The whole of enterprises’ endeavors and behaviors cannot be coerced into models lest they inhibit their ability to navigate ill defined and shifting business environments. Enterprises immersion in digital environments is making limits all the more explicit:
- On the environment side, facts, once like manna from heaven ready to be picked and interpreted, have turned into data floods swamping all recognizable models imprints
- On the symbolic side, concepts, once steadily supported by explicit models and logic, are now emerging like new species from the Big Data primordial soup.
Typically, business analysts are taking the lead on both fronts toting learning machines and waving knowledge graphs. In between system architects have to deal with a two-pronged encroachment on information models.
- On the one hand they have to build a Chinese wall between private data and managed information to comply with regulations
- On the other hand they have to feed decision-making processes with accurate and up-to-date observations, and adjust information systems with relevant and actionable concepts.
That brings a new light on the so-called conceptual, logical, and physical “data” models as key components of enterprise architecture:
- Physical data models are meant to be directly lined up with operations and digital environments
- Logical models represent the categories managed by information systems and must be up to par with systems functional architecture
- Conceptual models are meant to represent enterprise knowledge of business domains and objectives, as well as its embodiment in organisation and people.
Logical models (information) appear therefore as an architecture hub linking business facts (data) and concepts (knowledge), ensuring exchanges between environments and representations e.g.:
- Deduction: matching observations (data) with models to produce new information, i.e. data with structure and semantics
- Induction: making hypothesises (knowledge) about the scope of models in order to make deductions
- Abduction: assessing hypothesises (knowledge) supporting inductions
- Intuition: knowledge attained directly from observation and/or experience without supporting models (information)
Taking advantage of the digital transformation, such exchanges can be turned into osmosis between systems and information architectures.
FURTHER READING
- Focus: Data vs Information
- Enterprise Architect Booklet
- Agile Architectures: Versatility meets Plasticity
- Digital Transformation & Homeostasis
- Entropy & Homeostasis
- Knowledge-based Models Transformation
- Modeling Paradigm
- Views, Models, & Architectures
- Abstraction Based Systems Engineering
- EA & MDA
- EA: The Matter of Layers
- EA: Maps & Territories
- EA: Work Units & Workflows
- Ontologies & Enterprise Architecture
- Ontologies as Productive Assets
- Models Transformation & Agile Development