Arun Sen

An empirical investigation of the key determinants of data warehouse adoption

Data warehousing (DW) has emerged as one of the most powerful decision support technologies during the last decade. However, despite the fact that it has been around for some time, DW has experienced limited spread/use and relatively high failure rates. Treating DW as a major IT infrastructural innovation, we propose a comprehensive research model - grounded in IT adoption and organizational theories - that examines the impact of various organizational and technological (innovation) factors on DW adoption. Seven factors - five organizational and two technological - are tested in the model. The study employed rigorous measurement scales of the research variables to develop a survey instrument and targeted 2500 organizations in both manufacturing and services segments within two major states in the United States. A total of 196 firms (276 executives), of which nearly 55% were adopters, responded to the survey. The results from a logistic regression model, initially conceptualizing a direct effect of each of the seven variables on adoption, indicate that five of the seven variables (three organizational factors - commitment, size, and absorptive capacity - and two innovation characteristics - relative advantage and low complexity) are key determinants of DW adoption. Although scope for DW and preexisting data environment within the organization were favorable for adopter firms, they did not emerge as key determinants. However, the study provided an opportunity to explore a more complex set of relationships. This alternative structural model (using LISREL) provides a much richer explanation of the relationships among the antecedent variables and with adoption, the dependent variable. The study, especially the revised conceptualization, contributes to existing research by proposing and empirically testing a fairly comprehensive model of organizational adoption of an information technology (IT) innovation, more specifically a DSS technology. The findings of the study have interesting implications with respect to IT/DW adoption, both for researchers and practitioners.

A comparison of data warehousing methodologies

Using a common set of attributes to determine which methodology to use in a particular data warehousing project.

Metadata management: past, present and future

Abstract In the past, metadata has always been a second-class citizen in the world of databases and data warehouses. Its main purpose has been to define the data. However, the current emphasis on metadata in the data warehouse and software repository communities has elevated it to a new prominence. The organization now needs metadata for tool integration, data integration and change management. The paper presents a chronological account of this evolution—both from conceptual and management perspectives. Repository concepts are currently being used to manage metadata for tool integration and data integration. As a final chapter in this evolution process, we point out the need of a concept called “metadata warehouse.” A real-life data warehouse project called TAMUS Information Portal (TIP) is used to describe the types of metadata needed in a data warehouse and the changes that the metadata go through. We propose that the metadata warehouse needs to be designed to store the metadata and manage its changes. We propose several architectures that can be used to develop a metadata warehouse.

Current trends in web data analysis

Considering potential reasons for the underutilization of clickstream data and suggesting ways to enhance its use.

Decision support systems: An expert systems approach

Abstract This paper provides a conceptual framework for designing decision support systems (DSS) using an expert systems approach. Currently there is a significant trend towards the use of knowledge-based systems techniques in DSS design, but a comprehensive framework is yet to be proposed. Our paper addresses this problem and presents such a framework. Efforts are currently underway to design, implement and test a system based on this framework.

The role of opportunism in the software design reuse process

Software design involves translating a set of task requirements into a structured description of a computer program that will perform the task. A software designer can use design schema, collaborative design knowledge, or can reuse design artifacts. Very little has been done to include reuse of design artifacts in the software development life cycle, despite tremendous promises of reuse. As a result, this technique has not seen widespread use, possibly due to a lack of cognitive understanding of the reuse process. This research explores the role of a specific cognitive aspect, opportunism, in demand-side software reuse. We propose a cognitive model based on opportunism that describes the software design process with reuse. Protocol analysis verifies that the software design with reuse is indeed opportunistic and reveals that some software designers employ certain tasks of the reuse process frequently. Based on these findings, we propose a reuse support system that incorporates blackboard technology and existing reuse library management system.

