Felix Wortmann

Internet of Things - Technology and Value Added

It has been next to impossible in the past months not to come across the term ‘‘Internet of Things’’ (IoT) one way or another. Especially the past year has seen a tremendous surge of interest in the Internet of Things. Consortia have been formed to define frameworks and standards for the IoT. Companies have started to introduce numerous IoTbased products and services. And a number of IoT-related acquisitions have been making the headlines, including, e.g., the prominent takeover of Nest by Google for

Business Intelligence Maturity: Development and Evaluation of a Theoretical Model

3.2 billion and the subsequent acquisitions of Dropcam by Nest and of SmartThings by Samsung. Politicians as well as practitioners increasingly acknowledge the Internet of Things as a real business opportunity, and estimates currently suggest that the IoT could grow into a market worth

Inductive design of maturity models: applying the Rasch algorithm for design science research

7.1 trillion by 2020 (IDC 2014). While the term Internet of Things is now more and more broadly used, there is no common definition or understanding today of what the IoT actually encompasses. The origins of the term date back more than 15 years and have been attributed to the work of the Auto-ID Labs at the Massachusetts Institute of Technology (MIT) on networked radio-frequency identification (RFID) infrastructures (Atzori et al. 2010; Mattern and Floerkemeier 2010). Since then, visions for the Internet of Things have been further developed and extended beyond the scope of RFID technologies. The International Telecommunication Union (ITU) for instance now defines the Internet of Things as ‘‘a global infrastructure for the Information Society, enabling advanced services by interconnecting (physical and virtual) things based on, existing and evolving, interoperable information and communication technologies’’ (ITU 2012). At the same time, a multitude of alternative definitions has been proposed. Some of these definitions exhibit an emphasis on the things which become connected in the IoT. Other definitions focus on Internet-related aspects of the IoT, such as Internet protocols and network technology. And a third type centers on semantic challenges in the IoT relating to, e.g., the storage, search and organization of large volumes of information (Atzori et al. 2010). The fields of application for IoT technologies are as numerous as they are diverse, as IoT solutions are increasingly extending to virtually all areas of everyday. The most prominent areas of application include, e.g., the smart industry, where the development of intelligent production systems and connected production sites is often discussed under the heading of Industry 4.0. In the smart home or building area, intelligent thermostats and security systems are receiving a lot of attention, while smart energy applications focus on smart electricity, gas and water meters. Smart transport solutions include, e.g., vehicle fleet tracking and mobile ticketing, while in the smart health area, topics such as patients’ surveillance and chronic disease management are being addressed. And in the context of Accepted after one revision by Prof. Dr. Sinz.

An architecture for ad-hoc and collaborative business intelligence

In order to identify and explore the strengths and weaknesses of business intelligence (BI) initiatives, managers in charge need to assess the maturity of their BI efforts. For this, a wide range of maturity models has been developed, but these models often focus on technical details and do not address the potential value proposition of BI. Based on an extensive literature review and an empirical study, we develop and evaluate a theoretical model of impact-oriented BI maturity. Building on established IS theories, the model integrates BI deployment, BI usage, individual impact, and organizational performance. This conceptualization helps to refocus the topic of BI maturity to business needs and can be used as a theoretical foundation for future research.

Geschäftsmodelle im Internet der Dinge

Maturity models are an established means to systematically document and guide the development of organizations using archetypal capability levels. Often, these models lack a sound foundation and/or are derived on the basis of an arbitrary design method. In order to foster the design of relevant and rigorous artifacts, this paper presents a method for maturity model construction that applies the Rasch algorithm and cluster analysis as a sound methodical foundation. The Rasch algorithm is widely used to improve scholarly intelligence and attainment tests. In order to demonstrate the application of the proposed method and to evaluate its usability and applicability, we present a design exemplar in the business intelligence domain.

