This research study is related to the importance of Data Warehouse Systems. The purpose of this research is to study the uses and roles of the Data Warehouse Systems in the business organizations; whether the data and the tools provided by this system are helpful to the users; and how the systems are being used in the organizations. In order to collect the quantitative data, the survey questionnaire is prepared and qualitative data library research method is used. According to this research, more than 90% users of this system are satisfied with its use. According to this study, the reasons behind the development and growth of Data Warehousing are the changes in the technology, changes in global economy and the innovations. Also, the characteristics, authenticity of the system and the decisions based on this system can be very well analyzed by the use of Data Warehousing.
Currently, if we discuss about the methodologies of decision support system, we find that there are various technologies and methodologies, which help the management to make a fast decision-making process. The term Data Warehousing has its own importance in the present innovative environment. The main rationale behind this research study is to identify the importance of Data Warehousing system in the organization (Clark, Jones & Armstrong, 2007).
According to Marakas, the aim of the Data Warehouse system is to recover, store, manage and organize the data and information that is related to a particular process and its decision (Marakas, 2003). Many changes in the global economy, level of competition, growth in the technology and innovations have led to the development and growth of the Data Warehouse system across the various business institutions. This system uses the data, which is collected from both the internal and the external sources. Due to this system, the decision maker in the organization gets a comprehensive and consistent view of the organization.
Topic of the Research
The research topic of study is the role of Data Warehouse System in the organization. The study also concentrates upon whether it can be implemented in an easy manner. The concept of Data Warehouse (DW) emerges from several sets of information that a user needs. The needs have arisen due to the changes in the management style of different classes of end-users, who now need organization-wide view of the information. These needs are critical to the success of the business. The decision makers are required to react quickly to mission’s critical needs due to rapidly changing, volatile and competitive markets (Wixom & Watson, 2001). They need multi dimensional support on information. The decision makers now need information for strategic decision and not for routine operational decisions. The need for Data Warehouse is felt due to the quality and content requirement of different kinds of end users in an organization. According to Jawadekar, there are three kinds of users of information; the management, knowledge workers and operational staff (Jawadekar, 2002). It helps to know whether a critical change has taken place in the business; is the change showing any pattern? And which factors are affecting the changes and its pattern?
In order to control the change and use it to business advantage, the management requires analytical information support to make strategic decisions and the main purpose of this research is to evaluate the assumption that the Data Warehouse designed to meet these needs would satisfy such requirements.
Importance of the study
The importance of the research lies in the fact that it not only helps us to be familiar with the role and significance of a data warehouse system in decision-making but also illuminates the reasons for the success and failure of these systems. The research will prove beneficial at the time of planning, designing and implementing the data warehouse systems. With the development of better understanding between the relationships of the needs of the users of the systems and the methodologies used to present the data, the institutions will be able to do effective planning and implementation of the data warehouse systems.
Also, this knowledge will guarantee proper application and thus, the success of a data warehouse system in the organization. The research will also assist the organizational leaders and information system professionals in estimating and forecasting qualitative and quantitative returns on their investments in data warehouse systems.
Purpose of the study
In this research, I want to target the business personnel who are the current as well as the prospective users of the data warehouse. The data warehouse has to expand its reach not only to its current users but also aim at penetrating the markets, which are still untouched from its coverage. The selection of target respondents is based on the frequency of using Data Warehouse in the organization.
With the help of this project, I want to explicate the uses and roles of Data Warehouse systems in the organization and its importance to the users. The readers would be able to analyze the characteristics, features, authenticity of the system and the decisions based on this system. The main aim behind this research study is to explain the various parameters that are associated with the Data Warehouse and its use.
Background to the Study:
Data Warehouse system is an essential, critical and foundation tool for management support system. Success and growth of an organization is highly dependent upon its decision-making strategies and processes. The use of the data management system will create a path for the development of the organizational decision. There are various reasons that describe the success of the data warehouse technique in the system of data management. For example, the reasons behind the success of the data warehouse system include the perceived or actual relevance of the data in the system to the user, communication about the availability of the system, the perceived or actual ease of use of the system and the training provided on the system.
The reasons of the success mentioned above can affect the establishment and use of a data warehouse system in an institution like a university or corporate organization. This research is proposed to find out the actual users of the data warehouse systems and the existence of associations between the relevance of the information presented in the system, perceived or actual data available in the data warehouse system, the tools available to retrieve the data and the role of the users.
Summary of Articles
In order to complete the research study with the help of library research methodology, I have used various journal articles for analysis and collection of data and information. In order to understand the background of the literature, I have analyzed the concept, purpose and results of the articles. Summary of some articles is as follows:
Article 1: Data Warehouse Administration and Management by A. Benander, & B. Benander, in 2000.
The article “Data warehouse Administration” was written by Benander & Benander in the year 2000. In this article, he concluded that the data warehouse is basically a storage area of incorporated information picked from any number and variety of data source. The size of the data warehouse is usually very large and a wide range of information can be stored for a long period (Myerson, 2001). According to them, the information related to the business like the products, sales; customers, etc. are collected and incorporated in the data warehouse. For the successful administration and management of a data warehouse, there is a need of skilled and expert persons. DWA involves the overall management of data warehouse (Benander & Benander, 2000).
