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Current approaches for enterprise architecture lack analytical instruments for cyclic evaluations of business and system architectures in real business enterprise system environments. This impedes the broad use of enterprise architecture methodologies. Furthermore, the permanent evolution of systems desynchronizes quickly model representation and reality. Therefore we are introducing an approach for complementing the existing top-down approach for the creation of enterprise architecture with a bottom approach. Enterprise Architecture Analytics uses the architectural information contained in many infrastructures to provide architectural information. By applying Big Data technologies it is possible to exploit this information and to create architectural information. That means, Enterprise Architectures may be discovered, analyzed and optimized using analytics. The increased availability of architectural data also improves the possibilities to verify the compliance of Enterprise Architectures. Architectural decisions are linked to clustered architecture artifacts and categories according to a holistic EAM Reference Architecture with specific architecture metamodels. A special suited EAM Maturity Framework provides the base for systematic and analytics supported assessments of architecture capabilities.
A new class of information system architecture, decision-oriented service systems, is spreading more and more. Decision-oriented service systems provide services that support decisions in business processes and products based on the capabilities of cloud-computing environments. To pave the way for the creation of design methods of business processes and products based on decision-oriented service systems, this article introduces a capability-oriented approach. Starting from technological capabilities, more abstract operational and dynamic capabilities are created. The framework created is based on an integrated conceptualization of decision-oriented service systems that allows capturing synergetic effects. By creating the framework, the gap between the technological capabilities of technologies and the strategic goals of enterprises shall be narrowed.
An enormous amount of data in the context of business processes is stored as images. They contain valuable information for business process management. Up to now this data had to be integrated manually into the business process. By advances of capturing it is possible to extract information from an increasing number of images. Therefore, we systematically investigate the potentials of Image Mining for business process management by a literature research and an in-depth analysis of the business process lifecycle. As a first step to evaluate our research, we developed a prototype for recovering process model information from drawings using Rapidminer.
AI technologies such as deep learning provide promising advances in many areas. Using these technologies, enterprises and organizations implement new business models and capabilities. In the beginning, AI-technologies have been deployed in an experimental environment. AI-based applications have been created in an ad-hoc manner and without methodological guidance or engineering approach. Due to the increasing importance of AI-technologies, however, a more structured approach is necessary that enable the methodological engineering of AI-based applications. Therefore, we develop in this paper first steps towards methodological engineering of AI-based applications. First, we identify some important differences between the technological foundations of AI- technologies, in particular deep learning, and traditional information technologies. Then we create a framework that enables to engineer AI-applications using four steps: identification of an AI-application type, sub-type identification, lifecycle phase, and definition of details. The introduced framework considers that AI-applications use an inductive approach to infer knowledge from huge collections and streams of data. It not only enables the rapid development of AI-application but also the efficient sharing of knowledge on AI-applications.
Modern enterprises reshape and transform continuously by a multitude of management processes with different perspectives. They range from business process management to IT service management and the management of the information systems. Enterprise Architecture (EA) management seeks to provide such a perspective and to align the diverse management perspectives. Therefore, EA management cannot rely on hierarchic - in a tayloristic manner designed - management processes to achieve and promote this alignment. It, conversely, has to apply bottom-up, information-centered coordination mechanisms to ensure that different management processes are aligned with each other and enterprise strategy. Social software provides such a bottom-up mechanism for providing support within EAM-processes. Consequently, challenges of EA management processes are investigated, and contributions of social software presented. A cockpit provides interactive functions and visualization methods to cope with this complexity and enable the practical use of social software in enterprise architecture management processes.
Artificial Intelligence enables innovative applications, and applications based on Artificial Intelligence are increasingly important for all aspects of the Digital Economy. However, the question of how AI resources such as tools and data can be linked to provide an AI-capability and create business value is still open. Therefore, this paper identifies the value-creating mechanisms of connectionist artificial intelligence using a capability-oriented view and points out the connections to different kinds of business value. The analysis supports an agenda that identifies areas that need further research to understand the mechanism of value creation in connectionist artificial intelligence.
Digitization is more than using digital technologies to transfer data and perform computations and tasks. Digitization embraces disruptive effects of digital technologies on economy and society. To capture these effects, two perspectives are introduced, the product and the value-creation perspective. In the product perspective, digitization enables the transition from material, static products to interactive and configurable services. In the value-creation perspective, digitization facilitates the transition from centralized, isolated models of value creation, to bidirectional, co-creation oriented approaches of value creation.
The Dow Jones Sustainability Indexes (DJSI) track the performance of companies that lead in corporate sustainability in their respective sectors or in the geographies they operate. The Sustainable Asset Management (SAM) Indexes GmbH publishes and markets the indexes, the so-called Dow Jones Sustainability Indexes in collaboration with SAM. All indexes of the DJSI family are assessed according to SAM’s Corporate Sustainability AssessmentTM methodology.
Seit einigen Jahren befinden sich das globale ökonomische System, dessen Märkte und Organisationen in einem dynamischen und komplexen Veränderungsprozess; in diesem Zusammenhang ist der Begriff der Globalisierung eines der meist zitierten Schlagworte. Die Globalisierung der Märkte spiegelt sich in der Ausweitung, Intensivierung und grenzüberschreitenden Integration wirtschaftlicher Transaktionen in einem zuvor nie gekannten Ausmaß wider. Zu den Kennzeichen der ökonomischen Globalisierung gehören die internationalen Finanzmärkte und die Schaffung weltumspannender Wertschöpfungsketten durch eine Zunahme von Kooperationen transnational agierender Konzerne, den so genannten global players. Die Verflechtungen innerhalb der Finanzbranche und die Entstehung weltweiter Wertschöpfungsketten werfen eine Reihe moralisch sensibler Fragen hinsichtlich der Verantwortung von Unternehmen in der globalisierten Wirtschaftswelt auf. Ein Blick in die Schlagzeilen am Anfang des Jahrtausends lässt die Vielzahl der Konfliktfelder erahnen: Kollabierende Finanzkonstrukte von Investmentbanken, Schmiergeldzahlungen zur Sicherung von Aufträgen, nicht rechtmäßige Überprüfung von Mitarbeiterdaten sowie Skandale um Kinder- oder Sklavenarbeit in Ziegeleien und Kohlegruben sind nur ein kleiner Ausschnitt dieser Berichterstattungen. In der öffentlichen Wahrnehmung stehen die Reputation und Glaubwürdigkeit ganzer Branchen auf dem Spiel. Vertrauen, das über viele Jahre aufgebaut wurde, wird so binnen kürzester Zeit zerstört.
Unternehmen, die gesellschaftliche Verantwortung für die Auswirkungen ihrer Geschäftstätigkeit übernehmen, stehen vor der Herausforderung, CSR und Compliance systematisch und umfassend in ihr Unternehmen zu integrieren und kontinuierlich umzusetzen. Dieser Beitrag zeigt vor diesem Hintergrund auf, wie Unternehmen durch die Etablierung von Wertemanagementsystemen sowie PDCA-Zyklen (Plan-Do-Check-Act) CSR- und Compliancemanagementsysteme integrieren und umsetzen können. Des Weiteren werden Instrumente, die relevant für die Entwicklung, Implementierung und Umsetzung von effektiven und effizienten CSR- und Compliancemanagementsysteme sind, aufgelistet.