How are Data Models like Metaphors?

In classical linguistics a metaphor is a figure of speech that explains one thing in terms of another. The cognitive linguist George Lakoff revolutionized contemporary linguistics when he proposed that metaphor is “an ontological mapping across conceptual domains. Metaphor is not just a matter of language, but of thought and reason. The language is secondary; the mapping is primary.” Lakoff (1980)

Data models are like metaphors because they map one domain onto another. In fact, the logical data model is mapped in two directions.

  1. We use the logical model going downward to design the physical database so that it can record significant facts about the operations of the enterprise. In this direction the data model is a metaphor for the physical model and database.
  2. We use the logical model going upward to map the complex ideas implicit in the logical model onto a simpler conceptual model. The conceptual model can then be used for system integration by helping to understand how one application or business area fits into the larger enterprise.

A model is a visual metaphor. Instead of using figurative language to explain one thing in terms of another it uses a combination of visual shapes, colors, captions, and relationship symbols to map data requirements onto the physical or conceptual domains.

It may sound strange to talk about visual metaphors but that’s only because we are still thinking of the classical definition of metaphor as a part of the figurative language of poetry and literature. In order to see just how common visual metaphors have become one need only look at examples from commercial advertising. Visual metaphors have become so common that the conceptual mapping is almost subconscious.

Consider the Heinz Ketchup Advertisement. The image of a ketchup bottle sliced up like a tomato clearly expresses the message that Heinz ketchup is as fresh as a tomato.

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AI Governance can Learn from Data Governance

Viewing all of the ethical and existential risks associated with Artificial Intelligence we should encourage AI researchers and developers to develop frameworks for implementing AI Governance in their respective organizations. AI Governance can learn much from the Data Governance programs in IT. Data Governance has been around since the 90’s and has mature processes and tools.

Each organization should establish an AI Governance Steering committee consisting of director level managers who set direction and review high level progress. The AI Governance Council would be responsible for making operational decisions regarding the AI product and for initiating projects that touch upon ethical issues.

Organizations should conduct an AI maturity assessment to determine the level of ethical AI they have achieved.  Some of the AI Governance processes should recur, like validating model performance and reviewing emergent responses. The list of tools used to govern AI will be specific to each organization. IBM’s WatsonX.governance platform is one example.

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Comparing Data Governance to Information Governance

First let’s ask what’s the difference between data and information? Information is data in context so it can have meaning. The context may be a business process or sales opportunity but information implies an agent that is getting informed. Data doesn’t need context to exist. Data may be at rest in a data store. Information carries the notion of flow or movement. Information is derived from data. Information is data in motion.

 

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