An organization’s ability to learn and translate that learning into action is the ultimate competitive advantage.
Jack Welch, Former CEO, General Electric
Spinoza Car (Thomas Hirschhorn)
The spreading of Machine learning (ML) technologies and the immersion of enterprises in digital environments open the door to learning organizations. The question is how it can be achieved.
Taking a leaf from Spinoza, enterprises can expand their knowledge (or learn) in three ways:
- Through senses, typically by applying data- and process mining to facts observed at the digital level.
- Through reasoning, by applying knowledge graphs to the information managed by systems.
- Through judgment, as carried out by people and organizations putting information to use as knowledge.
That tripartite understanding of learning can be aligned with the distinction between data, information, and knowledge, as well as with the one between operational, tactical, and strategic decision-making.
Learning & the Knowledge Loop
While the distinction between data, information, and knowledge remains a debated issue, two decisive factors are forcing a clarification:
- The immersion of enterprises in digital environments and the tumbling down of traditional fences between systems and social networks have given rise to a wide range of data privacy regulations, forcing enterprises to separate anonymous data from managed information.
- Ubiquitous advances of ML technologies have induced the emergence of new business models focused on data (e.g., data factories) or knowledge (e.g., knowledge graphs).
It ensues that enterprises have to conciliate the distinctions imposed by business and technical environments, with the necessary integration of observation (data), reasoning (information), and judgment (knowledge).
At the same time, that integration implies more transparency and traceability with regard to processing (e.g., truth-preserving or statistical), resources (e.g., external or internal), and objectives (e.g., operational or strategic).
That can only be achieved with ontologies.
Languages & Actionable Ontologies
The contents of ontologies can be formally defined in terms of language, models and thesaurus:
- Thesauruses define the meaning of terms, categories, and concepts.
- Models add syntax to build representations of contexts and concerns.
- Ontologies add pragmatics in order to put models in broader perspectives.
Languages can then be used to make ontologies actionable by gearing their contents to enterprise architectures (EA):
- Data is managed at the EA digital level, typically through relational data bases. Technologies like Data mesh and Semantic networks are used to bridge the gap with thesauruses and conceptual models.
- Information is managed at the EA system level through modeling languages. Conceptual graphs are used to bridge the gap with ontologies and Knowledge graphs.
- Knowledge is managed at the EA business level through ontologies. Knowledge graphs are used to weave together domain semantics and business pragmatics.
The primary benefit of ontologies is to ensure a differentiated yet seamless integration of the different kinds of contexts and concerns: physical environment, systems, business environment.
With languages providing an actionable basis, ontologies can be extended with reasoning capabilities.
Learning & Decision-making
As far as enterprises are concerned, knowledge is all about decision-making. Based on the OODA (Observation, Orientation, Decision, Action) decision-making paradigm developed by John Boyd, it means:
- Observation: understanding the nature, origin, and time-frame of changes in business environments (aka territories).
- Orientation: assessment of the reliability and shelf-life of pertaining information (aka maps) with regard to enterprise’s stakes and current positions and operations.
- Decision: weighting of options with regard to stakes and capabilities.
- Action: carrying out of decisions according to stakes and time-frames.
For organizations, the first step is to integrate decision-making with knowledge architecture and business intelligence through EA maps and territories.
The next step is to characterize decisions with regard to supporting knowledge:
- Observation: data can be obtained from digital or business environments. It can be analysed and turned into knowledge (business intelligence) or directly matched with models of managed information.
- Orientation: reasoning (information) is applied to observations (data) and judgment (knowledge).
- Decision: judgment (knowledge) is applied to observations (data) and causal chains (information).
- Action: experience comes from operations carried out in symbolic and physical environment.
Using ontologies to integrate decision-making and knowledge management at the enterprise level paves the way to collective learning.
Learning & Organization
The focus put on Artificial intelligence and Machine learning has blurred the importance of communication for human intelligence. For organizations it means collaboration and collective knowledge; for enterprises, two aspects of intelligence as a social capability are especially significant:
With regard to behavior, emotional intelligence (usually defined in terms of motivation, empathy, and social skills) can significantly enhance people’s ability to learn and handle issues. The ways emotional intelligence translate into collective accomplishments may be perplexing, but the results are not in doubt: enterprise successes are always backed by strong corporate identity and culture.
With regard to outcome, creativity and innovation give enterprises their competitive edge. If anything, they give name to the puzzling alchemy that turns individual initiatives into collective momentum. Both aspects are contingent on active collaboration fed with renewed ideas and assumptions, opening new perspectives, and providing the momentum driving endogenous transformations.
To be effective and thrive through organizations, that momentum must be set across their two key junctures:
- Collaboration must weave together the reasoning and decision-making of individual and collective agents
- Communication must rely on a seamless processing of implicit (emerging) and explicit (planned) knowledge
That can be achieved through the integration of implicit to explicit knowledge across individual and collective levels:
- Between people and organizations, it’s typically done through a mix of experience and collaboration (a)
- Between systems and representations, it’s the nuts and bolts of Machine learning and Knowledge graphs technologies (b)
- Between people and systems, learning relies on the experience feedback achieved through the integration of ML into the OODA loop (c)
- Between organization and systems, learning relies on the functional distinction between judgment, to be carried out at the organizational level, and observation and reasoning, supported by systems (d)
That integration of individual and collective knowledge will ensure the accountability (organization) and traceability (systems) of decision-making and reasoning processes.
Organization+AI= Collective Intelligence
The AI revolution has already broken all historical records of footprint (everything is affected) and speed (a matter of years). Given the length of human education cycles, appraising the consequences comes with some urgency, beginning with the disposal of two entrenched beliefs:
- Knowledge should not be dealt with as a single and homogeneous corpus: by focusing on collective knowledge and decision-making humans can take the lead on cloned brains, whatever their learning capability.
- Cognitive and manual skills should not be considered separately: by combining actual experience, cooperation, and knowledge nurture, humans can remain the primary source of innovation.
Instead, enterprises should try to mimic the nervous system of octopuses, with each unit getting its brain and neurons, and so its own touch of knowledge and taste of decision-making. To that effect enterprises must develop and integrate three key symbolic capabilities: communication, representation, and imagination:
- Communication: thesauruses are used to ensure the continuity and consistency of terms and to map facts (business environment) to concepts (business models)
- Representation: modeling languages are used to map relevant facts to information managed by enterprise
- Imagination: knowledge graphs are used to associate actual categories to virtual representations, alternative of futures
Such a collective intelligence could further the understanding of Artificial ‘General’ Intelligence (AGI) in terms of motifs and motives:
- Motifs: intelligence rooted to operations (facts), organization (concepts) and systems (categories)
- Motives: intelligence driven by business processes (communication), information management (representation), and planning and decision-making (imagination)
Implicit knowledge and intuition would be rooted in data and process mining, and weaved into the fabric of consciousness built from a continuous adjustment of managed (actual) and planned (virtual) realities.
- Caminao Framework Overview
- Knowledge Management Booklet
- Edges of Knowledge
- The Pagoda Playbook
- ABC of EA: Agile, Brainy, Competitive
- EA in bOwls (Overview)
- Knowledge-driven Decision-making (1)
- Models & Meta-models
- Ontologies & Enterprise Architecture
- Abstraction Based Systems Engineering
- EA & MDA
- EA: The Matter of Layers
- EA: Maps & Territories
- EA: Work Units & Workflows