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Enterprise Architectures (EA) consists of many architecture elements, which stand in manifold relationships to each other. Therefore Architecture Analysis is important and very difficult for stakeholders. Due changing an architecture element has impacts on other elements different stakeholders are involved. In practice EAs are often analyzed using visualizations. This article aims at contributing to the field of visual analytics in EAM by analyzing how state of-the-art software platforms in EAM support stakeholders with respect to providing and visualizing the “right” information for decision-making tasks. We investigate the collaborative decision-making process in an experiment with master students using professional EAM tools by developing a research study and accomplishing them in a master’s level class with students.
In times of dynamic markets, enterprises have to be agile to be able to quickly react to market influences. Due to the increasing digitization of products, the enterprise IT often is affected when business models change. Enterprise Architecture Management (EAM) targets a holistic view of the enterprise’ IT and their relations to the business. However, Enterprise Architectures (EA) are complex structures consisting of many layers, artifacts and relationships between them. Thus, analyzing EA is a very complex task for stakeholders. Visualizations are common vehicles to support analysis. However, in practice visualization capabilities lack flexibility and interactivity. A solution to improve the support of stakeholders in analyzing EAs might be the application of visual analytics. Starting from a systematic literature review, this article investigates the features of visual analytics relevant for the context of EAM.
Enterprise Governance, Risk and Compliance (GRC) systems are key to managing risks threatening modern enterprises from many different angles. Key constituent to GRC systems is the definition of controls that are implemented on the different layers of an Enterprise Architecture (EA). As part of the compliance aspect of GRC, the effectiveness of these controls is assessed and reported to relevant management bodies within the enterprise. In this paper we present a metamodel which links controls to the affected elements of an EA and supplies a way of expressing associated assessment techniques and results. We complement the metamodel with an expository instantiation in a cockpit for control compliance applied in an international enterprise in the insurance industry.
New or adapted digital business models have huge impacts on Enterprise Architectures (EA) and require them to become more agile, flexible, and adaptable. All these changes are happening frequently and are currently not well documented. An EA consists of a lot of elements with manifold relationships between them. Thus changing the business model may have multiple impacts on other architectural elements. The EA engineering process deals with the development, change and optimization of architectural elements and their dependencies. Thus an EA provides a holistic view for both business and IT from the perspective of many stakeholders, which are involved in EA decision-making processes. Different stakeholders have specific concerns and are collaborating today in often unclear decision-making processes. In our research we are investigating information from collaborative decision-making processes to support stakeholders in taking current decisions. In addition we provide all information necessary to understand how and why decisions were taken. We are collecting the decision-related information automatically to minimize manual time intensive work as much as possible. The core contribution of our research extends a decisional metamodel, which links basic decisions with architectural elements and extends them with an associated decisional case context. Our aim is to support a new integral method for multi perspective and collaborative decision-making processes. We illustrate this by a practice-relevant decision-making scenario for Enterprise Architecture Engineering.
Organizations identified the opportunities of big data analytics to support the business with problem-specific insights through the exploitation of generated data. Sociotechnical solutions are developed in big data projects to reach competitive advantage. Although these projects are aligned to specific business needs, common architectural challenges are not addressed in a comprehensive manner. Enterprise architecture management is a holistic approach to tackle complex business and IT architectures. The transformation of an organization’s EA is influenced by big data transformation processes and their data-driven approach on all layers. In this paper, we review big data literature to analyze which requirements for the EA management discipline are proposed. Based on a systematic literature identification, conceptual categories of requirements for EA management are elicited utilizing an inductive category formation. These conceptual categories of requirements constitute a category system that facilitates a new perspective on EA management and fosters the innovation-driven evolution of the EA management.
discipline.
