International Journal of Latest Trends in Computing (E-ISSN: 2045-5364) 138 Volume 1, Issue 2, December 2010 The Empirical Study on the Factors Affecting Data Warehousing Success Md. Ruhul Amin 1 , Md. Taslim Arefin 2 1 BRAC Bank Ltd. Dept of Technology operations, Database Administrator(DBA) Team Dhaka, Bangladesh Email: titamin21@gmail.com 2 Daffodil International University Dept of Electronics & Telecommunication Engineering Faculty of Science & Information Technology Dhaka, Bangladesh Email: arefin@daffodilvarsity.edu.bd Abstract: - Data Warehouse is the centralized store of detailed data from all relevant source systems, allowing for ad hoc discovery and drill-down analysis by multiple user groups. Various implementation factors play critical role to successful data warehouse (DW) project implementation. DW has unique characteristics that need to consider during implementation. There is little empirical research about implementation of DW to get success. Determining factors affecting DW success are important in the deployment of this DSS technology by organizations. Keywords: - Data Warehouse, Business intelligence, Decision support system 1. Introduction Data Warehouse is a technique for properly storing and managing data from different data sources for the purpose of business performance analysis. Decision support system (DSS) is an area of the information systems (IS) discipline that focuses on supporting and improving managerial decision making [1]. In terms of contemporary professional practice, DSS includes personal decision support systems (PDSS), group support systems (GSS), executive information systems (EIS), online analytical processing systems (OLAP), data warehousing (DW), and business intelligence (BI). Over the three decades of its history, DSS has moved from a radical movement that has changed the way IS were perceived in business, to a mainstream commercial IT movement, in which all organizations engage [1]. Successfully supporting managerial decision making has become critically dependent upon the availability of integrated and high quality information organized and presented to managers in a timely and easily understood manner. DWs have emerged to meet this need. Surrounded by analytical tools and models, DWs have the potential to transform operational data into BI by effectively identifying problems and opportunities. Metadata Repository Data Source ETL -Tools Data Source Data Source Analytic BI Data Mart OLAP BI Data Warehouse Figure1: A typical data warehousing system architecture 2. Aims of this Research Large organizations have different data source to manage operation, so faced significant problem to build single view of their business from different data sources. During the mid-to- late 1990s, “DW became one of the most important developments in the information systems field” [1]. It is estimated that 95 percent of the Fortune 1000 companies either have a DW in place or are planning to develop one [2]. ” In 2002, the Palo Alto Management Group predicted that the DW market would grow to a $113.5 billion market, including the sales of systems, software, services, and in-house expenditures” [2]. About 3,000 data warehousing projects are undertaken each year and if the lowest perceived data warehouse failure rate (70%) is accurate, then each year there are 2,100 failures [3]. DW project is an expensive and risky undertaking [4]. The typical project costs over $1 million in the first year alone and it is estimated that one-half to two-thirds of all initial DW efforts fail [5]. There is a common perception that the failure rate of data warehousing projects is 70 to 80 percent (Inmon