Ein Reifegradmodell für Business-Intelligence-Lösungen

Zusammenfassung: Die steigende Bedeutung von Business Intelligence (BI) für die Steuerung und Wettbewerbsfähigkeit von Unternehmen erfordert eine kontinuierliche, transparente und detaillierte Bewertung der BI-Lösungen in den Organisationen. Zu diesem Zweck wurde das BI-spezifische Reifegradmodell biMM (Business Intelligence Maturity Model) entwickelt. Im Beitrag werden dessen fünf Reifegradstufen in den Dimensionen Fachlichkeit, Technik und Organisation beschrieben. Auswertungen einer Studie, basierend auf biMM, zeigen den State of the Art von BI-Lösungen im deutschsprachigen Raum. Die durchschnittliche Reife der Unternehmen ist der dritten Stufe zuzuordnen. Aus den geplanten BI-Aktivitäten und dem zukünftig prognostizierten Reifegrad lassen sich Trends und künftige Herausforderungen für Praxis und Wissenschaft gleichermaßen ableiten. Dazu zählen aufbauorganisatorische Maßnahmen, wie die Einrichtung eines BI-Competence-Centers, ein stärkerer Fokus auf die Wirtschaftlichkeit und ein verbesserter Wirkungsgrad der BI-Architektu

25+ Years of Business Intelligence and Analytics Minitrack at HICSS: A Text Mining Analysis

This research project is inspired by the occasion of the 50th anniversary of the Hawaii International Conferences on Systems Sciences (HICSS). As the current co-chairs of the longest-running minitrack on Business Intelligence (BI), Business Analytics (BA) and Big Data (as it is currently known) at HICSS, we report on its 27-year history of relevant and interesting research. Our insights into the key research themes and their progress over time were obtained through a semantic text mining of all research publications included in this minitrack since 1990. We also illustrate a practical method of using a sophisticated text-mining tool (Leximancer) so that it could be replicated by other researchers interested in content analysis methods in other research fields

The Digital Twin – Birth of an Integrated System in the Digital Age

Today we live in a time where new technologies are developing rapidly. Digitalization and automation are finding their way into various industrial sectors, especially in the area of Industry 4.0. As in previous digitalization efforts in the manufacturing sector, it can be observed that the discourse is strongly concentrated on technological themes, neglecting the overall integration of technologies into the organization. In this paper, we conduct a literature review on the concept of a digital twin, i.e. a simulation-oriented closed loop system consisting of physical and digital components. We map the identified themes to the elements of a socio-technical system to show which issues in the discourse are underrepresented from a managerial point of view in order to provide indications for a more holistic discourse

Social Business Intelligence: a Literature Review and Research Agenda

The domains of Business Intelligence (BI) and social media have meanwhile become significant research fields. While BI aims at supporting an organization’s decisions by providing relevant analytical data, social media is an emerging source of personal and individual knowledge, opinion, and attitudes of stakeholders. For a while, a convergence of the two domains can be observed in real-world implementations and research, resulting in concepts like social BI. Many research questions still remain open – or even worse – are not yet formulated. Therefore, the paper aims at articulating a research agenda for social BI. By means of a literature review we systematically explored previous work and developed a framework. It contrasts social media characteristics with BI design areas and is used to derive the social BI research agenda. Our results show that the integration of social media (data) into a BI system has impact on almost all BI design objects

Towards a Configurative Publication Schema for Design Science Research

Design science research (DSR) has matured and gained acceptance as an appropriate information systems research method. Despite the increasing number of DSR publications there is still no common sense and no comprehensive guidance how to present DSR in scientific literature. Therefore, this paper investigates the potential of a configurative DSR publication schema by means of a reference model allowing the deduction of concrete publication schemas. These schemas provide more detailed advice depending on the particular research context, such as the intended artifact type, the evaluation method, or the knowledge contribution type. By identifying configuration parameters (through an investigation of 13 DSR meta-analysis papers) and common configurations (through a meta-analysis of 52 DSR journal publications) we lay the foundations for a configurative reference model which can be adapted to provide detailed guidance in concrete DSR publication situations for both authors and reviewers. A detailed example sketches the future artifact

Towards a Framework for Open Data Related Innovation Contests

Open data is considered as a promising resource for innovation, in particular in light of the ongoing digitization of society and economy. Currently, organizations lack of knowledge and skills to make efficient use of this resource. Based on the theoretical underpinning of absorptive capacity, the paper investigates how organizations could be supported by using open data for innovation. Following the design science research paradigm, a morphological box for open data related innovation contests is designed by means of an extensive literature review as well as an empirical investigation of an academic analytics challenge. The resulting artifact guides the design of open data related innovation contests by illustrating the main design elements and their options

THE IMPACT OF NATIONAL CULTURE ON BUSINESS INTELLIGENCE MATURITY MODELS

Maturity level modeling has been a well-established and important stream of IS research. Maturity models have played a significant role in guiding organizational process improvements in many areas. It is therefore surprising that maturity model design and development methodologies have not yet been scrutinized from cross-cultural and international perspectives. In this research, we have prescribed a rigorous approach to identifying, validating and calibrating maturity models constructs for cross-cultural application. We have employed this new methodology in the context of widely accepted business intelligence maturity model that has been initially developed in Germany. Our current effort involves following our proposed methodology to target and calibrate those nationally specific model components that need modification and calibration for application in the US. We report on our progress, and we discuss implications in terms of both findings relevant to culture in IS research, maturity model methodology research and BI maturity model research

An Evaluation of Open Source Unit Testing Tools Suitable for Data Warehouse Testing

Verification and validation are two important processes in the software system lifecycle. Despite the importance of these processes, a recent survey has shown that testing of data warehouse systems is currently neglected. The survey participants named besides others modest budget and the lack of appropriate tools as potential reasons for this circumstance. In order to verify these reasons, the paper at hand presents an evaluation of unit testing tools suitable for data warehouse testing. To address the modest budget problem, the range of evaluation candidates is limited to no charge, open source solutions, namely AnyDbTest, BI.Quality, DbFit, DbUnit, NDbUnit, SQLUnit, TSQLUnit, and utPLSQL. The evaluation follows the IEEE 14102-2010 guidelines for evaluation and selection of computeraided software engineering tools in order to guarantee benefits from a practioners’ as well as scientific point of view. It results in a detailed overview of how the testing tools meet criteria such as different testing functionalities. At least one tool, namely DbFit, can be identified as a promising candidate with regard to the requirements

Looking Ahead: Business Intelligence & Analytics Research in the Post-Pandemic New Normal

The COVID 19 black swan event has disrupted every aspect of life in unprecedented ways, causing organizations to scramble to effectively sense and respond to the tumultuous business environment. Business intelligence and analytics (BI&A) capability has gained attention as a key weapon in the arsenal needed to combat turbulent times and to adjust to the post-pandemic new normal. Post-pandemic BI&A trends point to changes in organizational priorities for BI&A infrastructure that influence the traditional view of BI&A architecture and its role within an organization. As a result, new challenges and opportunities are emerging. This paper identifies and examines twelve key post-pandemic BI&A trends from industry practice and six major research themes. It also proposes an initial set of research questions that could inspire future research in BI&A in the post-pandemic new normal