some architectures are more appropriate than others.

• • Expert Influence

When building a data warehouse, there are many places to turn for help - consultants, the literature, conferences and seminars, internal experts, and end users. To varying degrees, these sources can influence the architecture that is selected. For example, a consultant may recommend an architecture that he or she has successfully implemented in the past.

• • Compatibility with Existing Systems

There are many benefits to implementing IT solutions that are compatible with the existing computing environment. Consequently, the selection of a data warehouse architecture is likely to be impacted by the systems and technologies that are already in place. This may include compatibility with source systems, metadata integration, data access tools, and technology vendors.

• • The Perceived Ability of the In-house IT Staff

The building of a data warehouse can be a daunting task and implementing some data warehouse architectures may be perceived as being more challenging than others, depending on the internal IT staff's technical skills, successful experiences with similar projects, and level of confidence. Consequently, the IT staff may chose an architecture that is compatible with what they think can be successfully built.

• • Source of Sponsorship

The source of sponsorship for a data warehouse may vary from a single department or business unit to the top management within an organization. The sponsor can influence and may control many aspects of the data warehousing initiative, such as monetary resources and the architecture selected. For instance, sponsorship from a business unit may steer an organization to select a data warehouse architecture that provides more control to the business unit, such as a data mart.

• • Technical Issues

A variety of technical considerations can affect the choice of an architecture - the ability to integrate metadata; scalability in terms of the number of users, volume of data, and query performance; the ability to maintain historical data; and the ability to adapt to technical changes, such as in source systems. Depending on the importance of these technical issues, some architectures may be better than others.

• • Individual Impacts

By itself, a data warehouse does not create value. Value creation occurs when users employ the warehouse in their work. Users should be able to quickly and easily access data. They should be able to think about, ask questions, and explore issues in ways that were not previously possible. Overall, the warehouse should improve users' decision-making capabilities.

• • Organizational Impacts

Ultimately, the warehouse should have positive impacts on the organization. It should satisfy the business requirements for which it was built, facilitate the use of BI, support the accomplishment of strategic business objectives, enable improvements in business processes, lead to high, quantifiable ROI, and improve communications and cooperation across organizational units.

• • Development Time

A data warehouse should be developed in a timely manner to meet business needs. The time to rollout the first business process(es) or subject area(s) should be timely and on or ahead of schedule.

• Development Cost

An organization's expenditure for the data warehouse should meet budgetary constraints for the project. The cost at key milestones during the development process, such as the cost to rollout the first business process(es) or subject area(s) and the annual cost to maintain the architecture, should be reasonable and at or below the budgeted amount.

The measures for development time and cost must be interpreted by considering the domain for which the data warehouse is implemented. An implementation in a large domain, such as the entire organization, typically requires more time and monetary resources than a warehouse implemented in a single business unit.

A Comment on Data Warehouse Failures

Some of the early literature on data warehousing mentions the high number of data warehouse failures. Obviously, this is of concern to everyone involved with data warehousing. It is important to recognize, however, that a "failure" can mean different things. It may be a project that is behind schedule, over budget, fails to meet requirements, is not fully used, or "bellies up."

While this study did not address data warehouse failures directly, it does provide interesting insights. For the bus, hub and spoke, and centralized architectures, the percentage of warehouses potentially in trouble is only about 10 percent. Most are doing well and many are a runaway success. The warehouses are less successful, however, when it comes to being on time and on budget for rolling out the first business process or subject area. Depending on the architecture, 30 to 50 percent of the warehouses tend to be either behind schedule or over budget.

These numbers emphasize the importance of understanding how a data warehouse failure is defined. If the definition includes a measure of overall success, the percentage of failures is probably much lower than is often reported. However, if the definition includes on time and on budget, the failure rate is higher.

Conclusion

Limitation that was discovered when implementing a data warehouse

Although the financial aid data warehouse implemented all the customer requirements and it is a good starting point to create a unified campus wide data warehouse, there are still many limitations in the application.

The first is that the updates cannot back flash to the database. It is understandable for the security reason, the original data should not be modified by any application other than its own. But no clear solution on how to correct the mistakes discovered by the application will cause problems in the future. If the CAS database mistakes have not been corrected, the application will continuously report the error and try to update already been updated data set.

The second limitation is SQL queries for the reports. Because SQL query is not object-oriented, scalability will be a problem when the reports change in the future. The current solution is to put all the queries in a file and import all the queries into the application at run time. But due to the complexity of the queries, especially aggregated queries, some complicated queries have to be broken into several small queries and the results are calculated in the code. If those queries changed, the programmer has to go to the code level to make the change.

