Book Pick: Data, Information, Knowledge

Data are digital objects devoid of context and, therefore, of meaning (left); information is data with context (center); knowledge is information with concern (right) (cartoon from Albert)
Excerpt from Enterprise Architecture Fundamentals:

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Ingrained habits die hard, and mental ones are the last to be disrupted. The sway of pyramids to represent enterprise architectures is one such habit, and the significance of such representations is more than metaphoric.

But for our purpose here, it is enough to say that pyramids are actual as well as metaphoric architectural dead ends: like homes carved in stone for the departed, they are immutable and closed for business. Thus, as a representation of enterprise architectures, pyramids induce two major misconceptions:

  • The juxtaposition of an information layer on top of or beneath other ones (e.g., application, technology, business) obfuscates the ubiquity and role of information as a neurotransmitter across enterprise architecture layers.
  • A flat and indiscriminate representation of data, information, and knowledge flies in the face of the digital transformation, which is characterized by the emergence of data factories as an industry of its own, the advent of data privacy as a strategic issue, and the spreading of Knowledge-graph technologies with their layered architecture.

Figure 4-11. EA: From Vault to Pillar

By contrast, defining information along an orthogonal axis set across enterprises’ architecture layers establishes information as a mainstay that unifies business and systems perspectives:

  • Data, from direct observations or as a product of data factories, can be neatly separated from information managed by systems — a necessary condition for compliance with privacy regulations.
  • Information, for the subset of data that fits within the categories pertaining to business objectives (e.g., customers, accounts), can be continuously and consistently managed as systems surrogates.
  • Knowledge, for individual or collective skills and expertise, can be explicitly represented in conceptual models, Knowledge graphs or ontologies. 

That separation of concerns and its alignment with enterprise architecture layers (organization, systems, platforms) brings clear and direct benefits for the governance of enterprises bound up in digital environments. As represented by the Pagoda Blueprint (cf. chapter 6), the integration of systems and knowledge architectures also ensures the convergence of business and systems perspectives:

  • Business/symbolic level: anchoring models of business environments and objectives to enterprise architecture
  • Logical/functional level: aligning information systems and business categories
  • Physical/digital level: merging observations from environments (data mining) with operational data (process mining)

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(From Chapter 4)