Introduction to data warehouse



This thesis could not have been written without the invaluable support of my supervisor Mr. Nigel Kermode with his review and comments this project would now have been a success. A word of thanks to Ms.

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Ambi Weerakon, who were extremely helpful and gracious with her counsel and information. Thank you for the support and advice during my project. A special acknowledgement to my family and friends, without your support this would not have been possible. Thank you for the constant support and prayers. It would be impossible for the completion of this work without any of the help from the people mentioned and the people around me.


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.



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 ease 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.


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.

  1. 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.
  2. To identify the functionalities required of the system and construct and deliver a system, given the specifications of the organization.
  3. 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 organization.


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.


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


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.



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.

Independent Data Marts

It is usual for organizational units to develop their own new data marts. These data marts are independent in to one another, and while they are meeting the needs for which they created, it’s not going to providing “a single version of the truth.” They typically have variable data definitions and uses different types of dimensions and measures that make it difficult to analyze these data across the marts. Figure shows the architecture for independent data marts.

Data Mart Bus Architecture with Linked Dimensional Data Marts

A organization needs analysis for a specific reason in business process such as common y used orders, deliveries, refunds, customer calls, or billing will be the key factor for this type of architecture. The first mart to be built for a single business process using capacity and measures that will be used with another mart. Additional marts are developing by using these classified of capacity, which will results in logically complete marts and an enterprise viewing the data. This will be summarized data on time and they are maintained in the marts of properly organised in a star schema to provide a different types view of the data.

Hub and Spoke Architecture

According to a large enterprise type analyzing the data requirements provided the basic for this architecture. Mainly it is also focused to build on a measurable and maintainable infrastructure. Using these enterprises viewing the data, the architecture will be developed in a manner, each subject area by each subject area. Data marts dependent are created in the source data from the data warehouse. The dependent data marts would be developed for each departmental, functional area, or special purposes would be have normalized, demoralized, or summarized in to atomic dimensional data structures that based on user needs. Most current users query the dependent data marts.

Centralized Data Warehouse (No Dependent Data Marts)

This centralized data warehouse is little similar to the hub and spoke architecture except that there want be dependent data marts. The warehouse has quality level data, some summarised data, and logical way of viewing the data. To the queries and applications can be accessed from both the relational data mart and the dimensional views. This type of architecture is mainly use as a logical rather than a physical implementation of the hub and spoke architecture.


This architecture going to leave current decision support structures in place. Based on business requirements, data is accessed from these sources. The data is logically or physically integrating the shared keys, global metadata, distributed queries, and other methods. These architecture are defending as a practical solution for firms that have a currently, complex decision support environment and does not want to rebuild.



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 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. 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.



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 design of a data warehouse has been done using snowflake schema by combining two star schemas – term fact schema and financial aid schema. One of the most important designs is financial aid and it is data warehouse is the data warehouse acquisition module. It is including the extracting data from all the data sources, it also be cleaning the data and storing data in the data warehouse. Cleaning data is much more important than extracting data and loading data because the data quality will be dependent on this. 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


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?


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)


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 £5 for shopping. In order to gain quantitative data, the researcher distributed the questioners to some executive managers.

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 Organization.


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.


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)


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.


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.


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.


In this chapter, divided into two parts, First part is that researcher determined that organization need a data warehouse to increase their marketing, another part is that the researcher critically reviewed the findings and analyzed the data collected from reports, newspaper, documents on the internet as a secondary data and the primary data from questionnaires, interviews opinions. Number of informal telephone interviews with the managers of Dialog will be , and a face to face interview with the company’s marketing manager Assistant.


I mainly followed to find answering of questionnaires because of my opinion is firstly I had to consider about to learn how IT professionals would like to implement a data warehouse and compare between company opinion with literature review part.

Findings from Questionnaire

Questionnaire was designed by the researcher which included mixed open and closed questions. It was 7 questions. It was included with fewer questions to make sure that it receiving the best possible answers that the respondent who giving their best thinking about the question it also gives time to discuss and get their opinion why respondent prefers that.

    1. Do you prefer a data warehouse to your company?

Based on the research this question was the main, referring to the respondents 93 precent of them agree prefer having a data warehouse to their organisaton is litueruay the best. Also 7 percent of them disagree to have it, by giving their reasons.

    1. Does the data warehouse make users access data more quickly and easily?

These two questions has got the same percentage of answering and this will show that by implementing a data warehouse organization get there staff to retrieve data and that data will be easy to access and time saving.


