Successfully supporting managerial decision-making is critically dependent upon the availability of integrated, high quality information organized and presented in a timely and easily understood manner. Data warehouses have emerged to meet this need. They serve as an integrated repository for internal and external data—intelligence critical to understanding and evaluating the business within its environmental context. With the addition of models, analytic tools, and user interfaces, they have the potential to provide actionable information resources—business intelligence that supports effective problem and opportunity identification, critical decision-making, and strategy formulation, implementation, and evaluation. Four themes frame our analysis: integration, implementation, intelligence, and innovation. 1:four major categories of business environment factors is INTEGRATION,IMPLEMENTATION,INTELLIGENCE AND INNOVATION. Organizations use data warehousing to support strategic and mission-critical applications. Data deposited into the data warehouse must be transformed into information and knowledge and appropriately disseminated to decision-makers within the organization and to critical partners in various capacities within the organizational value chain. Crucial problems that must be addressed in this area are: the modes of dissemination of information to the end user; the development, selection, and implementation of appropriate models, analytic tools, and data mining tools; the privacy and security of data; system performance; and adequate levels of training and support. The human–computer interface is of paramount importance in the data warehouse environment and the primary determinant of success from the end-user perspective. In order to support analysis and reporting tasks, the data warehouse must have high quality data and make these data accessible through intuitive interface technologies. Data warehouse browsing tools provide star- schema query-like access through a flexible menu-based interface, with pull-down menus representing important dimensions. These types of tools are easy to use and support some ad-hoc exploration, but are usually controlled through an administrative layer that determines the data available to endusers. In developing a flexible interface, there is a tradeoff between the ability to express ad-hoc queries and the ease-of-use that results from pre-defined constructs implemented by data warehouse designers and administrators. Of course, SQL can provide an ad-hoc query facility, but its use requires some care in the data warehouse environment where the combination of very large tables and ill-formed user queries can produce some truly awful performance and potentially erroneous results. Casual users may not have sufficient understanding of SQL or of the database schema to effectively use such an interface. Typically, only trained power users (e.g., DBAs, application developers) are permitted to write SQL queries on .

1. Successfully supporting managerial decision-making is critically dependent upon the availability
of integrated, high quality information organized and presented in a timely and easily understood
manner. Data warehouses have emerged to meet this need. They serve as an integrated repository
for internal and external data—intelligence critical to understanding and evaluating the business
within its environmental context. With the addition of models, analytic tools, and user interfaces,
they have the potential to provide actionable information resources—business intelligence that
supports effective problem and opportunity identification, critical decision-making, and strategy
formulation, implementation, and evaluation. Four themes frame our analysis: integration,
implementation, intelligence, and innovation.
1:four major categories of business environment factors is
INTEGRATION,IMPLEMENTATION,INTELLIGENCE AND INNOVATION.
Organizations use data warehousing to support strategic and mission-critical applications. Data
deposited into the data warehouse must be transformed into information and knowledge and
appropriately disseminated to decision-makers within the organization and to critical partners in
various capacities within the organizational value chain. Crucial problems that must be addressed
in this area are: the modes of dissemination of information to the end user; the development,
selection, and implementation of appropriate models, analytic tools, and data mining tools; the
privacy and security of data; system performance; and adequate levels of training and support.
The human–computer interface is of paramount importance in the data warehouse environment
and the primary determinant of success from the end-user perspective. In order to support
analysis and reporting tasks, the data warehouse must have high quality data and make these data
accessible through intuitive interface technologies. Data warehouse browsing tools provide star-
schema query-like access through a flexible menu-based interface, with pull-down menus
representing important dimensions. These types of tools are easy to use and support some ad-hoc
exploration, but are usually controlled through an administrative layer that determines the data
available to endusers. In developing a flexible interface, there is a tradeoff between the ability to
express ad-hoc queries and the ease-of-use that results from pre-defined constructs implemented
by data warehouse designers and administrators. Of course, SQL can provide an ad-hoc query
facility, but its use requires some care in the data warehouse environment where the combination
of very large tables and ill-formed user queries can produce some truly awful performance and
potentially erroneous results. Casual users may not have sufficient understanding of SQL or of
the database schema to effectively use such an interface. Typically, only trained power users
(e.g., DBAs, application developers) are permitted to write SQL queries on a data warehouse.
There are a number of commercially available analytic tools and data mining tools applied in
data warehousing. Online Analytical Processing (OLAP) tools support multidimensional views

