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.

Integrated decision support systems: A data warehousing perspective

This paper examines external pressures that influence the relationship between an organization's business intelligence (BI) data collection strategy and the purpose for which BI is implemented A model is proposed and tested that is grounded in institutional theory, research about competitive pressure, and research about the purpose of BI Two data collection strategies (comprehensive and problem driven) and three BI purposes (insight, consistency, and transformation) are examined Findings provide a theoretical lens to better understand the motivators and the success factors related to collecting the huge amounts of data required for BI This study also provides managers with a mental model on which to base decisions about the data required to accomplish their goals for BI

Factors influencing business intelligence (BI) data collection strategies: An empirical investigation

Even though data warehousing has been in existence for over a decade, companies are still uncertain about a critical decision - which data warehouse architecture to implement? Based on the existing literature, theory, and interviews with experts, a research model was created that identifies the various contextual factors that affect the selection decision. The results from the field survey and multinomial logistic regression suggest that various combinations of organizational factors influence data warehouse architecture selection. The strategic view of the data warehouse prior to implementation emerged as a key determinant. The research suggests an overall model for predicting the data warehouse architecture selection decision.

Key organizational factors in data warehouse architecture selection

Data warehousing is a strategic business and IT initiative in many organizations today. Data warehouses can be developed in two alternative ways -- the data mart and the enterprisewide data warehouse strategies -- and each has advantages and disadvantages. To create a data warehouse, data must be extracted from source systems, transformed, and loaded to an appropriate data store. Depending on the business requirements, either relational or multidimensional database technology can be used for the data stores. To provide a multidimensional view of the data using a relational database, a star schema data model is used. Online analytical processing can be performed on both kinds of database technology. Metadata about the data in the warehouse is important for IT and end users. A variety of data access tools and applications can be used with a data warehouse – SQL queries, management reporting systems, managed query environments, DSS/EIS, enterprise intelligence portals, data mining, and customer relationship management. A data warehouse can be used to support a variety of users – executives, managers, analysts, operational personnel, customers, and suppliers. Data warehousing concepts are brought to life through a case study of Harrah’s Entertainment, a firm that became a leader in the gaming industry with its CRM business strategy supported by data warehousing.

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Recent developments in data warehousing

Effective Information Quality Management (IQM) is still relatively poorly understood. As such, there is a gap in the literature regarding Critical Success Factors (CSFs) for IQM. This article presents 11 CSFs for effective IQM as identified through seven case studies and one ethnographic study. The CSF framework for IQM presented in this article adds to extant Information Systems (IS) theory, and may aid practitioners in the development of more effective IQM strategies.

A Critical Success Factor Framework for Information Quality Management

Companies can build a data warehouse using a top-down or a bottom-up approach, and each has its advantages and disadvantages. With the top-down approach, a project team creates an enterprise data warehouse that combines data from across the organization, and end-user applications are developed after the warehouse is in place. This strategy is likely to result in a scaleable data warehouse, but like most large IT projects, it is time consuming, expensive, and may fail to deliver benefits within a reasonable timeframe. With the bottom-up approach, a project team begins by creating a data mart that has a limited set of data sources and that meets very specific user requirements. After the data mart is complete, subsequent marts are developed, and they are conformed to data structures and processes that are already in place. The data marts are incrementally architected into an enterprise data warehouse that meets the needs of users across the organization. The appeal of the data mart strategy is that a mart can be built quickly, at relatively little cost and risk, while providing a

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James R. Revak

Papers

Sherwin-Williams' Data Mart Strategy: Creating Intelligence Across the Supply Chain