Business Intelligence Center Concepts

The approach of Business Intelligence (BI) as a support function for management decisions is established in practice and theory. BI can not just be considered as a simple sequence of isolated single projects. Its coordination requires permanent efforts to keep the BI function and the business organization in alignment. In the context of the present empirical study, BI organizations have been analyzed for the diffusion of BI units and their distinct characteristics. Furthermore these organizations have been classified in different types of BI centers based on development and operational tasks. The results indicate a wide spread implementation of BI units in companies with a multifaceted range of duty. Thereby conclusions for the practical constitution of BI centers are deduced from the results

Impact of service-oriented architectures (SOA) on business process standardization - Proposing a research model

Originally, Data Warehouses (DWH) were conceived to be components for the data support of controlling and management. From early on, this brought along the need to cope with extensive data preparation, integration, and distribution requirements. In the growing infrastructures for managerial support (“Business Intelligence”), the DWH turned into a central data hub for decision support. As the business environment and the underlying technical infrastructures are fostering an ever increasing degree of systems integration, the DWH has been recognized to be a pivotal component for all sorts of data transformation and data integration operations. Nowadays, the DWH is supposed to process both managerial and operational data – it becomes a transformation hub (TH). This article delineates the relevant motives that drive the trend towards THs and the resulting requirements. The logical composition of a TH is developed based on data transformation steps. Two case studies exemplify the application of the resulting architecture

Business Intelligence in the Cloud?

Business Intelligence (BI) deals with integrated approaches to management support. In many cases, the integrated infrastructures that are subject to BI have become complex, costly, and inflexible. A possible remedy for these issues might arise on the horizon with “Cloud Computing” concepts that promise new options for a net based sourcing of hard- and software. Currently, there is still a dearth of concepts for defining, designing, and structuring a possible adaption of Cloud Computing to the domain of BI. This contribution combines results from the outsourcing and the BI literature and derives a framework for delineating “Cloud BI” approaches. This is the bases for the discussion of six possible scenarios – some of which within immediate reach today

TOWARDS A CONSOLIDATED RESEARCH MODEL FOR UNDERSTANDING BUSINESS INTELLIGENCE SUCCESS

Research about the factors that determine the success of information systems (IS) suggests that IS success is an elusive phenomenon that can only be explained in terms of a multi-dimensional construct. Despite the usefulness and unique qualities of Business Intelligence (BI) solutions, the factors responsible for the success of BI solutions remain poorly understood. Our article attempts to illuminate a path towards a clearer understanding of how BI solutions succeed by drawing on the existing body of literature and critically reflecting on the updated model of information systems success presented by DeLone and McLean (2003) and Wixom and Watson’s (2001) model of data warehousing success. The principal research contribution consists of expanding, adapting, and synthesising these two models into a consolidated model for BI success. We derive a second order model, delineate its constructs, and conceptualise their relationships based on prior research related to IS success. The operationalization of these factors has the potential of leading to a more precise instrument for understanding, evaluating and analysing the success of BI solutions

The ICT convergence discourse in the information systems literature - A second-order observation

The growing relevance, scale, and complexity of Business Intelligence (BI) entails the need to find agile and efficient solutions for the coordination of maintenance and release processes – under consideration of the heterogeneity of the involved units on the IT and the business side. The finance industry with its mature BI infrastructures and its highly turbulent business environment is a forerunner for these developments. Based on a survey among BI users in the finance sector, relevant problem areas in the BI service provision are identified and structured. A series of qualitative interviews among banks and insurance companies is used to gain further insights into approaches for dealing with the related issues. The studies uncover several advantages of a central “BI Competency Centre” (BICC) as well as levers for effectively structuring the interfaces between BICC, IT, and user interface

Design Principles for Diffusion of Reports and Innovative Use of Business Intelligence Platforms

In order to innovate and respond quickly to new requirements, employees frequently supplement their information systems. This particularly applies to the context of business intelligence (BI) because many users supplement their BI platforms with individually tinkered spreadsheets. Unfortunately, these supplements bear numerous threats such as limited report reuse across all potential users. To address this gap, we establish a design science project. First, we qualitatively explore impediments to diffusion of reports and impediments to innovative use. Second, upon our findings and extant literature, we derive meta-requirements for BI platforms that foster diffusion of reports and innovative use. Third, we develop and discuss principles for how to design a BI platform that would meet the identified meta-requirements. The resulting design principles emphasize (1) permanent user sandboxes to improve innovative use and (2) hybrid recommendation agents based on user interaction, collaborative-filtering, and users\u27 social influence to improve diffusion of reports