2. of the data warehouse. OLAP bcubesQ are frequently extracted from the data warehouse and
made available to managers for specific decision-making situations. Using tools such as
ORACLE Discoverer, Cognos PowerPlay, MicroStrategy, Business Objects, or even pivot tables
in Excel spreadsheets managers can bslice, dice, drill-down, and rollupQ instance-level data
along pre-defined dimensions. These can be extremely useful for identifying and exploring the
causes of problem situations. For example, drilling down on sales for a specific product .
Analytic tools and data mining tools have become quite powerful; however, they may be too
complex and sophisticated for the average information consumer. Managers who are comfortable
with paper-based reports may find the transition to data warehouse tools to be uncomfortable and
counterproductive. Keys to effective data warehouse use are identifying the right tools for the
different types of data warehouse users and providing adequate training and support once those
tools have been selected. For a manager whose primary concern is monitoring sales levels over
time by product and sales region a simple Excel spreadsheet automatically connected to an
OLAP cube may be sufficient. For a manager attempting to identify new marketing strategies
and pricing schemes more sophisticated tools are required. Furthermore, the value of the
available tools is dependent upon matching the data characteristics to the managerial need. Early
data warehouse applications assumed that currency was not a required characteristic for
managerial decision-making. Hence data warehouses were often brefreshedQ from operational
databases on a weekly or monthly basis. Given the accelerated pace of business, bactiveQ or
bflashQ data warehouses are becoming more prevalent. Such data warehouses are updated
virtually in parallel with operational databases. This can lead to integrity and consistency
problems because data are in a constant state of flux. Analytical results can vary literally from
one moment to another. The trend toward real-time data warehousing for both tactical and
strategic decision-making has led to interest in the concept of Business Activity Monitoring
(BAM) [39]. When faced with a critical business decision, the manager must quickly assemble
and analyze the situation with full views of both the organizational internal and external
contexts. This requires access to current as well as historical information on objectives, past
performance, external forces, internal resources, potential events, and timerelated issues. The
manager will also need to be able to communicate and coordinate with others within and outside
the organization. Finally, a decision will be made to take action or to delegate the decision-
making authority. Fully deployed BAM systems assume that these capabilities are available to
managers throughout the business organization. In reality, tactical decision support systems and
BAM solutions will require innovative research and development before they reach an adequate
level of maturity for widespread deployment. Research issues pertinent to real-time data
warehousing include integration of operational information with historical information, handling
events and alerts as real-time data, scalability to growing numbers of users, realtime performance

3. of analytic engines, and building active applets and alert mechanisms into user interfaces (e.g.,
electronic dashboards) [8]. The role of an effective data warehouse is central to the future of real-
time tactical and strategic decision-making.
its the summary of ur entire questions answers pls go through the links for more quick
information
R.D. Banker, H. Chang, S.N. Janakiraman, C.A. Konstans, A balanced scorecard analysis of
performance metrics, European Journal of Operational Research 154 (2) (2004 (April)). [2] D.
Berndt, A. Hevner, J. Studnicki, The CATCH data warehouse: support for community health
care decision making, Decision Support Systems 35 (2003 (June)). [3] A.C. Boynton, R.W.
Zmud, An assessment of critical success factors, Sloan Management Review 25 (4) (1984
(Summer)). [4] G.G. Bustamente, K. Sorenson, Decision support at land’s end—an evolution,
IBM Systems Journal 33 (2) (1994 (June)).
Solution
Successfully supporting managerial decision-making is critically dependent upon the availability
of integrated, high quality information organized and presented in a timely and easily understood
manner. Data warehouses have emerged to meet this need. They serve as an integrated repository
for internal and external data—intelligence critical to understanding and evaluating the business
within its environmental context. With the addition of models, analytic tools, and user interfaces,
they have the potential to provide actionable information resources—business intelligence that
supports effective problem and opportunity identification, critical decision-making, and strategy
formulation, implementation, and evaluation. Four themes frame our analysis: integration,
implementation, intelligence, and innovation.
1:four major categories of business environment factors is
INTEGRATION,IMPLEMENTATION,INTELLIGENCE AND INNOVATION.
Organizations use data warehousing to support strategic and mission-critical applications. Data
deposited into the data warehouse must be transformed into information and knowledge and
appropriately disseminated to decision-makers within the organization and to critical partners in
various capacities within the organizational value chain. Crucial problems that must be addressed
in this area are: the modes of dissemination of information to the end user; the development,
selection, and implementation of appropriate models, analytic tools, and data mining tools; the
privacy and security of data; system performance; and adequate levels of training and support.
The human–computer interface is of paramount importance in the data warehouse environment
and the primary determinant of success from the end-user perspective. In order to support
analysis and reporting tasks, the data warehouse must have high quality data and make these data