In the application, security was considered to be highest priority, but still security loopholes are in the application, due to the mechanism of creating new user. The user name and password for the newly created user will be emailed in plain text which can be exposed to any people. Another security issue the application trying to fix is to eliminate the plain text file stored on the local machine, but it didn't get fixed and only gets worse, because every query result will be stored on the financial aid office local machine. It requires the financial aid office to aware this security loophole and cautiously adapt correct procedure to minimize the loophole.

REFLECTIVE SUMMARY:

This chapter summarises that the importance of the findings, analysis, the problems and difficulties that the researcher has been faced with. The rest of this chapter will indicate the assumptions of those issues for the research topic. However, there were also limitations about the research topic generally. But the researcher tried to handle all those problems to finish the research on time.

The main issue on that point is the identifying all problems and clarifying solutions for the research process. Therefore, the main issues should be caught up by the researcher to get ideas for the process of the research.

The plan of the research based on the fully understanding of the research. The critical points should be leading the main ideas for the structure of the research. The clarification of the points about the research topic generated for the future ideas of the research.

The researcher has been tried to indicate main issues for the structure of the research points. These points are mainly related with the research topic. The research topic must be clear to state exact subjects to be successful.

The purpose of the study was explaining the topic elements such as data warehouse implementation, evaluating these methods, choosing these methods, implementing these methods and describing the problematic issues. Choosing the appropriate method is pretty important for the future methods of the research subject.

The main elements of the research may be differed into many sections. These sections could be summarised with these questions: How could it be effect the organization by implementing a data warehouse? How could it be possible to select an appropriate architecture amongst all others? How the firms should decide whether to have a data warehouse or not to have a data warehouse? What are the implementation methods of these? And How the organization decide the best method for them?

To answer the questions above the methods of the research must be accepted very carefully. On the other hand, blending all information and data is the crucial part of the research. The main elements of the research were stated above in all chapters systematically. As regards to the implementing a data warehouse, what factors will effect and will it affect the organization these problems should be revised correctly otherwise it would be disaster for the researcher.

In this thinking through choosing and implementing a data warehouse, the concepts of theories were integrated with the research topic. The research is basically focused on architectures and uses them logically for the operations of Dialog for problem solving and reasoning to assist instruction in more targeting market. The research topic is logically included: problem identification of the research; the difference between all data warehouse architectures and explanation this methods logically. The researcher used definitions, theories, concepts and made comments on the research topic for the best understanding of the researcher. Some key applications that were introduced for learning the basic elements of the architecture methods and implement all these applications for the benefits of the research.

The reflective summary has been used as an assessment tool in research practice, in the fully integrated context directed leading postgraduate program at the University Of East London. This research reports the commonest issues evidenced within the IT management and the well known companies based on journals, articles, books and other tools in the business world of today. The outcomes confirm that these are linked to the stage of progression and experience of the researcher in both academic knowledge and practical experiences.

The researcher has gained many practical experiences from this research. These experiences are all about the understanding of the topic. Generally, the researcher aims at the get all the critical points in the lights of findings, literature review, analysis and all other methods that have been used in this research.

The practical experiences that gained from this research enabled the researcher to reach the peak points of the success for the later career in the sector. Clearly this can be said that, the researcher did not know many things about the process and structure of the research in the very beginning. But in the later on process the researcher has become very experienced person about the research topic.

The practical experiences lightened the researcher in each step during the research process. Furthermore, the researcher should be acquired detailed knowledge for the fundamental issues about the research topic.

The process of the research is mainly established on the well organized bases for the research topic. The process should follow the main goals and objectives for the future steps of the subject which was handled by the researcher.

This research works as a significant guide for interpretation and using reflective, practical and experiential learning - whether it is for personal or professional development, or as a tool for learning. The research takes a fresh look into practical and reflective learning, locating them within an overall practical framework for learning and exploring the relationships between different methods. As well as the theories, concepts, methods, plans and applications the research methods provides practical ideas for applying the models of learning, with tools, activities and copy able resources which can be incorporated directly or indirectly into research practice. This research is essential reading to guide any other researcher, lecturer or supervisor wanting to improve teaching and learning about this research subject.