The task of administration consists of a reliable check, tracking of data, assessment, performance & replication issues and data quality. It can be recommended that the data warehouses should have a backup and recovery plan so that the data can be recovered after a critical situation. According to Anne M. Smith, a data warehouse is a separate structural design used to maintain important historical data that has been removed from operational data storage. A data warehouse provides the information by creating an incorporated folder of important, subject-oriented and historical data for analysis (Smith, 1997). Data warehouse structural design provides the long-term benefits to many companies. These benefits include competitive advantage, improved knowledge, analytical and decision-making gain, etc.
Article 2: Web-Based Data Warehousing by Chen & Frolick in 2000.
The article “Web based data warehousing” was written by Chen & Frolick in the year 2000. According to them in general terms, the Web based data warehousing involves reviewing, analyzing and disseminating the information extracted from a data warehouse through internet and Intranet or Extranet (Chen & Frolick, 2000). The aim to start the web based data warehousing is to gain more and more popularity among organization. The web based data warehousing illustrates the organization to overcome the limitation of data warehousing. The web warehouses are designed to be ascendable because they have many users. The web users could have little technical experience because the intuitiveness should be stressed and inconveniences are minimized in the design (Matt, 1998).
Web technology is a perfect method for arranging high-volume records due to their uncomplicated maintenance techniques. The web enabled data warehouse delivers the accession to decision support (Matt, 1998). If the web based data warehouses are designed properly, it would cost less than the traditional solutions. For the maximum growth and flexibility, the web based data warehousing is designed and structured (Singh & Singh, 1998). According to the Ayers, the web based data warehousing is the new form of data warehousing. To resolve the drawbacks, web based data warehousing can be used. Therefore, a web based data warehouse system should be highly scalable to handle a large number of users randomly. The key to web-based data warehousing is the server based processing.
Article 3: Seven Key Interventions for Data Warehouse Success by Tim Chenoweth, Karen Corral and Haluk Demirkan in 2006.
According to Chenoweth, Corral & Demirkan, predictable knowledge holds that having a management supporter with a strongly focused (data mart) design and restrictive tools will direct to achieve high achievement. It is observed in the case study that the overturn situation can be just equal to the successfulness (Chenoweth, Corral & Demirkan, 2006).
To deliver value to the organization, if the clients see the potential of the data warehouse, they can be the winners and would be able to convince management to adopt the latest techniques and technology. In the same way, the data mart approach is frequently recommended as the preferred approach due to its simplicity (Chenoweth, Corral & Demirkan, 2006).
It is acknowledged that the needs of the users are more important. A single data warehouse may actually be more satisfying for the clients and users, if they understand both the technology as well as the organization’s business processes. Interaction of technology and social circumstances decide the success of data warehouses (Kelly, 1997). The article actually presents a new insight for the execution process as well as interventions that can lead to the achievement of goals. In today’s era, data warehousing represents a significant solution to the increasing challenge. A data warehouse is a single large database that has collected relevant information from several other sources (Kelly, 1997).
Article 4: “Does Data Warehouse End-User Metadata Add Value?” by Neil Foshay, Avinandan Mukherjee and Andrew Taylor in 2007.
This article “Does Data Warehouse End-User Metadata Add Value?” was written by Neil Foshay, Avinandan Mukherjee and Andrew Taylor in 2007 on the basis of their research study. Presently, knowledge workers are not able to use data warehouses efficiently. Levels of adoption can be increased by the use of good quality end-user metadata. Metadata provision develops the end-user services as it helps in searching of digital documents. End-user metadata is also known as business metadata and context is added in it. Business definitions for specific data attributes are provided by end-user metadata (Dyche, 2000).
Metadata is a list of information for primary data and it also identifies access to the warehouse. It makes the meaning clear and gives definitions to various terms in business and data elements. It provides users with a roadmap to the information. End-user and transformational are the two types of metadata. End-user metadata is helpful in business, as it decodes cryptic name code. After decoding, the data can be recognized and used by the end-users as data element is now meaningfully described. The end-user metadata has a positive influence on the attitudes of user and level of utilization of data warehouse. End-user metadata can be standardized and in the business, it defines the layers of metadata. End-user metadata has an application for the users who look for analysis before or after forwarding queries and specific data elements are described by it.
Article 5: Web-Based Data Warehousing: Current Status and Perspective by Binshan Lin and Chang-Tseh Hsieh in 2002.
This article was written by C. Hsiesh in December 2002. In this article, he analyzes the present position of the Web-based Data Warehousing and its future perspectives. The feasibility of data warehousing in businesses has increased due to the quick propagation of personal computers and the networking technologies. It started in the organizations for catering the needs of different departments. There was no coordination among them. The Internet has changed all the information delivery process within and outside the organizations. In fact, the profitability has really been enhanced due to the facilitation of customer-driven e- marketplaces, both B2C and B2B (Binshan, January 2002).
In the later part, the distributed architecture was developed but it failed to provide the common metadata component, which led to the formation of “legamarts”. This customized version supported an incremental approach to the data warehouse. It included the common dimensions, enterprise subject areas, metrics, data sources and business rules. All of these were represented in the GMR form. The majority of the data warehouses were executed by means of multiprocessor hardware. The development of Cuypers system really helped in managing and reconciling the formal heterogeneity. Such techniques will enhance the efficiency and sophistication of the data warehouse and the search tools. It will also make the transforming of multimedia information of the target users more flexible (Binshan, January 2002).