4. accessible through intuitive interface technologies. Data warehouse browsing tools provide star-
schema query-like access through a flexible menu-based interface, with pull-down menus
representing important dimensions. These types of tools are easy to use and support some ad-hoc
exploration, but are usually controlled through an administrative layer that determines the data
available to endusers. In developing a flexible interface, there is a tradeoff between the ability to
express ad-hoc queries and the ease-of-use that results from pre-defined constructs implemented
by data warehouse designers and administrators. Of course, SQL can provide an ad-hoc query
facility, but its use requires some care in the data warehouse environment where the combination
of very large tables and ill-formed user queries can produce some truly awful performance and
potentially erroneous results. Casual users may not have sufficient understanding of SQL or of
the database schema to effectively use such an interface. Typically, only trained power users
(e.g., DBAs, application developers) are permitted to write SQL queries on a data warehouse.
There are a number of commercially available analytic tools and data mining tools applied in
data warehousing. Online Analytical Processing (OLAP) tools support multidimensional views
of the data warehouse. OLAP bcubesQ are frequently extracted from the data warehouse and
made available to managers for specific decision-making situations. Using tools such as
ORACLE Discoverer, Cognos PowerPlay, MicroStrategy, Business Objects, or even pivot tables
in Excel spreadsheets managers can bslice, dice, drill-down, and rollupQ instance-level data
along pre-defined dimensions. These can be extremely useful for identifying and exploring the
causes of problem situations. For example, drilling down on sales for a specific product .
Analytic tools and data mining tools have become quite powerful; however, they may be too
complex and sophisticated for the average information consumer. Managers who are comfortable
with paper-based reports may find the transition to data warehouse tools to be uncomfortable and
counterproductive. Keys to effective data warehouse use are identifying the right tools for the
different types of data warehouse users and providing adequate training and support once those
tools have been selected. For a manager whose primary concern is monitoring sales levels over
time by product and sales region a simple Excel spreadsheet automatically connected to an
OLAP cube may be sufficient. For a manager attempting to identify new marketing strategies
and pricing schemes more sophisticated tools are required. Furthermore, the value of the
available tools is dependent upon matching the data characteristics to the managerial need. Early
data warehouse applications assumed that currency was not a required characteristic for
managerial decision-making. Hence data warehouses were often brefreshedQ from operational
databases on a weekly or monthly basis. Given the accelerated pace of business, bactiveQ or
bflashQ data warehouses are becoming more prevalent. Such data warehouses are updated
virtually in parallel with operational databases. This can lead to integrity and consistency
problems because data are in a constant state of flux. Analytical results can vary literally from

5. one moment to another. The trend toward real-time data warehousing for both tactical and
strategic decision-making has led to interest in the concept of Business Activity Monitoring
(BAM) [39]. When faced with a critical business decision, the manager must quickly assemble
and analyze the situation with full views of both the organizational internal and external
contexts. This requires access to current as well as historical information on objectives, past
performance, external forces, internal resources, potential events, and timerelated issues. The
manager will also need to be able to communicate and coordinate with others within and outside
the organization. Finally, a decision will be made to take action or to delegate the decision-
making authority. Fully deployed BAM systems assume that these capabilities are available to
managers throughout the business organization. In reality, tactical decision support systems and
BAM solutions will require innovative research and development before they reach an adequate
level of maturity for widespread deployment. Research issues pertinent to real-time data
warehousing include integration of operational information with historical information, handling
events and alerts as real-time data, scalability to growing numbers of users, realtime performance
of analytic engines, and building active applets and alert mechanisms into user interfaces (e.g.,
electronic dashboards) [8]. The role of an effective data warehouse is central to the future of real-
time tactical and strategic decision-making.
its the summary of ur entire questions answers pls go through the links for more quick
information
R.D. Banker, H. Chang, S.N. Janakiraman, C.A. Konstans, A balanced scorecard analysis of
performance metrics, European Journal of Operational Research 154 (2) (2004 (April)). [2] D.
Berndt, A. Hevner, J. Studnicki, The CATCH data warehouse: support for community health
care decision making, Decision Support Systems 35 (2003 (June)). [3] A.C. Boynton, R.W.
Zmud, An assessment of critical success factors, Sloan Management Review 25 (4) (1984
(Summer)). [4] G.G. Bustamente, K. Sorenson, Decision support at land’s end—an evolution,
IBM Systems Journal 33 (2) (1994 (June)).