The research process would show the equality that would make the research topic a much better area to research, but equality has so many affectivities for the research objectives. Like description of conditions, opportunities, choosing of methods, implementation of methods understanding of the research, using the suitable method or methods amongst the data warehouse companies that are still in operation. If the researcher looks at all those things carefully and put together all elements it seems so unachievable. Especially, it is just because the research is so unjust and methods are so unpredictable at times. The researcher would try and take advantage of the methods, and use them unfairly in the advantage of the research. No need to say that the researcher would be careful to examine all those issues carefully. It is like a dream all foggy to describe from the point of managers, but still the researcher was doing that for the benefits of the Dialog. The researcher doubts that if Dialog seeks these methods carefully or not. At this point the researcher made adequate comments about the research process. Of course that Dialog is still in progress. It tries to create more peaceful environment or learn how to solve problems calmly and respectfully. The most important thing here is to determine the best method for the company itself.

The researcher has faced with many problems and difficulties during this research. Mainly these were about time and money. The company head office is located in Sri LAnka, and the researcher had to contact with the managers from Sri Lanka. The researcher tried to reach them both by phone or over the internet. Dialog was chosen for this research just because it is one of the best examples for the research topic. Dialog examined critically from all sides by observations, experimental researches and methodical studies.

The problems and difficulties originated spontaneously for the research during the research preparation. The researcher had to manage them successfully and catch the specific points for the research itself. On the other hand, this problems and difficulties should have been resolved by the researcher. The main problems or difficulties should also have been sort out by the helps of authorised. They contributed very gently for the researcher to this research. Sometimes it was difficult to contact with the authorised people but overall it was not that difficult.

The researcher has become very experienced by those problems and difficulties. The researcher should know how to handle a problem and how to deal with that. It was the main issue of the research topic. This topic was chosen by the researcher to get experience and be an expert at that point for the benefits of the research.

The researcher even did not know how to ask away questions those authorised managers in the very beginning but the researcher has gained a lot of experiences that may be used in the future career by the researcher.

Basically, the systematic work experience would be utility for the research itself. The most important factors of the systematic work experiences would use to sort out all problems and difficulties. However, it would not that easy to step up easily. Consequently the methodical and systematic tools were helpful to point out the integrated elements which are connected with different organizations in the business life of today.