Article 6: A Framework for Developing Enterprise Data Warehouses by Ali H. Murtaza in 1998.
This article was written by Murtaza in the year 1998. According to him, the enterprise data warehousing (EDW) project is generally a huge and time consuming activity. He also elucidated that in many cases; the benefits are not immediately quantifiable and need a leap of faith to rationalize it (Murtaza, 1998). It is suggested that a successful, long-lasting EDW must be elastic, extensible and incorporated. In this article, the author explained that the flexibility is concerned with the impact of new or modified business data requirements on the design of data warehouse. An enterprise data warehousing initiative is one of the most frightening projects that an organization can handle (Murtaza, 1998).
For an executive sponsorship, a typical high level effort is required. The article explains that a clearly defined enterprise strategy with specific goals and objectives must be presented to the stakeholders before entering into any data warehousing journey (Barquin & Ramon, 1997). In general terms, a data warehouse venture differs from technological projects. The goal of the Framework Data Warehousing is to make the activities of design, implementation, and management of data warehousing solutions easier (Barquin & Ramon, 1997). According to the writer, it has been designed to provide an open structural design that can be broadened easily by the customers as well as the third-party businesses by using industry-standard technology. According to Murtaza, the difficulty in quantifying the benefits is one of the problematic issues that are faced by the project manager. Murtaza also suggested that the designed structure of the metadata and data warehouse must be scalable enough to support future changes to meet information needs and analytical requirements (Murtaza, 1998).
Article 7: The Data Mart: A new approach to data warehousing by Pamela Pipe in 1997.
This article “the Data Mart: A new approach to data warehousing” was given by Pamela Pipe in 1997. In a computerized environment, variety of data is available from various systems in the organization but all of the data does not enter into the data warehouse. His article is based on the Data Mart, which means data about data. According to the research of Pipe (1997), W.H. Inmon described that in any information system application, three types of Data Mart are created: design time, control and usage data. While designing a system, a data which describes the use of input data in the application is known as Time Metadata (Immon, 1995). The control Metadata is used by the system to produce Data Warehouse. This data is used to manage and control the process of Data Warehouse creation. For example, data hierarchy is the metadata which is required to control the business data entry into the warehouse. The usage Metadata is required by the users of the data warehouse (Pipe, 1997).
In this article, Pipe summarizes that the managers of the data warehouse use the technical metadata, e.g., the sources of the data, the data cleansing or enhancement rules, and the destination of the data, etc. (Pipe, 1997). The technique of Metadata usage makes business decisions on facts and not on intuition. This is applicable to both tactical and strategic business decisions. Pipe stated that if viewed intelligently and with imaginative mind, it helps the users to sense early warning on some aspects of business, calling for business review and radical change in policy, rule and strategies.
Article 8: Refreshing Data Warehouses with Near Real-Time Updates by Rahman, Nayem in 2007.
In 2007, Rahman, Nayem was given an article “Refreshing Data Warehouses with Near Real-Time Updates”. In tactical and strategic management support system, data warehouse has proved to be the chief component (Rahman, 2007). For the analysis of the historical information, data warehouse was used in conventional decision support systems. Earlier, it was easy to keep the record of the acquired data and maintain activities on the basis of demand. During that time, batch windows were used at night when the users of the business reached home. In order to make strategic business decisions, decision makers require cutting-edge information, therefore; there is a need to refresh the data ware house many times in a day (Rahman, 2007). In near real-time decision support system, the data warehouses are reviewed by using metadata model and it also enhances the frequency of batch cycle runs.
A Real Time Data Warehouse shows an analytical constituent of an enterprise or project level data stream. The data stream plays an important role, as it endures asynchronous, nonstop and multi-point deliverance of data. The movement of data is from the source of origin to all uses, which do not need any kind of staging. Once the writing of original data is completed, the movement of data takes place. The delay in time is accounted to the transport latency to send off the case of data being rendered. Respondents who perform near real-time data warehousing has increased from 2 percent to 24 percent in a period of 18 months. The complete application is reserved coherent by means of these real-time updates (Ramamritham, et al, 1996).
Article 9: A Comparison of Data Warehousing Methodologies by Arun Sen and Atish P. Sinha in 2005.
This article was written by Sen and Sinha in the year 2005. The article explains that the data warehousing has been quoted as the post-millennium project with highest-priority with more than half of IT executive’s (Sen & Sinha, 2005). To support the growing market, a large number of data warehousing methodologies and apparatuses are available. A major concern for many firms is to choose a tool from various methodologies which can be easily employed in a given data warehousing project (Sen & Sinha, 2005). This article evaluates and assesses various important data warehousing methodologies based on a common set of attributes.
This article mainly focuses on the designing of the data warehousing methodologies that are commonly proposed (Inmon, 2002). It is assumed that the business clients have diverse goals and expectations. The data warehousing is frequently successful than a pool of data warehousing expertise in-house. The various authors explain that a common set of attributes determines the methodology, which should be used in a particular data warehousing project (Inmon, 2002). Author Inmon actually had the opinion of generating a data warehouse on a subject-by-subject area basis. According to the various authors, building a data warehouse is very difficult because it is quite a new discipline and does not offer well-established approaches and methods for the process of development (Hackney, 1997).
