Complexity and anticipation in development. The disadvantage
mentioned most frequently (48; 11 percent) is complexity in development. IS
cannot just buy a data warehouse; IS has to build one because each warehouse
has a unique architecture and a set of requirements that spring from the
individual needs of the organization [Ladaga, 1995; Myers, 1995b]. IS needs to
ask a wide range of questions in building it [Redding , 1995; Goldberg, 1995b]. Builders
need to pay as much attention to the structure, definitions, and flow of data
as they do to choosing hardware and software [Hildebrand, 1995; Adhikari, 1996;
Edwards, 1995; Wallace, 1994a]. Data warehouse construction requires a sense of
anticipation about future ways to use the collected records [Goldberg, 1995b].
Developers need to be aware of the constantly changing needs of their company's
business and the capabilities of the available and emerging hardware and
software [Lardear, 1995a]. How to scale the warehouse to meet increasing user
demand for both volume and complexity [Lardear, 1995a] makes its development
more complex. Also, there are difficulties in choosing the right products
[Harding, 1994; Cafasso, 1995b]. In summary, developing such a large database
requires an expert [Harding, 1994].
2. Takes time to build. Second, 32 (7 percent) articles point out that to build a data warehouse takes time (2 to 3 years) [Goldberg, 1995b; Hildebrand, 1995; Ladaga, 1995; Redding, 1995]. In a situation where there is not strong executive sponsorship, IS directors or others wishing to develop a warehouse may spend an inordinate amount of time justifying the need.
3. Expensive to build. Similarly, 17 (4 percent) mentioned that a data warehouse is also expensive to build ($2 to 3 million) [Harding, 1994; Hildebrand, 1995; Ladaga, 1995;Redding ,
1995]. One reason data warehouses are so expensive is that data must be moved
or copied from existing databases, sometimes manually, and data needs to be
translated into a common format [Cole, 1995h].
4. Lack of API. Ten (2 percent) articles suggest that data warehousing software still lack a set of application programming interfaces (API) or other standards that shuttle data smoothly through the entire warehouse process, such as Open Database Connectivity (ODBC) interface (Microsoft Corp.). However, ODBC API that lets PCs access data from many different databases, is not everywhere [Nash, 1995b].
5. End-user training. Seven (2 percent) articles suggest it is necessary to create a new "mind-set" with all employees who must be prepared to capitalize upon the innovative data analysis provided by data warehouses; those end users require extensive training. A communication plan is essential to educate all constituents [Goldberg, 1995b; Ladaga, 1995].
6. Complexity involved in SMP and MPP. Six (1 percent) of the articles point out the complexity of data warehousing, which will be increased if the warehouses involve symmetrical multiprocessing (SMP) and massively parallel processing (MPP). Synchronization and shared access are difficult [Goldberg, 1995b; Burleson, 1995].
7. Difficulty in distributed database environment. Because the data warehouse is a method of bringing disparate data together, it is centralized by its very nature [Wallace, 1994b] and this is mentioned in 5 or 1 percent of the articles. While many companies are still in the preliminary stages of putting their data warehouses together, this centralization means only workers located at the same site as the warehouse have access to the data [Wallace, 1994b].
8. Time-lag between data warehouses and operation. Lastly, in 3 (1 percent) of the articles, it is said that the data in data warehouses is extracted from operational databases that are continuously changing. A real-time data warehouse is an oxymoron because it is impossible to have real-time replication while maintaining a full-scale data warehouse [Burleson, 1995]. Data warehouses store only a time slice of corporate data that is steadily drifting backward out of relevance until the warehouses are replenished.
Conclusion
This study makes two contributions: one for practitioners who are planning to implement data warehouses, and one for researchers who are studying the phenomenon. For the practitioners, the lists of advantages and disadvantages show what to expect when implementing a data warehouse and what kinds of problems they may face. Simplicity of the data structure, ease of use, fast access, and better quality data which leads to improved productivity and decision making are the most mentioned advantages. On the other hand, complexity of the development, time, and cost to build data warehouses are the most recognized disadvantages. For researchers, the findings provide a wide range of issues to be researched.
p> < � > 0 � @e� parallel processing. Eleven (2 percent) of these authors indicate that parallel processing helps users perform database tasks more quickly [Brown, 1995; Bull, 1995b; Stedman, 1995a]. Users can ask questions that were too process-intensive to answer before and data warehouse can handle more customers, users, transactions, queries, and messages. It supports the higher performance demands in client/server environment, provides unlimited scaleability, and thus, better price/performance [Capacity Management Review, 1995].
11. Robust processing engines. Ten (2 percent) of the articles mention that data warehouses allow users to directly obtain and refine data from different software applications without affecting the operational databases, and to integrate different business tasks into a single, streamlined process supported by real-time information. This provides users with robust processing engines [Goldberg, 1995b; Seybold, 1995].
12. Platform independent. Seven (2 percent) of the articles point out that data warehouses can be built on everything from a high-end PC to a mainframe, although many are choosing Unix servers and running their warehouses in a client/server environment. IBM and other five data warehouse software venders formed alliances to clear the cross-platform hurdles inherent in data warehouse implementation. Similar partnerships have been formed by other vendors. It is crucial to have such independence which was not easy in the legacy system [Systems Management 3X 400, 1995; Wallace, 1994a].
13. Computing infrastructure. Seven (2 percent) of the articles mention data warehousing helps the organization create a computing infrastructure that can support changes in computer systems and business structures [Wallace, 1994b].
14. Downsizing facilitation. Six (1 percent) articles suggest that data warehouses empower employees to make decentralized decisions since they put information closer to users. They are designed to give end users faster access to the information that is already there without impacting other systems or resources. Therefore, users do not need to ask IS to get needed data and IS managers can concentrate on other tasks. This potentially cuts the information middle-man who passes information from one place to another and suggests downsizing [Bull, 1995b; Seybold, 1995a].