Method Support of Information Requirements Analysis for Analytical Information Systems

The success of organizations or business networks depends on fast and well-founded decisions taken by the relevant people in their specific area of responsibility. To enable timely and well-founded decisions, it is often necessary to perform ad-hoc analyses in a collaborative manner involving domain experts, line-of-business managers, key suppliers or customers. Current Business Intelligence (BI) solutions fail to meet the challenges of ad-hoc and collaborative decision support, slowing down and hurting organizations. The main goal of our envisioned system, which will be designed and implemented in a future research project, is to realize a highly scalable and flexible platform for collaborative, ad-hoc BI over large data sets. This will be achieved by developing methodologies, concepts and an infrastructure to enable an information self-service for business users and collaborative decision making over high-volume data sources within and across organizations.

Uncovering Four Strategies to Approach Master Data Management

ZusammenfassungUnternehmen, die heute primär in nicht-digitalen Branchen agieren, benötigen theoretisch und praktisch fundierte Hilfestellungen bei der Entwicklung und Umsetzung von Geschäftsmodellen im Internet der Dinge (Internet of Things, IoT). Durch unsere Untersuchung der Rolle des Internet in Geschäftsmodellen kommen wir zum Schluss, dass die Bedeutung des Internet in der Geschäftsmodellinnovation seit den 90er Jahren laufend zugenommen hat, dass jede Internet-Welle zu neuen digitalen Geschäftsmodellmustern geführt hat und dass die größten Umbrüche bisher in digitalen Branchen stattgefunden haben. Wir zeigen, dass digitale Geschäftsmodellmuster neu auch in der physischen Industrie relevant werden. Die Trennung von physischen und digitalen Branchen ist damit endgültig vorbei. Der Schlüssel dazu ist das IoT, das physische Produkte und digitale Services zu hybriden Lösungen verschmelzen lässt. Wir leiten eine sehr allgemein gehaltene Geschäftsmodelllogik für das IoT ab und stellen konkrete Bausteine und Muster von Geschäftsmodellen vor. Für die zentralen Herausforderungen bei der Umsetzung solcher hybriden Geschäftsmodelle zeigen wir erste Lösungsansätze auf.

Erfolgsfaktoren der Applikationsintegration

Due to specific characteristics of analytical information systems, their development varies significantly from transaction-oriented systems. Specific method support is particularly needed for requirements engineering and its information-related component, information requirements analysis. The paper at hand first evaluates the state of the art and identifies necessary method support extensions. On this basis, method support requirements for information requirements engineering are identified. The survey is structured along the five core activities of traditional requirements engineering. It reveals a need for further research especially on information requirements elicitation, validation, and management. It further contributes to a discussion of aspects that should be considered by any method support. Due to comparatively long life cycles of analytical information systems, the introduction of a process perspective is discussed in order to ensure the continuous elicitation, documentation, and management of information requirements.

Managing Process Performance to Enable Corporate Sustainability: A Capability Maturity Model

Just recently much Information Systems (IS) research focuses on master data management (MDM) which promises to increase an organizations overall core data quality. Above any doubt, however, MDM initiatives confront organizations with multi-faceted and complex challenges that call for a more strategic approach to MDM. In this paper we introduce a framework for approaching MDM projects that has been developed in the course of a design science research study. The framework distinguishes four major strategies of MDM project initiations all featuring their specific assets and drawbacks. The usefulness of our artifact is illustrated in a short case narrative.

Situational Business Intelligence Maturity Models: An Exploratory Analysis

In this paper success factors for application integration are identified. Furthermore, the factors are confirmed using structural equation modeling. Success is measured using five goal indicators: quality of process support, business satisfaction, application integration costs, architectural flexibility and time-to-market. All factors discussed — architecture management, business-IT alignment, organization of integration, integration methodology, service oriented architecture, consolidation of applications, standardization and technical quality of integration infrastructures — show positive impact on application integration success. Since no dominant factor could be identified, a balanced implementation of all success factors is recommended.