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.


Although the financial aim is to implement a data warehouse all the customer requirements and it is a good starting point to create a different atmosphere wide data warehouse, there are still many limitations in the data warehouse application. The first main issue is that the updates cannot be back flash to the database. It can be due to the security reason; the original data should not be modified and if they modify it will occur the errors, and it can be modify by any application other than its own. These cause problems in the future but there is still no clear solution on how to correct these mistakes, discovered by the application. If the database mistakes have not been corrected, then the application will continuously report the error message and it will try to update already been updated data set. The secondly common limitation will be SQL queries for the reports. Since SQL query is not a object-oriented, measure for this will be a problem when the reports get change in the future. For this situation 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 how big the queries are, especially long queries, some complicated queries have to be broken into several small queries and the results will be calculated in the code. If those queries changed, the programmer has to go to the code level to make the change. In the application, the security was considered to be as a highest priority, but still security there was some way of escaping in the application, when due to the mechanism of creating new user. The user name and password for the newly created user name and the password will be emailed in plain text which can be seen by any people. Another security that was occurring will be issue the application trying to fix is to eliminate the plain text file stored on the local machine, since every query result going to be stored on the administrator office local machine. It requires the admin to aware this security.


This suggests why there are agreements and disagreements over whether to have a data warehouse. The findings show conclusively that it will make different desiccation can be made fast information quality, system quality, individual impacts, and organizational impacts will be faced. This is consistent with the conventional to attract. Al though not as weak, the certain matters of the architecture tends relatively low on the success metrics. Perhaps it will be the most interesting study finding is how similar are the bus, hub and spoke, and centralized architectures gong to effect on the information and system quality and the individual and organizations. This helps to explain why these competing architectures have been survived over time and why they are equally successful for their intended purposes Form the significant figures participant agreed that Hub and spoke will be the best architecture. However, in terms of developing 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. It is interest to know that, when all of the success metrics are considered, the bus and centralized architectures tend to be the most alike. It is similar to the success of the bus, hub and spoke, and centralized architectures it 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 participant did sometimes question a description and point it to an early writing. When it was compared that description to more recent writings, it became apparent that the particular the various architectures has been often evolved their thinking over time, with the architectures becoming more alike. For an example, the hub and spoke architecture may include dimensional data marts, which will be the heart of the bus architecture. This reserch is appropriate and good for the any industry, but it is also a possible reason that can be scores on the success metrics are similar. Figure shows the percentages of participant that suggest to use the various architectures. The most participant prefer the hub and spoke, with 39 percent, followed by the bus architecture with 26 percent. Slightly over 17 percent of the participants prefer to implement a centralized data warehouse. Only a little over 12 percent of the participant 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 respondents report having a federated architecture and any comparisons with the other architectures must be done carefully because of the small sample size.

The Factors that Affect the Selection of an Architecture

From the literature review and the experts, eleven factors were identified that might potentially affect the architecture selection decision. Some of these factors are relate to rational theory, like the information processing theory of the organization, while others are based on social/political theories, such as power and politics. These are the factors wich were identified.

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

Upper Management’s Information Needs

In order to carry out the job responsibilities, senior management offtenly requires information from lower levels. This may need to monitor progress of the meeting company goals, divided into areas of interest, mostly lower-level data, and be confident that the company is in compliance with company regulations.

Urgency of Need for a Data Warehouse

An organization may have an urgent need for a particular data warehouse (or a data mart) and the urgency of the business need may decide to a fast implementation. Some architecture are more quickly implemented than others, but all depend on which architecture that going to be selected.

Nature of End User Tasks

Some users are performing non-routine tasks. Structured queries and reports are unreliable for their needs. They may have to analyze data in a 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 of the data warehouse architectures require more resources to develop and operate than others. As a result, the availability of IT management, business unit’s person, and monetary resources can impact the selection of a good architecture.

View of the Data Warehouse Prior to Implementation

Organizations would be different for each needs in their view or plans for the warehouse (or mart). Some may need it as part of their strategic plans while another organization may not. As a result, it may be able to developed to provide a “point solution” to each particular business unit’s need, it may effect to be a decision support for the project to support a range of applications, or it may be a critical and enabling to support a company’s strategic business objectives. Depending on the view of the warehouse, some architectures going to be are more appropriate than others.