Data Warehousing Process Maturity: An Exploratory Study of Factors Influencing User Perceptions

This paper explores the factors influencing perceptions of data warehousing process maturity. Data warehousing, like software development, is a process, which can be expressed in terms of components such as artifacts and workflows. In software engineering, the Capability Maturity Model (CMM) was developed to define different levels of software process maturity. We draw upon the concepts underlying CMM to define different maturity levels for a data warehousing process (DWP). Based on the literature in software development and maturity, we identify a set of features for characterizing the levels of data warehousing process maturity and conduct an exploratory field study to empirically examine if those indeed are factors influencing perceptions of maturity. Our focus in this paper is on managerial perceptions of DWP. The results of this exploratory study indicate that several factors-data quality, alignment of architecture, change management, organizational readiness, and data warehouse size-have an impact on DWP maturity, as perceived by IT professionals. From a practical standpoint, the results provide useful pointers, both managerial and technological, to organizations aspiring to elevate their data warehousing processes to more mature levels. This paper also opens up several areas for future research, including instrument development for assessing DWP maturity

A Model of Data Warehousing Process Maturity

Even though data warehousing (DW) requires huge investments, the data warehouse market is experiencing incredible growth. However, a large number of DW initiatives end up as failures. In this paper, we argue that the maturity of a data warehousing process (DWP) could significantly mitigate such large-scale failures and ensure the delivery of consistent, high quality, “single-version of truth” data in a timely manner. However, unlike software development, the assessment of DWP maturity has not yet been tackled in a systematic way. In light of the critical importance of data as a corporate resource, we believe that the need for a maturity model for DWP could not be greater. In this paper, we describe the design and development of a five-level DWP maturity model (DWP-M) over a period of three years. A unique aspect of this model is that it covers processes in both data warehouse development and operations. Over 20 key DW executives from 13 different corporations were involved in the model development process. The final model was evaluated by a panel of experts; the results strongly validate the functionality, productivity, and usability of the model. We present the initial and final DWP-M model versions, along with illustrations of several key process areas at different levels of maturity.

IT alignment strategies for customer relationship management

Customer relationship management (CRM) is the overall process of building and maintaining profitable customer relationships by delivering superior customer value and satisfaction. A CRM strategy involves the entire enterprise and is employed on an ongoing basis. Despite the fact that CRM projects incur huge expenditures, a large percentage fails to achieve the stated objectives. Failure in CRM initiatives could be avoided if a firms CRM strategies are intelligently linked with its employees, customers, channels, and IT infrastructure. In this paper, we focus on those linkages, particularly on the linkages between an organizations CRM strategies and its IT infrastructure. Even though the relationships between IT and business strategies have been extensively explored in the IT alignment literature, prior research has not addressed how a firms CRM strategies are aligned with its IT infrastructure. In this paper, we investigate the issues relating to CRM-IT alignment based on an in-depth case study of a large, well-known Internet travel agency.

Data Warehousing Infusion and Organizational Effectiveness

Data warehousing (DW) has emerged as one of the most powerful technology innovations in recent years to support organization-wide decision making and has become a key component in the information technology (IT) infrastructure. Proponents of DW claim that its infusion can dramatically enhance the ability of businesses to improve the access, distribution, and sharing of information and provide managerial decision support for complex business questions. DW is also an enabling technology for data mining, customer-relationship management, and other business-intelligence applications. Although data warehouses have been around for quite some time, they have been plagued by high failure rates and limited spread or use. Drawing upon past research on the adoption and diffusion of innovations and on the implementation of information systems (IS), we examine the key organizational and innovation factors that influence the infusion (diffusion) of DW within organizations and also examine if more extensive infusion leads to improved organizational outcomes. In this paper, we conducted a field study, where two senior managers (one from IS and the other from a line function) from 117 companies participated, and developed a structural model to test the research hypotheses. The results indicate that four of the seven variables examined in this paper-organizational support, quality of the project management process, compatibility, and complexity-significantly influence the degree of infusion of DW and that the infusion, in turn, significantly influences organization-level benefits and stakeholder satisfaction. The findings of this paper have interesting implications for both research and practice in IT and DW infusion, as well as in the organization-level impact of the infusion of enterprise-wide infrastructural and decision support technologies such as DW.