Enterprises and information societies confront crucial challenges currently, while Industry 4.0 becomes important in the global manufacturing industry and Society 5.0 should contribute to a supersmart society, especially for healthcare. Physical activity monitoring digital platforms are architected to improve the healthcare status of patients with diabetes and other lifestyle-related diseases. Furthermore, digital platforms are expected to generate profits for health technology companies and help control costs in the healthcare ecosystem. However, current digital enterprise architecture approaches are not well-established, and the potentials have not yet been realized. Design thinking approach and agile software development methodologies can overcome these limitations, beginning with proof of concept and pilot projects and then scaling to the production environment. In this paper, we describe how that the adaptive integrated digital architecture framework (AIDAF) for Design Thinking approach is proposed and verified in a case of a university hospital in the Americas. In addition, challenges and future activities for this area are discussed that cover the directions for Society 5.0.
New business concepts such as Enterprise 2.0 foster the use of social software in enterprises. Especially social production significantly increases the amount of data in the context of business processes. Unfortunately, these data are still an unearthed treasure in many enterprises. Due to advances in data processing such as Big Data, the exploitation of context data becomes feasible. To provide a foundation for the methodical exploitation of context data, this paper introduces a classification, based on two classes, intrinsic and extrinsic data.
Preface of IDEA 2015
(2016)
Neue Modelle für digitale Unternehmensarchitekturen mit Big Data, Services & Cloud Computing, mobilen Systemen, Internet of Things sowie Industrie 4.0 Ökosystemen machen eine enge Kooperation verschiedener Partner aus Wissenschaft, Anwendungsunternehmen, öffentlichen Organisationen, Softwarehersteller und IT- Dienstleister notwendig. Ziel dieser Zusammenarbeit ist die Zusammenführung neuer Konzepte und Möglichkeiten der Informationstechnologie zur bestmöglichen Unterstützung sich verändernder Unternehmensziele und -strategien. Software- und Unternehmensarchitekturen spielen hierbei eine zentrale Rolle. So werden Anforderungen bezüglich Flexibilität und Agilität in digitalen Unternehmen wesentlich durch serviceorientierte Ansätze unterstützt. Der Ordnungsgrad und die kosteneffiziente Gestaltung komplexer IT-Landschaften soll durch Digital Enterprise Architecture Management deutlich verbessert werden – passend zu neuen Möglichkeiten von Services & Cloud Computing, Big Data, sowie kollaborativen Geschäftsprozessen.
The advent of chatbots in customer service solutions received increasing attention by research and practice throughout the last years. However, the relevant dimensions and features for service quality and service performance for chatbots remain quite unclear. Therefore, this research develops and tests a conceptual model for customer service quality and customer service performance in the context of chatbots. Additionally, the impact of the developed service dimensions on different customer relationship metrics is measured across different service channels (hotline versus chatbots). Findings of six independent studies indicate a strong main effect of the conceptualized service dimensions on customer satisfaction, service costs, intention to service reusage, word-of-mouth, and customer loyalty. However, different service dimensions are relevant for chatbots compared to a traditional service hotline.
Digitalization of products and services commonly causes substantial changes in business models, operations, organization structures and IT infrastructures of enterprises. Motivated by experiences and observations from digitalization projects, the paper investigates the effects of digitalization on enterprise architectures (EA). EA models serve as representation of business, information system and technical aspects of an enterprise to support management and development. By comparing EA models before and after digitalization, the paper analyzes the kinds of changes visible in the EA model. The most important finding is that newly created digitized products and the associated (product)- and enterprise architecture are no longer properly integrated into the overall architecture and even exist in parallel. Thus, the focus of this work is on showing these parallel architectures and proposing derivations for a better integration.
Digital technologies are main strategic drivers for digitalization and offer ubiquitous data availability, unlimited connectivity, and massive processing power for a fundamentally changing business. This leads to the development and application of intelligent digital systems. The current state of research and practice of architecting digital systems and services lacks a solid methodological foundation that fully accommodates all requirements linked to efficient and effective development of digital systems in organizations. Research presented in this paper addresses the question, how management of complexity in digital systems and architectures can be supported from a methodological perspective. In this context, the current focus is on a better understanding of the causes of increased complexity and requirements to methodological support. For this purpose, we take an enterprise architecture perspective, i.e. how the introduction of digital systems affects the complexity of EA. Two industrial case studies and a systematic literature analysis result in the proposal of an extended Digital Enterprise Architecture Cube as framework for future methodical support.