Article 10: The Benefits of Data Warehousing: Why Some Organizations Realize Exceptional Payoffs by Hugh J. Watson, Dale L. Goodhue and Barbara H. Wixom in 2002.
This article was written by H. J. Watson in the year 2002. It describes the various benefits of Data Warehousing. In the field of information system (IS), Data Warehousing is one of the key developments. Due to its varied benefits, different organizations are generating different returns, depending on its impact on the organization. The common model provided by this technique makes the analysis and reporting of data from different sources easier. By the reduced inconsistencies of analysis and reporting, the companies are able to control the cost and thus resulting into the generation of high profits. The speed of the operational system of the company is also unaffected during the retrieval of data. Thus, it saves a large part of the time of the employees and the cost of the company (Immon, 1995).
Though Data Warehousing involves high cost; yet, when used effectively and efficiently, it proves to be very economic. DW enables an easy approach to required information and enhances the efficiency of various decision-making processes. It assists the applications of DSS (decision support system) like that of exception reports, trend reports and reports which demonstrate the actual performance against the goals. With the help of these reports, the management and the employees of the organization are easily able to grab the opportunities of the market and generate high returns (Watson, 2000). The customer relationship management (CRM) systems are also handled easily through it. The analysis in the article also shows that the benefits of each company can be very well distinguished on the basis of the framework of its organization.
Article 11: An Empirical Investigation of the Factors Affecting Data Warehousing Success by H. Wixom and Hugh J. Watson in 2001.
This article was written by Wixom and Watson in the year 2001. The data warehouse from early 1990s acts as the base for the highly developed decision support applications. The literature supporting IT implementation proposes that variety determines the success of data warehousing. There is a small empirical investigation about the factors affecting data warehousing success. Distinct characteristics are shown by the Data warehousing that may affect the significance of factors that are related to it (Wixom & Watson, 2001).
Through the analysis of Partial Least Squares, the results show data quality and system quality factors. Further, it was observed that support from the management and resources help in dealing with the organizational issues arising on implementation of warehouse. Other factors like user participation, resources and members of extremely-skilled project team determine the finishing of the warehouse project on time, in allocated budget and with the correct functionality. The technical issues arising from low quality and deprived development technology must be tried to overcome (Wixom and Watson, 2001).
Apart from this, the other factors according to Watson & Haley include use of methodology, modeling, easy understandable goals and the management of expectations. The factors like well defined business needs, support from top management and user participation help in determining the operational side of the project (Watson & Haley, 1997).
Research Methods and Design
Research is a common parlance, which refers to a search for knowledge. One could also define a research as a scientific and systematic search for pertinent information on a specific topic. In fact, the research could be defined as an art of scientific investigation. This research study is based on pre-planned steps. Appropriate result of any study is based on its quantitative and qualitative data. In order to collect the data, I will use a research methodology named Analysis of Library Data along with a survey method to analyze the human thinking about this system. Library research method or analysis of historical database is the methodology in which I will analyze the articles and studies done by other researchers in the past. This methodology is useful for my analysis because the review of previous study will provide me with a quantitative, reliable and valid data (Kothari, 2005). In order to collect the qualitative data and identify the views of users of the system, I will conduct a survey analysis. In this, a survey questionnaire will be provided to the recipients to fill out. This would help to evaluate the satisfaction level of the users about the particular system and analyze the findings of the data, which we will get from the analysis of various articles.
Rationale of the Methodology
The method of collecting data by the questionnaires is most extensively employed in various economic and business surveys. The rationale behind using this methodology is as follows:
Data Collection Method
By arranging the conditions for collection and analysis of data in a manner for the purpose of combining relevance to the research purpose with economy in procedure, the research planning and designing is done. The research problem in this assignment is related to the Data Warehousing and its implementation in the business organization. Currently, the issue regarding the Data Warehousing is an integral issue. Various public universities and researchers have accomplished their researches on this issue and developed various applications in this direction. In order to conduct the research analysis and collect the secondary data about this particular problem, I will collect the primary data by using the previous applications and researches on this particular problem (last 10 years). I will use the library research methodology to complete this research study.
In order to collect the primary data regarding the research problem, it will be beneficial to use the survey method. A survey questionnaire will be prepared that asks questions from both the current users of the Data Warehousing and personnel that are potential users (but may either not be aware of the system or don’t find it beneficial). A series of questions will be prepared to know about the roles, behavior and the advantages of this system. The questionnaire will include the questions like the benefits of the system, the tools used to access the systems, etc. (Kothari, 2005).
Through the analysis of questionnaire, the satisfaction level of user can be measured by the data. Both of the research methodologies, i.e. analysis of the previous library researches and survey through questionnaire method would be beneficial for the research to clarify the problem statement of the study and to identify the various solutions to improve the applications of the Data Warehousing.
In order to collect the primary data through the survey questionnaire method, a study would be conducted by taking a sample of 50 people. The age of participants is from 21 to 50.
Number of people
The primary data of this research study would completely be based on the result of this survey methodology. The questionnaire is intended to determine who is using the data warehouse systems in the organization, how they are being used, whether or not the data needs are being met with this system and how the users learned of the availability of this system. Satisfaction levels of users will be measured regarding the data in the warehouse, the tools used to access the warehouse and the communication, training and support provided for the warehouse.
