Wardley Maps offer a powerful way to analyse a business's competitive landscape, value chain, and the evolution of its components — but most Enterprise Architecture frameworks have not formally incorporated them. This paper examines how Wardley Mapping complements EA methods, proposes a meta model integration using the Inspired Holistic Architecture Language (HAL), and demonstrates practical benefits including reduced effort, improved model fidelity, and richer strategic insight. A useful read for enterprise architects and business architects looking to bring greater contextual awareness into their architecture practice.
Managing Large-Scale Collaborative Modelling: Meta Model Extensions for Enterprise Architecture Tools
As enterprise architecture initiatives grow to span multiple teams, geographies, and time zones, the repositories and tools supporting them face real challenges: information overload, ownership conflicts, version management, and the need to present different views to different user communities. This paper formalises a set of meta model and meta-meta model extensions — including context, domains, filters, versioning, and scenarios — developed through real-world deployment of a collaborative EA modelling platform. The result is a more manageable, flexible, and scalable foundation for large-scale collaborative architecture work.
Cooking up a MEAL: Creating a Meta Enterprise Architecture Language
Enterprise architecture tools struggle to interoperate, and existing exchange standards like XMI are notoriously unreliable in practice. This paper proposes MEAL — a Meta Enterprise Architecture Language — a human-readable, domain-specific language designed to define, populate, query, and analyse EA models and repositories, and to serve as a high-level API between tools. It presents the requirements, a prototype implementation in Smalltalk, and example syntax demonstrating the concept's practical promise.
Integrated Meta Model for Enterprise Modelling including Strategy, Business Architecture, Risk and Change
Abstract
The paper describes the development of an integrated meta model capable of supporting a variety of approaches in strategy and business architecture, including TOGAF®, ArchiMate®, Zachman, MEMO, Inspired and others. It describes the sources of concepts, relationships and properties; the modelling approach and rationale and the resultant model, which has proven effective in support of multiple business transformation projects. The model integrates strategy, contextual factors and business architecture elements as well as interfacing to implementation architectures, enterprise risk and programme management. It leverages a multi-level meta modelling approach to overcome challenges of prior meta models. Advantages and challenges related to a large integrated model are discussed and suggestions made for dealing with these challenges.
Published in
Enterprise Design and Engineering / Practice of Enterprise Modeling Forum, Vienna, Austria
?Facilitating Design and Use of Effective Visual Languages in Enterprise Modelling and Information Systems
Enterprise modelling and information systems work often relies heavily on graphical models expressed in visual languages to concisely capture, rigorously model and effectively convey meaning between stakeholders. Recent research has highlighted problems with the effectiveness of popular modelling notations. A physics of notations (PoN) was proposed to address these issues. Application of the PoN has not proven routinely successful. Models are often constructed by experts, but must be well received by non-experts to achieve their goals. This research contends that recent information from the fields of cognition, visualisation and graphic design can be exploited to enhance the return on modelling effort (ROME) and the value of models. Improved meta models, methods for visual language design and enhanced tools can support the definition and use of effective visual languages and the application of the PoN and derivatives.
Published in
?Practice of Enterprise Modelling (PoEM), Doctoral Consortium Papers
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