[Enterprise architects] “…are like sailors who have to rebuild their ship on the open sea, without ever being able to dismantle it in dry dock and reconstruct it from the best components.”
Otto Neurath
Preamble
Beyond labels (layers, levels, views, tiers, …) and perspectives (business, data, applications, technologies, …), there is a broad consensus around a ternary divide of enterprise architectures. That divide can also be aligned with the traditional one between models: conceptual and organisational, logical and functional, and physical and operational.
A Shift in Paradigm
The immersion of enterprises in digital environments and the spreading of AI technologies call for a change of paradigm that could take into account the difference between data (facts), managed information (categories), and knowledge (concepts).
Such a shift can be achieved by a change of perspective from traditional layered pyramids to faceted prisms.
Ontological Prisms & Abstractions
Ontological prisms represent modeling realms for extensions, intensions, and designs; abstraction semantics can be define accordingly.
Homogeneous abstractions are set within the same symbolic realms and thus pertain to the same kind of representations:
- Extensional (data models): sets and subsets of facts characterized by variant features
- Intensional (business models): semantic networks representing concepts relationships
- Design (systems models): structural and functional inheritance between categories
Heterogeneous abstractions (or conversely realisations) are set across symbolic realms:
- Thesaurus, between terms and concepts
- Taxonomies, between facts and categories
- Ontologies, between categories and concepts
That abstraction framework enables a principled mapping of templates.
Ontological Prisms & Enterprise Architecture
Ontological prisms ensure the interoperability of EA representations:
- A separate yet integrated management of symbolic resources (data), assets (information), and services (knowledge)
- A symbolic anchoring of enterprise architectures with their environments
- The integration and interoperability of traditional and AI technologies
That integration of symbolic (blueprints) and actual (architecture) artefacts would pave the way to actionable architectures befitting Otto Neurath’s appraisal quoted above. To that effect symbolic prisms must enable a functional integration of basic EA use cases:
- Requirements (managed facts)
- Data analytics (environment facts)
- Business analysis (facts/concepts)
- Business intelligence (concepts)
- Strategic planning (concepts/categories)
- Systems engineering (facts/categories)
- Systems modeling (categories)
The structural and functional integration of symbolic prisms can be further developed into digital twins achieving osmosis and homeostasis.
From Symbolic Twins to “Brain of the Firm”
Immersed in digital environments, EA symbolic prisms can be turned into digital twins:
- Ontologies ensure a comprehensive and consistent symbolic representation of the enterprise architectures’ symbolic assets
- Gears are provided by organisation (knowledge/information systems), operations (information systems/data), and processes (knowledge/data)
- Osmosis can be achieved with Machine learning employed between facts and business models (data mining), and between facts and systems (process mining)
- Homeostasis can be achieved through Knowledge graphs bringing together business models and enterprise governance
That combination of sensory-motor systems immersed in digital environments on the one hand, a collective intelligence with reasoning, judgment, and learning capabilities on the other hand, mirrors the three fundamental cognitive capabilities:
- Understanding of contexts and opportunities (business intelligence)
- Symbolic representation of relevant aspects of contexts and opportunities (system modeling)
- Symbolic representation of virtual aspects of contexts and opportunities (planning)
That gives a new relevance to Stafford Beer’s vision of a “Brain of the Firm”.
FURTHER READING
KALEIDOSCOPE SERIES
- Signs & Symbols
- Generative & General Artificial Intelligence
- Thesauruses, Taxonomies, Ontologies
- EA Engineering interfaces
- Ontologies Use cases
- Complexity
- Cognitive Capabilities
- LLMs & the matter of transparency
- LLMs & the matter of regulations
- Learning