Assistant platforms are becoming a key element for the business model of many companies. They have evolved from assistance systems that provide support when using information (or other) systems to platforms in their own. Alexa, Cortana or Siri may be used with literally thousands of services. From this background, this paper develops the notion of assistant platforms and elaborates a conceptual model that supports businesses in developing appropriate strategies. The model consists of three main building blocks, an architecture that depicts the components as well as the possible layers of an assistant platform, the mechanism that determines the value creation on assistant platforms, and the ecosystem with its network effects, which emerge from the multi-sided nature of assistant platforms. The model has been derived from a literature review and is illustrated with examples of existing assistant platforms. Its main purpose is to advance the understanding of assistant platforms and to trigger future research.
Digital assistants like Alexa, Google Assistant or Siri have seen a large adoption over the past years. Using artificial intelligence (AI) technologies, they provide a vocal interface to physical devices as well as to digital services and have spurred an entire new ecosystem. This comprises the big tech companies themselves, but also a strongly growing community of developers that make these functionalities available via digital platforms. At present, only few research is available to understand the structure and the value creation logic of these AI-based assistant platforms and their ecosystem. This research adopts ecosystem intelligence to shed light on their structure and dynamics. It combines existing data collection methods with an automated approach that proves useful in deriving a network-based conceptual model of Amazon’s Alexa assistant platform and ecosystem. It shows that skills are a key unit of modularity in this ecosystem, which is linked to other elements such as service, data, and money flows. It also suggests that the topology of the Alexa ecosystem may be described using the criteria reflexivity, symmetry, variance, strength, and centrality of the skill coactivations. Finally, it identifies three ways to create and capture value on AI-based assistant platforms. Surprisingly only a few skills use a transactional business model by selling services and goods but many skills are complementary and provide information, configuration, and control services for other skill provider products and services. These findings provide new insights into the highly relevant ecosystems of AI-based assistant platforms, which might serve enterprises in developing their strategies in these ecosystems. They might also pave the way to a faster, data-driven approach for ecosystem intelligence.
Platforms feature increasingly complex architectures with regard to interconnecting with other digital platforms as well as with a variety of devices and services. This development also impacts the structure of digital platform ecosystems and forces providers of these services, devices, and services to incorporate this complexity in their decision-making. To contribute to the existing body of knowledge on measuring ecosystem complexity, the present research proposes two key artefacts based on ecosystem intelligence: On the one hand, complementarity graphs represent ecosystems with an ecosystem's functional modules as vertices and complementarities as edges. The nodes carry information about the category membership of the module. On the other hand, a process is suggested that can collect important information for ecosystem intelligence using proxies and web scraping. Our approach allows replacing data, which today is largely unavailable due to competitive reasons. We demonstrated the use of the artefacts in category-oriented complementarity maps that aggregate the information from complementarity graphs and support decision-making. They show which combination of module categories creates strong and weak complementarities. The paper evaluates complementarity maps and the data collection process by creating category-oriented complementarity graphs on the Alexa skill ecosystem and concludes with a call to pursue more research based on functional ecosystem intelligence.
Potentials of smart contracts-based disintermediation in additive manufacturing supply chains
(2019)
We investigate which potentials are created by using smart contracts for disintermediation in supply chains for additive manufacturing. Using a qualitative, critical realist research approach, we analyzed three case studies with companies active in additive manufactures. Based on interviews with experts from these companies, we could identify eight key requirements for disintermediation and associate four potentials of smart contracts-based disintermediation.
Digitization transforms business process models and processes in many enterprises. However, many of them need guidance, how digitization is impacting the design of their information systems. Therefore, this paper investigates the influence of digitization on information system design. We apply a two-phase research method applying a literature review and an exploratory case study. The case study took place in the IT service provider of a large insurance enterprise. The study’s results suggest that a number of areas of information system design are affected, such as architecture, processes, data and services.