Extending and Automating Maturity Models for More Value

How can maturity models be extended beyond a simple rating to deliver recommendations, prioritised actions, and a path forward — and automated to remove the friction of doing so?

These slides accompany the paper: Extending and Automating Maturity Models for More Value

Maturity models are a staple diagnostic tool in enterprise architecture and information systems — but in practice, their value is often squandered. Organisations complete an assessment, receive a score, and are left to figure out what to do next. The friction of collecting data, calculating ratings, and managing results over time further discourages repeated use. This paper tackles both problems: how to extend the model itself to provide genuine guidance, and how to automate the process so that the effort of running an assessment becomes trivial.

The paper presents a generic domain model for maturity assessment, developed and validated at Inspired.org, which supports not just scoring across multiple dimensions but also recommendations tied to each gap between maturity levels, with relative effort ratings and dependency relationships between recommended actions. An algorithm prioritises recommendations by combining score gaps, effort, and dependency order — producing a ranked, actionable improvement plan rather than a list of observations. The domain model was implemented in the EVA platform in approximately two hours, with the full online assessment flow — including Kiviat chart scoring, recommendation presentation, action selection, and Gantt chart export — delivered in under a week. APIs were subsequently added to support integration with partner systems, adding around three days of effort.

The paper concludes with a reflection on Return on Modelling Effort (ROME): working at the domain concept level, rather than writing custom application code, enabled rapid delivery, easy adaptation, and high reuse — making this a compelling case for meta model-driven, low-code approaches to enterprise tooling.

Originally published as a journal article by Graham McLeod in the EMISA Journal (Enterprise Modelling and Information Systems Architectures), with a companion presentation at the Models at Work stream, PoEM 2022.

Designing an Effective Graphical Modelling Language

How can visual modelling languages in enterprise architecture be designed to genuinely communicate meaning — rather than confusing or alienating the stakeholders they're meant to serve?

These slides accompany the paper: Designing Effective Visual Languages for Enterprise Modelling and a video of the presentation is available here: Design and Support of Modelling Languages for Effective Graphical Representation, Analysis and Communication

Graphical models are everywhere in enterprise architecture — yet a persistent gap exists between the effort invested in building them and the value they deliver. Models are too technical for business audiences, too homogenous to highlight what matters, or presented in formats that stakeholders simply cannot parse. When practitioners try to bridge this gap by converting rigorous models into PowerPoint slides or Word documents, they sever the connection to the underlying repository — destroying integrity, reusability, and currency in the process.

This paper presents the research programme Graham McLeod is pursuing at the University of Duisburg-Essen, supervised by Prof. Ulrich Frank, to address these problems at a foundational level. The research draws on human visual cognition, semiotics, information encoding theory, the Physics of Notations, and the emerging field of polymetric diagramming — a technique that modifies visual symbol properties such as size, colour, and shape to reflect underlying data, enabling pre-attentive processing and rapid identification of important patterns in large, complex models. The proposed contributions include extended theory for visual notation design, a meta-meta model supporting multiple visual languages over the same semantic model, and a layered tool architecture enabling runtime adaptation of models to purpose, audience, and medium.

For enterprise architects, this research points toward a future where modelling tools can produce representations genuinely suited to a CFO, a process owner, or a technical architect — from the same underlying repository, without manual translation.

Originally published as a doctoral consortium paper by Graham McLeod in the PoEM 2018 Doctoral Consortium Proceedings (CEUR-WS Vol. 2234), Vienna, Austria, 2018.