Expert Influence

When starting to build a data warehouse, they’re going to many places to turn for help consultants, the literature, conferences and seminars, internal experts, and end users. To varying to their opinion and degrees, these sources going to be a influence when 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 so many benefits to be count when implementing. And also IT solutions that are available ,compatible with the existing computing environment. Consequently, the selection of data warehouse architecture is likely to be impacted by any of these systems and technologies that are already in place. This may going to include compatibility with source systems, metadata integration, data access tools, and technology vendors.

The Perceived Ability of the In-house IT Staff

Building of a new data warehouse can be a big 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.


The early literature on data warehousing it has mentioned the high number of data warehouse failures. Therefore this was a big concern to everyone involved with the data warehouse implementation. It is very important to recognize, however, that a “failure” can mean different things. It can be a project that was behind schedule, or over budget, fails to meet requirements, is not fully used. While this study did not show data warehouse failures directly, it does provide interesting insights when interviewed the participant who disagree to have a data warehouse. For the bus, hub and spoke, and centralized architectures, the percentage of warehouses potentially in trouble is only about 10 percent. Most of the organizations doing well and many are in a runaway success. The warehouses are less successful, because however, when it comes to finish 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 going to be either behind the schedule or over budget. These numbers going to be 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 it is going to be lower than is often reported. However, if the definition includes on time and on budget, the failure rate is higher.

Technical Issues

A variety of technical considerations may affect the choice of which architecture and the ability to understand integrate metadata, how large it is in terms of the number of users, volume of data, and query performance; this will ability to maintain historical data; and the ability to adapt to technical changes, such as in source systems. Depending on the main importance of these technical issues, some architecture may be better than the others.

Individual Impacts

A data warehouse does not going to create any values. Value creation would be occurs when users employ the warehouse in their work. Users would be able to quickly and easily access to the data. They should be able to think about, ask questions, and explore issues in their ways that were not previously can be possible. Overall, the warehouse should be able to improve users’ decision making capabilities.

Organizational Impacts

Ultimately, the warehouse should have a positive impacts on the organization. It should satisfy all the business requirements for which it was built, facilitate the use of recourses, 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 or subject area 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 or subject area 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.


The aim of this research was to determine an appropriate data warehouse can be adopted to any organization, in order to implement an affective knowledge management strategy that will be introduced and in turn help generate reports that will allow the users to monitor the monetary activities of the data. In addition to these features, the database system will allow for the organization to improve their marketing by adopting a data warehouse. Through the research, it was identified, how a data warehouse will impact the distribution of knowledge within organization. The study investigates how Nonprofit organizations must be able to cope with the changing business environment. Through the adoption of strategic techniques a more focused and strengthened sense of unity, direction and purpose within the organization can be developed through the provision of tools that can help understand the external environment and manage the long term development of the business. The strategic movement of the current system to an fast easy system shows the businesses ability to appreciate the changing business environment while ensuring the organization remains a leader in its service. The study evaluates existing businesses as a source to help identify the positive impact of a data warehouse system and the advantages that it would contribute to, by reviewing the use of such a system as a cost reduction tool, with its ability to secure information and process it quickly and accurately. The study further assesses the need for businesses to adopt less commercial tools of storing information and therefore freeing time for the staff to cater to the needs of the community. It is important to keep in mind that technology is a fast paced and ever changing commodity that will bring about new concepts and theories in the future. It is necessary for to implement a system with an idea of the capabilities, functions and advantages that digitalization will bring to the organization.


This chapter summarizes 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.


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Hi, I am Chandi Wijesekara, I am doing my MSc to be awarded by the above mentioned university. I am doing a research on data warehouse factors which can lead to more target marketing with a best architecture for my dissertation. Your contribution towards my research by simply filling in this questionnaire will be greatly appreciated. I assume that you all the information you give me shall be treated with confidentiality. Thank you for your cooperation.

  1. Do you prefer a data warehouse to your company?
    • Yes
    • No
  2. Does the data warehouse make users access data more quickly and easily?
    • Yes
    • No
  3. Is data easy and intuitive to understand and use?
    • Yes
    • No
  4. Do you think the decision making capabilities of users is improved?
    • Yes
    • No
  5. Does it enable improvements in business processes?
    • Yes
    • No
  6. “Data warehouse improves communication and cooperation across organizational units” Do u agree?
    • Yes
    • No
  7. Which architecture will you prefer for a good data warehouse?
    • Independent data marts
    • Data mart bus architecture
    • Hub and spoke
    • Centralized
    • Federated
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