Christian M. Schweda

Modeling Decisions for Collaborative Enterprise Architecture Engineering

New or adapted digital business models have huge impacts on Enterprise Architectures (EA) and require them to become more agile, flexible, and adaptable. All this 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.

On Enterprise Architecture Change Events

In practice it is difficult to maintain a high quality enterprise architecture (EA) model with regards to its actuality and completeness. However, neither literature from practice and EA frameworks nor EA research literature provide sufficient guidance for the difficult task of maintaining EA models. Recently, researchers have presented methods to collect structured data from existing data sources, e.g. from IT operations in order to (semi-)automatically update EA models. In this paper, we make an argument for the additional use of EA change events from (management) information systems. These change events do not provide clearly mappable structured information, but can be used to trigger and guide manual EA model maintenance tasks when changes occur. We present the first classification of relevant events in EA literature, detailing on their sources and impact on the EA model. Finally, we propose a model maintenance workflow that is driven by events, explain an example usage case and point to open issues in the context of EA change events.

Social-Software-Based Support for Enterprise Architecture Management Processes

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.

Architectural Decision Management for Digital Transformation of Products and Services

The digitization of our society changes the way we live, work, learn, communicate, and collaborate. The Internet of Things, Enterprise Social Networks, Adaptive Case Management, Mobility systems, A ...

Leveraging Analytics for Digital Transformation of Enterprise Services and Architectures

The digital transformation of our society changes the way we live, work, learn, communicate, and collaborate. The digitization of software-intensive products and services is enabled basically by four megatrends: Cloud Computing, Big Data Mobile Systems, and Social Technologies. This disruptive change interacts with all information processes and systems that are important business enablers for the current digital transformation. The Internet of Things, Social Collaboration Systems for Adaptive Case Management, Mobility Systems and Services for Big Data in Cloud Services environments are emerging to support intelligent user-centered and social community systems. Modern enterprises see themselves confronted with an ever growing design space to engineer business models of the future as well as their IT support, respectively. The decision analytics in this field becomes increasingly complex and decision support, particularly for the development and evolution of sustainable enterprise architectures (EA), is duly needed. With the advent of intelligent user-centered and social community systems, the challenging decision processes can be supported in more flexible and intuitive ways. Tapping into these systems and techniques, the engineers and managers of the enterprise architecture become part of a viable enterprise, i.e. a resilient and continuously evolving system that develops innovative business models.

Tool Capability in Visual EAM Analytics

Enterprise Architectures (EA) consist of a multitude of architecture elements, which relate in manifold ways to each other. As the change of a single element hence impacts various other elements, mechanisms for architecture analysis are important to stakeholders. The high number of relationships aggravates architecture analysis and makes it a complex yet important task. In practice EAs are often analyzed using visualizations. This article contributes to the field of visual analytics in enterprise architecture management (EAM) by reviewing 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. We evaluate the students’ findings by comparing them with the experience of an enterprise architect.

Decision Case Management for Digital Enterprise Architectures with the Internet of Things

The Internet of Things (IoT), Enterprise Social Networks, Adaptive Case Management, Mobility systems, Analytics for Big Data, and Cloud services environments are emerging to support smart connected products and services and the digital transformation. Biological metaphors of living and adaptable ecosystems with service-oriented enterprise architectures provide the foundation for self-optimizing and resilient run-time environments for intelligent business services and related distributed information systems. We are investigating mechanisms for flexible adaptation and evolution for the next digital enterprise architecture systems in the context of the digital transformation. Our aim is to support flexibility and agile transformation for both business and related enterprise systems through adaptation and dynamical evolution of digital enterprise architectures. The present research paper investigates mechanisms for decision case management in the context of multi-perspective explorations of enterprise services and Internet of Things architectures by extending original enterprise architecture reference models with state of art elements for architectural engineering for the digitization and architectural decision support.

A situational method for semi-automated enterprise architecture documentation (SoSyM abstract)

Large organizations critically rely on their IT infrastructure. So called Enterprise Architecture (EA) models are often created to understand how the IT supports the business and used to optimize the IT and align it with the business. However, the models grow very large and are hard to keep up-to-date. Current approaches focus on automated data collection to tackle this problem, which is not feasible in many situations. In this paper we present a semi-automated EA documentation method and tool support that tackles this problem and takes the organizational contexts into account.