The book will be released May 25, with preorders for early birds on:
Generally speaking, architecture can be understood both as an activity (what architects do) and its outcome (what architects build). On that account there is a frustrating imbalance between enterprise architecture (EA) as a set of practices and as a discipline; this book is an attempt to bridge the gap by establishing the discipline on principled foundations.
To that end the book is defined by three premises:
- Enterprise and systems architectures must be set apart
- Outcome: enterprise architectures are a mix of symbolic representations, organization, and computer systems
- Activity: enterprise architecture is by nature a continuous undertaking because change is the rule of the game for enterprises, especially for ones immersed in digital environments.
The book is then built on four pillars which, if not universally accepted, are commonly understood:
- A modeling paradigm based on the Stanford Symbolic System Program (SSP) which defines computer systems as containers managing the symbolic counterparts (aka surrogates) of sets of actual or symbolic objects and activities deemed relevant.
- The Pagoda blueprint, an extension of the Zachman’s framework, is used to chart EA’s mix of symbolic, organizational, and IT components
- Agile principles, use cases, and model-based system engineering, are meant to ensure some continuity with established practices of systems engineering
- Ontologies and knowledge graphs, for an all-inclusive representation of data, information, and knowledge.
All in all, the aim is to consolidate established specific schemes into an ecumenical, comprehensive, and formally consistent framework.
The book is divided into five parts: enterprises and systems, objectives and frameworks, representations, engineering, and enterprises as viable organisms.
Part I outlines the basics of EA modeling:
- Chapter 1 establishes the foundations in terms of maps (or blueprints) and territories (environments, systems, processes).
- Chapter 2 introduces the basics of modeling languages (syntax and semantics) and symbolic representation (objects and behaviors, anchors and aspects, surrogates).
- Chapter 3 applies the modeling paradigm to EA models (descriptive, prescriptive, technical) and engineering (workshops and decision-making).
Part II sets forth the ends and means of EA as a discipline:
- Chapter 4 makes the case for a distinction between business and systems perspectives; the former focused on value chains, and the latter, on architecture capabilities.
- Chapter 5 expounds the benefits of frameworks for mapping architectures and managing changes, and sets guidelines for selecting the right one.
- Chapter 6 is a detailed presentation of the Pagoda blueprint, revisiting the Zachman framework.
Part III deals with the all-inclusive representation of data (environments), information (systems), and knowledge (enterprise):
- Chapter 7 expounds on the modeling principles introduced in chapter 3, and specifically on the use of anchors to attach the prescriptive models of systems to the descriptive models of business environments and objectives.
- Chapter 8 considers the pros and cons of patterns, profiles, and meta-models, and the benefits of ontologies.
- Chapter 9 examines how ontologies and Knowledge graphs can be used to turn the representations of environments, organization, and systems into actionable maps that weave data, information, and knowledge with enterprise organization and systems architectures.
- Chapter 10 considers the benefits for decision-making of the integration of the systems and representations discussed in chapter 9.
Part IV deals with engineering and the transformation of enterprise architectures:
- Chapter 11 takes a bird’s-eye view of requirements and puts their taxonomy in an EA perspective.
- Chapter 12 sets guidelines for the refactoring of requirements along EA concerns, with a focus on digital transformation.
- Chapter 13 considers the management of EA engineering projects, with a focus on Use cases as modeling interfaces between business and enterprise objectives, on the one hand, and user-driven and architecture-based developments, on the other hand.
- Chapter 14 puts Model-based systems engineering (MBSE) at the hub of enterprise architecture transformations. At the systems level, an augmented backlog mechanism ensures the dynamic integration of business-driven (Agile) and architecture-based (MBSE) developments. At the enterprise level, the morphing of augmented backlogs into Knowledge graphs ensures the conceptual integration of engineering with business strategies and enterprise modernization.
Part V puts enterprise sustainability in the broader perspective of cybernetics, Artificial intelligence, and Machine learning:
- Chapter 15 considers architectures’ agility in terms of versatility and plasticity. For enterprises competing in digital environments, it means that sustainability depends on their ability to make the most of the flux of information; in other words, to limit entropy. That understanding is used to revisit Capacity maturity model integration (CMMI).
- Chapter 16 takes a strategic view on the impact of Artificial intelligence and Machine-learning technologies at the systems and enterprise levels. The focus is put on organizational behavior, innovation, and their impact on intangible assets.
- Chapter 17 concludes with the issue of enterprises’ resilience to systemic changes and EA significance with regard to externalities.
- A focus on enterprise architecture as a reasoned discipline
- A shift from the system modeling paradigm as to encompass business environments and enterprises’ objectives and organization.
- A focus on Knowledge based EA engineering supported by a formal ontological thesaurus available online
- Key concepts illustrated with original artwork (Albert)
- A formal yet concrete modeling an engineering perspective
- Integration of established approaches (e.g., Agile, Use cases, Model-based systems engineering, Capacity maturity model integration), and AI technologies, typically Knowledge graphs and Deep learning
Two Companion series will be available online, focused on the use of the Caminao ontology kernel to implement the principles developed in the book:
- The book Companion series is meant to provides footnotes directly aligned with the book chapters.
- The case study Companion series puts functional topics in a workshops perspective, with a focus on ontological representations. The series is meant to be used independently of the book.
A first version of the Caminao kernel (CaKz) can be consulted online on the OWL 2 Protégé website with the link CaKe_WIP.