Data Warehouse and Business Intelligence Systems in the Context of E-HRM

Data Warehouse and Business Intelligence Systems in the Context of E-HRM

Martin Burgard (Saarland University, Germany) and Franca Piazza (Saarland University, Germany)
DOI: 10.4018/978-1-59904-883-3.ch034
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The increased use of information technology leads to the generation of huge amounts of data which have to be stored and analyzed by appropriate systems. Data warehouse systems allow the storage of these data in a special multidimensional data base. Based on a data warehouse, business intelligence systems provide different analysis methods such as online analytical processing (OLAP) and data mining to analyze these data. Although these systems are already widely used and the usage is still growing, their application in the area of electronic human resource management (e-HRM) is rather scarce. Therefore, the objective of this article is to depict the components and functionality of these systems and to illustrate the application possibilities and benefits of these systems by selected application examples in the context of e-HRM.
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In the past the importance of data warehouse and business intelligence systems has continuously increased and the rate of companies using a data warehouse and/or a business intelligence system is rather high (e.g., Watson, Annino, Wixom, Avery, & Rutherford, 2001). An increasing number of case study publications (e.g., Marks & Frolick, 2001; Watson, Wixom, Hoffer, Anderson-Lehman & Reynolds, 2006) and general literature for practitioners (e.g., Humphries, Hawkins, & Dy, 1999) are further indicators showing the ever-growing importance of these systems. On the other hand, publications concerning these system categories in the context of e-HRM, except for short discussions to some isolated topics as online recruiting (Lin & Stasinskaya, 2002), enterprise resource planning (Ashbaugh & Miranda, 2002), or human resource information systems (Kovach, Hughes, Fagan, & Magitti, 2002) are rather scarce. Data warehouse and business intelligence systems are commonly used in sales or marketing departments. In contrast, their use in HR departments is relatively low (Watson et al., 2001). However, the adoption of these systems in the context of e-HRM offers new potentials to the management of human resources. In the following their technical and functional aspects are depicted.

A data warehouse is defined as a “subject-oriented, integrated, non-volatile and time-variant collection of data in support of management’s decisions” (Inmon, 2005, p. 29). So the main task of the data warehouse is to integrate the data from a variety of different source systems existing inside and outside a company in a single data base and to store the data in a multidimensional structure which is optimized to support the management’s analysis activities. In doing so the operative systems are no longer charged with the reporting requests of the management which resulted in poor system performance.

The data warehouse is the core component of the data warehouse system which further consists of several components (see Figure 1): the extraction, transformation, and loading system (ETL-system), the administration system, the archiving system, and the metadata repository.

Figure 1.

Reference architecture of data warehouse and business intelligence systems

Key Terms in this Chapter

Online Analytical Processing (OLAP): Refers to the possibilities to consolidate, view, and analyze data according to multiple dimensions. The fast analysis of shared multidimensional information (FASMI) concept characterizes OLAP by means of five attributes.

Data Warehouse: A subject-oriented, integrated, time-variant, and nonvolatile collection of data. The data are usually stored in multidimensional cubes, an optimized way to provide data for analyze purposes.

Data Mining: Subsumes a variety of methods to extract unknown patterns out of a large amount of data. Data mining methods originate from the area of machine learning, statistics, and artificial intelligence. The main tasks of data mining are classification, segmentation, and association analysis.

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