عوامل موثر بر استراتژی های جمع آوری داده های هوش کسب و کار (BI) : تحقیق تجربی

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.

In the last decade, Business intelligence (BI) has evolved as one of the critical applications in organizations to provide useful insight, support decision-making, and drive organizational performance [10] and [54]. BI has permeated various industries including retail, insurance, banking, finance and securities, telecommunications, and manufacturing [58]. Companies such as Continental Airlines and First American Corporation (FAC) have successfully implemented BI to improve their customer loyalty and increase their return on investments (ROI) [2] and [19]. There are other organizations, however, that have not been as successful in utilizing BI to increase their profit and achieve their expected performance [31]. Such mixed results have motivated researchers to examine a variety of factors that contribute to the successful implementation of BI [21], [28], [40] and [41]. The effectiveness of BI lies in its ability to present business information in a timely manner [18]. Thus, the success of any BI project depends on the data available. Furthermore, research indicates that data consistency and data quality are a major cause of the success or failure of BI initiatives [3], [8] and [24]. Data collection does not refer just to the collection of data for BI applications, but it is also concerned with providing clean, consistent, high quality, and integrated data for BI applications [52], [56] and [82]. Therefore, organizational data collection strategy may form a crucial foundation for BI success and differentiate between organizations that successfully implement BI to realize high benefits and organizations that are unable to gain high benefits through BI. The data for BI is generally stored in a central repository known as the data warehouse [22] and [74]. Data warehousing strategies involve the collection and integration of data for BI purposes [25]. Although research has examined some factors that affect the selection of different data warehousing strategies [12] and [41], these studies are largely descriptive. There are few theoretically grounded studies that focus specifically on the factors that influence BI data collection strategies. The purpose of this research is to examine the forces that influence an organization's BI data collection strategy. The model developed and tested in this study provides a theoretical lens to better understand the success factors and the motivators related to collecting the huge amounts of data required for BI. The findings of the study also provide managers with insight into how to develop strategies and plans for collecting data that match the purpose of BI. This will further help BI managers and users by providing them with a mental model on which they can base their decisions about the data required to accomplish their goals for BI.

As organizations embark on new IT-enabled organizational initiatives, such as BI implementation, their strategic and tactical choices are influenced by external factors such as competitive and institutional pressures. Drawing on institutional theory and strategic management literature, we developed and tested a theoretical model that links various external factors to BI implementation choices including the goal of BI implementation and the data collection strategy used. Our results offer support for a positive relationship between institutional pressures to adopt BI and implementing BI for consistency. Our results also offer support for a positive relationship between such BI implementation goals as consistency and organizational transformation, and selecting a comprehensive data collection strategy. Surprisingly we did not find support between competitive pressure and BI implementation goals, which may point to a lack of strategic alignment between business and IT. Additional studies are necessary to investigate such non-supported relationships. We believe that our model has important implications for both researchers and practitioners because it highlights the importance of taking into account external factors when making and evaluating BI implementation decisions. BI technology has pervaded every large industry sector including banking, finance, insurance and security, government, manufacturing, retail, and services. Therefore, understanding the relationship between the purpose for implementing BI and BI data collection strategies may prove vital for the success of any BI initiative. This study may serve as a first step in understanding the influence of external environmental factors on BI purposes and BI data collection strategies.