Limitations and Challenges with the Research Methodologies
Every research method has some limitations and the researcher has to face some challenges. Some limitations, which I have already faced, are as follows:
Library Research Method:
The questionnaire research methodology and the data collection plan used by the researcher also have some limitation, which are as follows:
Report on Data Collection
Difference between data ware house and transactional database
Data ware house plays a vital role in the decision-making where as Transactional databases help the people to carry out different activities. For example, a transactional database can easily show the number of available seats on an airline flight; this can help the travel agent to book a fresh reservation and the data warehouse can show the empty seats in a historical pattern.
Another difference can be made on the basis of the goal. Goal of data warehouse is to stock data designed with the help of transactional data subset. On the other hand, the goal of the transactional database is related to the storage of appropriate data linked to the business unit.
A data warehouse is considered as integrated, subject-related, time-variant and nonvolatile. It lays emphasis on the concept like sales instead of the process like issuing of invoices. It consists of all pertinent information based on concept gathered from the system of multiple processing. For the speed of the operation, online transaction database has been designed in the processing business transactions. It also helps to strike a balance between the insertion of record and generation of report (Jawadekar, 2005).
For day to day operations, the transactional database has been defined such as insert, delete and update system or operation. In order to avoid the redundancy, these databases are normalized highly. On the other hand, for Analytical purpose, the Data warehouse databases have been designed. The core components that describe an entity in the design of a relational database are recognized in the normalized table in the form of columns. The Other components of the same entity are shifted to the different table called as lookup tables (components having repeating values). This arrangement plays an important role as it permits the quicker processing of data while minimizing the requirements of storage (Jawadekar, 2005).
Relational Database Management System and Data Warehouse
In order to gain a competitive strength, Management Information System is required in every organization for the intention of handling online functions, mission control applications, and to exercise the functional and management control. In this competitive and technological environment, it is not sufficient to simply computerize the back office operations. It demands a tool to effectively handle both the transactions processing and decision processing requirement. A RDBMS is a software, which fulfills all the requirements of an organization in relation to the transaction processing, decision-making, providing quick solutions, managing the data base and information, etc. (Jawadekar, 2005).
A data warehouse is a distinct part of RDBMS installation. This part contains the copies of data and information from on-line system that makes it easy for the management to take decisions. With the help of the RDBMS, the management can fulfill the requirements of both the transaction process and decision support.
Data Warehouse is a special database containing large sockets of an enterprise data related to Meta data processed to a ready to use stage for decision makers for operational and analytical business analysis. It helps to follow a fast decision-making process to meet the needs of functional information system and critical needs of decision makers (Jawadekar, 2005).
Types of Data Warehouse
To assist responding to the users’ analytical questions, each of the three types of data management systems is used (Benander, et al., 2000). All of these three systems vary from the operational database. The primary function of the operational database is to support the transactions of daily business operations (Chen & Frolick, 2000). To get a more flexible and faster access to several of the key information of an organization, data warehouse is used (Chen & Frolick, 2000). In the later part of the discussion, we will discuss the different types of data access tools, which may be offered to the users.
Operational Data Store (ODS): Basically, the operational data stores are an updated version of the files containing customer information (Benander, et al., 2000). Generally, data of 12 to 18 months is stored in the operational data stores so as to restrict the trend analysis of the data held there. It is particularly used to respond to the questions on the current operations. In the organization, the users at all levels can use an operational database (Benander, et al., 2000).
To summarize the current operational data at a thorough level, the use of operational data stores (ODS) is made. Normally, only the tabulation of current data is done by the ODS and no historical information is stored by the ODS. It has been seen in many cases that for performing and allowing the data queries, the legacy systems of the institution are not set up efficiently, easily and effectively. With the help of operational data store, the users can inquire about the current data for the purpose of decision-making. The decisions, which are made by making the use of the operational data store, are the day-to-day and tactical decisions. Pipe states that the institutions, which organize an ODS, could develop that data warehouse into an enterprise data warehouse at some later time period (Pipe, 1997).
Data Mart (DM): The database repositories, which are designed for only one subject or functional area, like that of a human resource or finance, are known as the Data Marts. The scope of data mart is larger than the scope of operational data stores in which the information related to many years is stored, but they are not larger than the data warehouses, which have multiple functional areas. Typically, the data mart is used for providing decision support on specific subject area or to some specific department (Benander, et al., 2000). It is possible that in some cases, the data marts are first implemented in the institutions and then only the data will be put together to form an enterprise data warehouse. While in other cases, the reverse will be done. First the enterprise data warehouse will be established and then with the help of this data warehouse, the data mart will be implemented (Benander, et al., 2000). The execution of a data mart decreases the development time and the cost, when compared to a full enterprise data warehouse (Chen & Frolick, 2000). Adding up, the complexity of an enterprise data warehouse can be reduced with the help of the data mart by rendering a part of the data, which has been tailored to meet the exact user requirements (Chenoweth, et al., 2006).
Coskun and Pohlen, emphasized and brought into light some of the disadvantages of an enterprise data warehouse in comparison to the data mart. They said that an enterprise data warehouse required long time for its development and also involved high cost (Coskun & Pohlen, 2002). While the data marts are quicker to implement and less costly, they also have the ability to carry specific data analyses (Coskun & Pohlen, 2002).