15. Quantitative value. Another advantage, mentioned in six articles (1 percent), is realistic benchmarking. Data warehouses provide the quantitative metrics necessary to establish business process baselines that are derived from historical data and allow business managers to measure progress [Jain, 1995; Modisette, 1996].
16. Security. Three (1 percent) articles talk about the fact that clients of the data warehouses cannot directly query the production databases, thus improving security of the production databases as well as their productivity [Ricciuti, 1994a]. Some warehouses also provide management services for handling security [Smith, 1996].
2. Takes time to build. Second, 32 (7 percent) articles point out that to build a data warehouse takes time (2 to 3 years) [Goldberg, 1995b; Hildebrand, 1995; Ladaga, 1995; Redding, 1995]. In a situation where there is not strong executive sponsorship, IS directors or others wishing to develop a warehouse may spend an inordinate amount of time justifying the need.
3. Expensive to build. Similarly, 17 (4 percent) mentioned that a data warehouse is also expensive to build ($2 to 3 million) [Harding, 1994; Hildebrand, 1995; Ladaga, 1995;
4. Lack of API. Ten (2 percent) articles suggest that data warehousing software still lack a set of application programming interfaces (API) or other standards that shuttle data smoothly through the entire warehouse process, such as Open Database Connectivity (ODBC) interface (Microsoft Corp.). However, ODBC API that lets PCs access data from many different databases, is not everywhere [Nash, 1995b].
5. End-user training. Seven (2 percent) articles suggest it is necessary to create a new "mind-set" with all employees who must be prepared to capitalize upon the innovative data analysis provided by data warehouses; those end users require extensive training. A communication plan is essential to educate all constituents [Goldberg, 1995b; Ladaga, 1995].
6. Complexity involved in SMP and MPP. Six (1 percent) of the articles point out the complexity of data warehousing, which will be increased if the warehouses involve symmetrical multiprocessing (SMP) and massively parallel processing (MPP). Synchronization and shared access are difficult [Goldberg, 1995b; Burleson, 1995].
7. Difficulty in distributed database environment. Because the data warehouse is a method of bringing disparate data together, it is centralized by its very nature [Wallace, 1994b] and this is mentioned in 5 or 1 percent of the articles. While many companies are still in the preliminary stages of putting their data warehouses together, this centralization means only workers located at the same site as the warehouse have access to the data [Wallace, 1994b].
8. Time-lag between data warehouses and operation. Lastly, in 3 (1 percent) of the articles, it is said that the data in data warehouses is extracted from operational databases that are continuously changing. A real-time data warehouse is an oxymoron because it is impossible to have real-time replication while maintaining a full-scale data warehouse [Burleson, 1995]. Data warehouses store only a time slice of corporate data that is steadily drifting backward out of relevance until the warehouses are replenished.
Conclusion
This study makes two contributions: one for practitioners who are planning to implement data warehouses, and one for researchers who are studying the phenomenon. For the practitioners, the lists of advantages and disadvantages show what to expect when implementing a data warehouse and what kinds of problems they may face. Simplicity of the data structure, ease of use, fast access, and better quality data which leads to improved productivity and decision making are the most mentioned advantages. On the other hand, complexity of the development, time, and cost to build data warehouses are the most recognized disadvantages. For researchers, the findings provide a wide range of issues to be researched.
p> < � > 0 � @e� parallel processing. Eleven (2 percent) of these authors indicate that parallel processing helps users perform database tasks more quickly [Brown, 1995; Bull, 1995b; Stedman, 1995a]. Users can ask questions that were too process-intensive to answer before and data warehouse can handle more customers, users, transactions, queries, and messages. It supports the higher performance demands in client/server environment, provides unlimited scaleability, and thus, better price/performance [Capacity Management Review, 1995].
11. Robust processing engines. Ten (2 percent) of the articles mention that data warehouses allow users to directly obtain and refine data from different software applications without affecting the operational databases, and to integrate different business tasks into a single, streamlined process supported by real-time information. This provides users with robust processing engines [Goldberg, 1995b; Seybold, 1995].
12. Platform independent. Seven (2 percent) of the articles point out that data warehouses can be built on everything from a high-end PC to a mainframe, although many are choosing Unix servers and running their warehouses in a client/server environment. IBM and other five data warehouse software venders formed alliances to clear the cross-platform hurdles inherent in data warehouse implementation. Similar partnerships have been formed by other vendors. It is crucial to have such independence which was not easy in the legacy system [Systems Management 3X 400, 1995; Wallace, 1994a].
13. Computing infrastructure. Seven (2 percent) of the articles mention data warehousing helps the organization create a computing infrastructure that can support changes in computer systems and business structures [Wallace, 1994b].
14. Downsizing facilitation. Six (1 percent) articles suggest that data warehouses empower employees to make decentralized decisions since they put information closer to users. They are designed to give end users faster access to the information that is already there without impacting other systems or resources. Therefore, users do not need to ask IS to get needed data and IS managers can concentrate on other tasks. This potentially cuts the information middle-man who passes information from one place to another and suggests downsizing [Bull, 1995b; Seybold, 1995a].
15. Quantitative value. Another advantage, mentioned in six articles (1 percent), is realistic benchmarking. Data warehouses provide the quantitative metrics necessary to establish business process baselines that are derived from historical data and allow business managers to measure progress [Jain, 1995; Modisette, 1996].
16. Security. Three (1 percent) articles talk about the fact that clients of the data warehouses cannot directly query the production databases, thus improving security of the production databases as well as their productivity [Ricciuti, 1994a]. Some warehouses also provide management services for handling security [Smith, 1996].
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