Institutionalizing Analytic Data Sharing in SME Ecosystems – A Role-Based Perspective

There is a variety of reasons that sharing data among Small and Medium-Sized Enterprises (SMEs) carries business potential, particularly for analyti-cal applications. But outside a few niche domains, the number of success stories for data sharing is rather modest. Based on a qualitative study and first experiences from a research project with pilot im-plementations, we argue that this is mainly due to a lack of an institutionalized governance structure: Founding a separate legal entity for data sharing and analysis can address core concerns regarding sharing valuable data assets. However, this requires a well-calibrated set of defined roles for the in-volved partners. Based on our results we propose a first concept on delineating and mapping out those roles

Eliciting And Connecting Information Requirements: A Study Of Brokering Situations In Data Warehouse Development

Information management and logistics rely on underlying data warehouse (DWH) systems. The development of DWHs brings together different communities of practice. In this paper, we concentrate on the role of DWH professionals as a brokering community in DWH development projects. We argue that each time they engage in brokering activities towards neighboring communities of practice, representatives from these communities take brokering roles as well. As a result, a so-called brokering situation resides within the social structure, which builds a brokering community. To closer observe the roles of DWH professionals within these brokering situations, we conducted in-depth interviews with experienced DWH professionals. Based on the analysis, we argue that the selection of the community’s representatives with experience in neighboring communities can improve brokering situations. Objects exchanged between communities of practice during brokering situations can have either positive or negative effects, depending on their capacity and the type of complexity on the boundary

Linking Report Individualization and Report Standardization: A Configurational Perspective

Many organizations are facing the challenge that employees supplement their standardized Ac-counting Information Systems (AIS) with individually tinkered spreadsheets or other types of workaround systems. While such supplements provide employees with the flexibility to adjust the AIS to their individual preferences and to respond quickly to new opportunities, these sup-plements also cause adverse effects such as data redundancy, limited report reuse, loss of eco-nomic scale effects, and loss of compliance with regulatory and supervisory reporting require-ments. \ \ To provide organizations with a model for balancing report standardization and report individu-alization, I explore and analyse the AIS of four organizations. Specifically, I adopt a configura-tional perspective to examine two AIS use processes simultaneously: report standardization and report individualization. The resulting model indicates the need for an iterative approach which supports discussion and feedback on individualized reports and views individualized reports as prototypes for standardized reports. I conclude may work by discussing the value and limitations of the model and research design.

Designing Social Nudges for Enterprise Recommendation Agents: An Investigation in the Business Intelligence Systems Context

According to behavioral economists, a “nudge” is an attempt to steer individuals toward making desirable choices without affecting their range of choices. We draw on this concept, and design and examine nudges that exploit social influence’s effects to control individuals’ choices. Although recommendation agent research provides numerous insights into extending information systems and assisting end consumers, it lacks insights into extending enterprise information systems to assist organizations’ internal employees. We address this gap by demonstrating how enterprise recommendation agents (ERAs) and social nudges can be used to tackle a common challenge that enterprise information systems face. That is, we use an ERA to facilitate information (i.e., reports) retrieval in a business intelligence system. In addition, we use social nudges to steer users toward reusing specific recommended reports rather than choosing between recommended reports randomly. To test the effects of the ERA and the four social nudges, we conduct a within-subject lab experiment using 187 participants. We also conduct gaze analysis (“eye tracking”) to examine the impact of participants’ elaboration. The results of our logistic mixed-effects model show that the ERA and the proposed social nudges steer individuals toward certain choices. Specifically, the ERA steers users toward reusing certain reports. These theoretical findings also have high practical relevance and applicability: In an enterprise setting, the ERA allows employees to reuse existing resources (such as existing reports) more effectively across their organizations because employees can more easily find the reports they actually need. This, in turn, prevents the development of duplicate reports