The data about a particular functional area or unit is contained in each data mart. As the data across the functional units and areas are not integrated, the data analysis to the functional areas cannot be done. Taking an example of the university setting, to study the cost per credit hour, there will be a requirement of integrating the data of students, financial and human resources. Credit hours will be known by looking at the student data. Data related to the departments of the faculty offering those credit hours will be provided by the human resource data; and the expenditures on those faculties and some other non-personal expenditures related with providing those credit hours is known through the financial data.
The data in the data mart may be at a detailed level or a summary and comprises of data of several months and years. Though its scope is limited to particular unit or functional area, yet it allows the trend analysis.
Enterprise Data Warehouse: The span and quantity of information incorporated in a data warehouse helps in distinguishing it from a data mart and an operational data store (ODS) (Benander, et al., 2000). It includes the data, which contains information of multiple years and crosses the functional areas, like human resources, students and finance (Benander, et al., 2000). In many cases, the end users will not be able to access the enterprise data warehouse because of the presence of many complexities (Benander, et al., 2000).
In order to help the corporations in assembling huge quantity of data, the data warehouses were developed. Due to alterations in the market and its effects on the business, together with the changing purchasing behaviors, consumption patterns, market saturation, growing competition, difficulty in differentiating the product line and emerging markets, the need for quick generation of information is growing (Coskun & Pohlen, 2002). The need for analytical databases along with the transactional databases has been created because of all the above factors (Coskun & Pohlen, 2002). The above mentioned data must be transformed into managerial information so that it can be easily retrieved and analyzed. In order to support decision-making, the data warehouse acts as a vehicle for summarizing and transforming the information (Coskun & Pohlen, 2002). The data warehouses, which contain data from the legacy systems, do not support the easy analysis and summarization of data. Thus, the data warehouse puts together all the data for answering the queries of multiple end-users (Coskin & Pohlen, 2002). Data across the institutions are integrated by the data warehouse. In the example of the university settings, the data would include information of the students, human resources, fiancé and alumni. The figure also illustrates it well.
This data may also contain important information related to the strategic decision-making and the space being used by the university. All of these functional areas could be interconnected. To obtain additional details, the user may use drill-down technology. It includes both the historical and current data so that the institutions can have a look at the trends over the time period. The data ware house allows the accessibility of multi-year data.
Data Warehouse Access tools
To measure the perceived usefulness of information technology systems, Fred Davis did a study of two scales in the year 1989. It was supposed that both these factors had an effect on the user’s acceptance of the user technology (Adams, Nelson and Todd, 1992). In 1992, the study report was repeated by Adams, Nelson, and Todd to study the mental attitude of users toward messaging technology through both voice and electronic mail. It was also used to analyze the attitude of customers toward word spreadsheets, graphics and processing. The studies carried by them showed that these scales were effectual in evaluating the significance of easiness to use. These factors are important in determining the use of system (Adams, et al., 1992). The studies also distinguish that there may be some execution factors that determine the use of technology; for example training, support, user involvement, and user expectations. These factors may determine the assessment of the value of the technology.
The proposal conducted through the research study will look at the users’ perceptions of the usefulness and ease of the use of the tools, which are available for accessing the data mart, data warehouse and operational data stores at the place of university. The scales designed by Nelson, et al., will be applied in the survey that is conducted.
It was indicated by Adam, et al. that analyzing the relationship between ease of use, usefulness and usage of information technology is tricky. This was because the use of the systems was frequently required and was based on the job responsibilities of individuals (Adams, et al., 1992). In this study, the use of the data management systems included is not generally required. The investigation will point out whether the individual has been required to use these systems or not. It may be the only alternative for getting the information needed to perform one’s job responsibilities, if the system is not required. As a result, this factor may require further study.
The information access layer is defined as the component of the data warehouse that allows the user to access the data (Chen & Frolick, 2000). To make the data available to the end-user in an easy fashion is the main goal of the information access layer. Most of the information access layers use graphic user interface (GUI) applications to run on the desktop computer (Chen & Frolick, 2000). The factors that show the impact on the success of a data warehouse are flexibility and scope of the tools offered to users (Chenoweth, Corral & Demirkan, 2006). It was recognized by Chenoweth, et al. that it is not easy to provide simple tools as well as ad hoc queries and reports (Chenoweth, et al., 2006).
An investigation done by Murtaza indicates that the business client should be the one who drives the implementation process. His investigation further indicates that the organization will not receive the full benefit of the data warehouse until and unless the business user can easily plot a route and fully understand the detailed, summarized and historic data in the warehouse (Murtaza, 1998). The study given by Murtaza indicates that selection of a tool for evaluating the data management system should be reliable and constant with the sophistication of the user (Murtaza, 1998). Therefore, the study concludes that it seems proper for the data management system to have more than one access tool. The three tools that can be used to access data warehouse are:
Standard Reports: The extremely protective option for accessing the data management system diminishes the complexity and vagueness (Chenoweth, et al., 2006). The first option for getting the information of data warehouse is to set the users and to develop and deploy a set of standard reports. Generally, this option is used for making executive decision. An executive report is provided with an easy access to a dashboard of metrics for reviewing on a daily, weekly or monthly basis. This dashboard might include enrollment management metrics in a university setting; for example, graduation rates over time, external data on department rankings, and tuition revenue trends. This option is generally used to offer information to a group of less-advanced customers. They have the need for the same set of standard information each day, week or month.