References

• Arun S, Satish P (2005) A Comparison of Data warehousing methodologies, Communications of the ACM, Vol. 48, No. 3
• Ariyachandra T, Watson J, (2006) Which data warehouse is more successful, business intelligent journal
• Blaxter, L., Hughes, C., Tight, M., (2006), How to Research, McGraw-Hill International, USA.
• Baker S. (1994) CORBA Implementation Issues IEEE Colloquium on Distributed Object Management Digest, 1994 7
• Barquin, R (1996) On the first issues of the Journal of Data Warehousing. Journal of Data Warehousing, 2-6.
• Bersoff H, Davis M (1991 ) Impacts of life cycle models on software configuration management Communication ACM
• Berson A, Stephen J. Smith,(1997) Data Warehouse, Data Mining, and OLAP, McGraw-Hill
• Chaudhuri A, Dayal U (1997) An Overview of Data Warehousing and OLAP Technology ACM SIGMOD Record
• Candal-Vicente I (2009) Factors That Affect The Successful implementation Of A Data Warehouse Fourth International Multi-Conference on Computing in the Global Information Technology
• Chowdhary P, Mihaila G, Lei H (2006) Model Driven Data Warehousing for Business Performance Management IBM T.J. Watson Research Center
• Chowdhury S (2004) Best Practices in Data Warehousing to Support Business Initiatives and Needs Proceedings of the 37th Hawaii International Conference on System Sciences
• Courtney R (2002)"Strategic Management for Voluntary Nonprofit Organisations" p. 37 Routledge Publications
• Cohen, Rich (2006). Business Intelligence Strategy: Seven Principles for Enterprise DataWarehouse Design. DM Review. Retrieved Oct 18, 2009, from https://www.dmreview.com/article_sub.cfm?articleId=1045818N
• Chenoweth T,Corral K, Demirkan H,(2006) Seven Key Interventions for Data Warehouse Success Communications of the ACM,Vol.49,No.1 Dwbasics.pdf. [online] Available at: https://res0.esnips.com/doc/ac965b84-e90c-4bc7-b927-0d1df5ad53dc/dwbasics [Accessed 11 June 2009].
• Dialog Telekom [online] Available at:https://www.dialog.lk/business/mobile/[Accessed 13 June 2009]
• Francois J, Herna W, Viktor L (1999) A Data Warehouse for Policy Making: A Case Study Proceedings of the 32nd Hawaii International Conference on System Sciences
• Gupta H, Singh I (2005) Selection of Views to Materialize in a Data Warehouse IEEE transaction of knowledge and data engineering, VOL 17
• Garner R, (2005) Getting Smarter: Process Expertise counts in Business Intelligence work, Issue 1153, p72, 1p ,Item: 17629117
• Grover R, Gibson L,(1999) Identification of Factors Affecting the Implementation of Data Warehousing Proceedings of the 32nd Hawaii International Conference on System Sciences
• Gray, P. and Watson, H. J.(1998). Decision Support in the Data Warehouse. Data Warehousing Institute Series from Prentice Hall.
• Han Q, Gao X (2009)Research of Decision Support System Based on Data Warehouse Techniques Second International Workshop on Knowledge Discovery and Data Mining
• Hwang M, Xu H(2007) The Effect of Implementation Factors on Data Warehousing Success: An Exploratory Study Journal of Information, Information Technology, and Organizations
• Hillard, R, Blecher, P. and O'Donnell, P. (1999). The Implications of Chaos Theory on the Management of a Data Warehouse. International Society for Decision Support Systems
• Inmon W H(2005) Building the Data Warehouse, 4th Edition, Wiley Publishing, Indianapolis
• Jukic N, Jukic B (2009) Bridging the Knowledge Gap between Operational Databases and Data Warehouses Proceedings of the ITI 2009 31st Int. Conf. on Information Technology Interfaces, June 22-25
• Jukic N (2006) Data Modeling Strategies and Alternatives for Data Warehousing Projects, Communications of the ACM
• Kimball R, Ross M (2002) The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling, 2nd Edition, John Wiley and Sons, Inc., Chichester
• Kimball, Ralph (1998). The Data Warehouse Lifecycle Toolkit. John Wiley & Sons, Inc.
• Lockwood M, Kothari A, Worboys G (2006)"Marketing Protected Areas: A Global Guide" p.665 James & James/Earthscan
• Michael Reed, A Definition of Data Warehousing[online] Available at:https://www.intranetjournal.com/features/datawarehousing.html [Accessed 12 June 2009].
• McFadden R (1996) Data Warehouse for EIS: Some Issues and Impacts Proceedings of the 29th Annual Hawaii International Conference on System Sciences
• Nelson k, (2007)partitioning applications, DBMS and Internet Systems, Miller Freeman Inc. https://www.dbmsmag.com/9705d13.html, retrieved 29 Aug 2009
• Paluraj P (2001) Data Warehousing Fundamentals, Wiley Publishing
• Peng Z, Qing L, Ling F, Li X, Liu J (2005) Using Object Deputy Model to Prepare Data for Data Warehousing IEEE transaction of knowledge and data engineering, VOL 17
• Reddy S, Lavanya A, Khanna V,(2006) Research Issues on data warehouse maintenance International Conference on Advanced Computer Control
• Roman V Belavkin,Lecture 03 Data warehousing and OLAP. [online] Available at: https://www.cs.mdx.ac.uk/staffpages/rvb/teaching/03_Data_WH_and_OLAP.pdf [Accessed 10 June 2009]
• Rob and Coronel , Sixth Edition ,Database Systems: Design, Implementation, and Management.
• Rusu L, Rahayu J, Taniar D (2005)A methodology for building XML data warehouses. Int.Jour. of Data Warehousing and Mining
• Sen A, Sinha A, and Ram K (2006) Data Warehousing Process Maturity IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, Aug 2006
• Soloman M, (2005) Ensuring a successful Datawarehouse Initiative,www.ism-journal.com, [Accessed 06 Oct 2009].
• Tong L, Yan C, Ren S, (2008) Analysis on Data Warehouse Technology and Its Development Situation International Symposium on Information Science and Engieering
• Timon C, Wong J (2004) Designing Data Warehouses for Supply Chain Management
• Warren T, (2005) Kimball University: Get Started With Data Mining Now, Intelligent Enterprise; Vol. 8 Issue 10, p43-45, 3p
• Watson H J, Houdeschel G,Rainer R(1997). Building executive information systems and other decision support applications. New York: John Wiley & Sons.
• Winter R, Strauch B (2002) A Method for Demand-driven Information Requirements Analysis in Data Warehousing Projects
• Watson J, Wixom B, Hoffer J, Lehman R,and Reynolds A,(2006) Real time business intelligence : Best Practices at Continental Airlines, Information Systems Management
• Widom, J. (1995) Research problems in data warehousing (1995) Proceedings of the 4th international conference on information and knowledge management (pp. 25-30)

Cite this page