Standard reports with parameter driven access: The second option is to provide a set of standard queries to the users. This gives the customers the power to select detailed parameters and also to select summary of detailed level data. The customers may look at enrollment trends for one particular department or division or may want to look at the numbers of faculties for one particular year provided by the department. Developing some standard reports and allowing the users to modify those reports by selecting only certain parameters can provide the customers with access to significant amounts of data without writing personalized reports.
Ad hoc access: Ad hoc access query tool allows users to select the tables and data from the fields they wish to select. To meet the customer’s data need, users may simply get a flat file of data that can be allowed to analyze the own data in numerous ways. For instance, customers could obtain a flat file of overheads data. The customer can produce their own reports by furnishing it, by other sources, by department, by functional expenditure or by combining them all. The ad hoc query tool might also permit the customers to set their own preferences of reporting.
It has also been beneficial to provide less restrictive access to some data management system users (Chenoweth, et al., 2006). It was founded by Clark that there will be an impact of the use and success of the system (Clark, et al., 2007). According to Clark, near about 20% users are the information producers and near about 80 % of users are information consumers (Clark, et al., 2007). The users who submit ad hoc queries that are used to create reports and analyses are termed as the information producers whereas the information consumers use the reports and information that is produced by the information producers. More powerful tools for accessing the data are required by the information producers.
Analysis and Discussion
Success Factor of Data Warehouse
Earlier, the successful implementation process of an information system project was based on two factors management support and user’s involvement. A study conducted by Chenoweth, et al. also indicated that the comprehended availability of expert support, which could be a team of data warehouse or user experts or super user of data warehouse system, results a successful implementation of data warehouse system (Chenoweth, et al., 2006). In order to understand the purpose of data warehouse system, this support is very essential so that the user could use this system in an easy manner (Chenoweth, et al., 2006). The super users can utilize this system as a method for distributing their knowledge and awareness throughout the whole organization.
According to the literature of Watson, it is very difficult to quantify the various benefits of a data warehouse because some of the benefits are intangible and the research has not been consistent in agreeing on what benefits should be measured and how to quantify them (Watson, et al., 2002). Some of the benefits related to the sample include: data accuracy, ease of use, customer satisfaction and useful information. Apart from this, it also includes decision confidence, system usage, time to make a decision and reliable information. Few of these measures will be incorporated in this study’s questionnaire.
Management and Executive Support: A factor that has been established long before is Management and executive support, which has led to the proper use of a management support system and success (Clark, et al., 2007). The executives play a vital role in laying down the resources so as to construct and support the systems. Apart from this, they will provide the drift to the proper use of the systems (Clark, et al., 2007). In addition to this, the management also understands that they should construct systems so as to collect and provide access to huge quantities of data. This process will help in the data mining and also assist in the decision making in the organization (Clark, et al., 2007).
User Involvement: The main reason behind the development of the system should be the needs of the users. However, in those times, Alter found that participation of the user was not widespread in the projects related to the system development. His study reveals the cause of the low involvement of the user, generally stanching from the fact that systems might be sold and forced upon the users. Therefore, in each situation, the involvement level of user will show natural variation in every situation. Alter also describes that a relationship exists between the success rates implementation and user involvement.
In 2007, a study carried by Clark et al. pointed the significance of user involvement in the growth of an analytic aid (decision). Because of the following reasons, involvement of users was regarded important (1) It helped in the development of the dedication and better understanding, which was required for successfully implementing the decision support tools and (2) It helped in tailoring the system as per the needs and requirement of the users and also depending on their role inside the organization (Clark, et al., 2007).
The involvement of the user permits the system designer in better understanding of the user’s needs and broader goals of the organization. It also provides the designer with an opportunity to know the tasks required in achieving the desired goals. The understanding so developed is helpful in developing the ability of the designer so as to implement and built an efficient system. The fact that the user involvement is important in the development of the information system is not favored by all studies.
In the study of Barki & Hartwick, it was found that participation and involvement of users should be measured in a separate manners. According to them, users’ participation could be defined as the activities of users during the development of system whereas user involvement could be defined as a subjective psychological state that a user feels about the importance and relevance of the system (Barki & Hartwick, 1994).
The participation of the user can be direct participation, when he is participating directly in the process and it can be an indirect participation when a representative of the user participates on his behalf (Barki & Hartwick, 1994). The participation is formal when it takes place through meetings, groups and some other mechanism while an informal discussion is done through informal relationships and discussions (Barki & Hartwick, 1994). The participation can also vary in scope during the various stages of the process; that is, the participation may be in identifying the problem, evaluating the problem, formulating a solution, or implementing the solution (Barki & Hartwick, 1992).
Data Warehouse Implementation
Though sufficient tools are available for each process involved in the design and development of warehouse, the subject is complex and requires participation of senior management and IS personnel. In practice, Data Warehouse conceptual model could be for the enterprise as a whole; however, it needs to be developed in stages to ensure its success and business benefits. So the implementation of full data warehouse would be in stages, starting with warehouse initiation project.
This is done through segmenting the Data Warehouse in smaller components. This means defining high level enterprises model and enterprise data. The subset should have its own clear source options and business data, which it would create. This data must have business information value for strategic application. The subset could be visualized as critical management application, affecting key areas and so on. The steps involved in stage implementation are following:
To get a better start on the project; it is better to initiate a preparatory project to get everybody in the organization to understand the why and how of Data Warehouse. Following activities are carried out for this purpose:
The critical success factors for successful conclusion of Data Warehouse project are the same as that for any information system project. It requires long term commitment and involvement of key business managers whose business data needs will be served through Data Warehouse. A programmatic and stage implementation plan ensuring the success and assuring the benefits is necessary. The sound understanding of existing information system supplying operational data for data warehouse is necessary. The architects and developers must have mastery on related technology and tools for their effective application in building the Data Warehouse. Training the end-users for using data warehouse for fulfilling their business information needs and expectations is absolutely necessary. The point is that Data Warehouse makes them self reliant to meet their needs. They are not dependent any more on IS department to process their information needs. This is a cultural change. Hence, training in tools like report writers, spread sheets and SQL is necessary to exploit the benefits of Data Warehouse.
In this research, I have analyzed different articles and found that Data Warehouse system is an important tool for the organization at the time of decision-making process. I have come to know the importance of Data Warehousing in various business ventures and institutions. The reasons behind the development and growth of Data Warehousing are the changes in the technology, changes in global economy and the innovations. If the previous task is completed successfully, the satisfaction level of the users can also be evaluated.
There are two basic reasons for users not using the data warehouse systems; either the users were simply unaware of the existence of the warehouse or the warehouse was not perceived as helpful in completing their job responsibilities. According to my research analysis, there are more than 90% recipients, who are satisfied with the Data Warehouse systems and its techniques. They really feel that this system is very helpful to analyze information to take decisions within minimum time period. Different tools of data warehouse discussed in the report have the ability to run standard reports, select parameters for those standard reports, and provide data through ad hoc queries.
In order to conduct a research in the future, it is essential for the researcher to analyze each and every aspect of the problem again because of the changes in the environment, technology, procedure, etc. So, it will be easy for him to conduct an appropriate research and giving a result which would be based on the accuracy and authenticity.
Adams, D., Nelson, R., & Todd, P. (1992, June). Perceived Usefulness, Ease of Use, and Usage of Information Technology: A Replication. Journal of Management Information System Quarterly, 16(2): 227-247.
Barki, H., & Hartwick, J. (1994, March). Measuring user participation, user involvement, and user attitude. Journal of Management Information System Quarterly, 18(1): 59.
Benander, A. & Benander, B. (2000). Data Warehouse Administration and Management, Information Systems Management, 17(1):71-80.
Chen, L., & Frolick, M. (2000). Web-Based Data Warehousing. Information Systems Management, 17(2): 80.
Chenoweth, T. Corral, K., & Demirkan, H. (2006, January). Seven Key Interventions for Data Warehouse Success, Communications of the ACM 49(1): 114-119.
Clark, J., Jones, M., & Armstrong, C. (2007, September). The Dynamic Structure of Management Support Systems: Theory Development, Research Focus, and Direction. MIS Quarterly, 31(3): 579-615
Coskun, S., Pohlen, T., & Bozovic, N. (2002, Winter). A Review of Data Mining Techniques as They Apply to Marketing: Generating Strategic Information to Develop Market Segments. Marketing Review 3(2): 211 – 228.
Foshay, N., Mukherjee, A., & Taylor, A. (2007, November). Does Data Warehouse End-User Metadata Add Value? Communications of the ACM, 50(11): 70-77.
Hsieh, C., & Lin, B. (2002, December). Web-Based Data Warehousing: Current Status and Perspective. Journal of Computer Information Systems, 43(2): 1.
Jawadekar, W. S. (2005). Management Information System (3rd Revised Edition). New Delhi: Tata McGraw Hill Publishers.
Kothari, C.R. (2005). Research Methodology (3rd Edition). Delhi: New Age International (P) Ltd., Publishers.
Marakas, G. M. (2003). Decision Support Systems (2nd ed.). Upper Saddle River, NJ: Prentice Hall.
Murtaza, A. (1998). A Framework for Developing Enterprise Data Warehouses. Information Systems Management, 15(4): 21 – 27.
Pipe, P., (1997, October). The Data Mart: A New Approach to Data Warehousing, International Review of Law, Computers & Technology, 11(2): 251-262.
Rahman, N. (2007, Spring). Refreshing Data Warehouses with Near Real-Time Updates. Journal of Computer Information Systems 47(3): 71 – 80.
Sen, A., & Sinha, A. (2005, March). A Comparison of Data Warehousing Methodologies. Communications of the ACM, 48(3): 79-84.
Watson, H., Goodhue, D., & Wixom, B. (2002, May). The Benefits of Data Warehousing: Why Some Organizations Realize Exceptional Payoffs. Information & Management, 39(6): 491.
Wixom, B., & Watson, H. (2001, March). An Empirical Investigation of the Factors Affecting Data Warehousing Success. MIS Quarterly, 25(1): 17-41.
Our editors will help you fix any mistakes and get an A+!Get started
We will send an essay sample to you in 2 Hours. If you need help faster you can always use our custom writing service.Get help with my paper