Information BehaviorsHow Information is More Important Than Knowledge

An Archestra notebook.

© 2014 Malcolm Ryder / archestra research

Relativity

Our “knowledge about knowledge” has led to scientific delineation of its elemental structure, offering a production pattern for its synthesis.

The synthetic pattern features data as the smallest unit (“atoms”), combined in defined relationships that create information (“molecules”), which in turn groups and acts in specified contexts as a state of knowledge (‘objects” or “compounds”).

If data, information and knowledge are each already defined to structurally distinguish them from each other, then how is it that one person’s information is another person’s knowledge? That one person’s knowledge is merely another person’s data? That one person’s data is another person’s information?

These conditions occur because they are the normal result of utilitarian matters, where the way something needs to be used is what really decides how it is defined, and at minimum the decision is borne out by the experience of the results.

A universe of intellectual contentMost interest in “knowledge” has to do with thinking, and most thinking is topical. There is always high interest in building up a reliable coverage of the topic.

But most thinking does not begin with data.

Most thinking begins with expressions received as information.

As a starting point, information is processed into both data and knowledge. More processing of data and knowledge can continue to occur; but without deliberate intent, that additional processing does not necessarily link the derived data to the derived knowledge. At minimum, we know this is the case because of rhetoric.

Operations performed on information create a functional “space” of possible relationships between information, data and knowledge -- relationships which are not necessarily hierarchical and can be non-linear, as well as one-to-many or many-to-many, and bidirectional.

What’s important about that is the real-world experience of that space, which is not about data, information and knowledge. Instead, the main concern is with how Messages, Facts and Meanings co-exist – in turn giving roles to information, data and knowledge, respectively. Roles turn out to be a more useful and consistent way of defining these elements.

INFORMATION(Expressions, Signals)

DATA(Observations, Selections)

KNOWLEDGE(Contextualizations, Validations)

Information Operations: substantiating the info

Data can become information, but alsoit can become knowledge. Some operations can compose (synthesize) data to produce information. Some operations can interpret (decode) data to produce knowledge. Meanwhile, knowledge can also be modeled to refer to information.

Goal: Facts

Goal: Messages

Goal: Meanings

Composition

Reference Modeling

yz

x

Data interpretation

Info collection

© 2014 Malcolm Ryder / archestra research

INFORMATION(Expressions, Signals)

DATA(Observations, Selections)

KNOWLEDGE(Contextualizations, Validations)

Information Operations: elaborating the info

Information is detected or received. Information can become data and it can become knowledge. Some operations can analyze information to produce data. Some operations can assess information to produce knowledge. Meanwhile, knowledge can also be codedas data.

Goal: Facts

Goal: Messages

Goal: Meanings

Info analysis

Info assessment

yz

x

Knowledge coding

Info collection

© 2014 Malcolm Ryder / archestra research

Field of InterestIn our ordinary conceptual life, we experience numerous different balances and disparities of meanings, messages and facts.

These differences stem partly from where we are, “mentally”, when we encounter those items, and partly from how they are being provided to us (both separately and concurrently).

The initially experienced balances can subsequently change, either with our own help or without.

When taken “as is”, without changing, the balances “cover” our interest in ways that we can decide to accept and possibly even reinforce.

But we can also consider and attempt to change the balances in order to fit them more closely to our immediate purposes. The purposes may be persuasive, remedial, exploratory, conformational, etc.

Field EffectsIn effect, it is behaviors that generate the coverage of our interest – by determining the extent to which messages, facts and meanings respectively contribute.

Within the field of interest, coverage includes those items and any of their potential concurrencies such as:

• Facts-with-facts; facts-with-messages; facts-with-meanings

• Messages-with-messages; messages-with-meanings

• Meanings-with-meanings

The manipulations, whether impending or already evident, operate on ideas to variously render and manipulate them as messages, facts and meanings in the form of information, data, or knowledge – thereby supporting the retention, regeneration and reuse of the ideas.

Field EffectsThe following sketches illustrate typical field manipulations and effects.

Each illustration is also associated with some typical issues regarding the availability or uses of the items underlying the coverage in the field.

These illustrations and issues are neither “technical specialties” nor “standards”. And they are not intended to be collectively exhaustive.

Instead, they are simply reflections of common experience.

In that light, it is common that “interested” behaviors are often goal-oriented, constrained, and productive. This characteristic is also part of the annotations, identifying how key operations on information are routinely distinctive.

INFORMATION

DATA

KNOWLEDGE

Topicality

Information, data and knowledge collectively cover attention to a topic.

Within the 3-D “space” of operations, the yield of data from information at a given time can be lesser or greater than the extent of knowledge developed with the information.

Said differently, there can be: more facts than meaning; more meaning than facts; or, in tandem, more or less of both.

Goal: Facts

Goal: Messages

Goal: Meanings

assessment

yz

x

analysis

collection

© 2014 Malcolm Ryder / archestra research

INFORMATION

DATA

KNOWLEDGE

Volume, Diversity, Redundancy

More information, on its own, does not necessarily increase the data nor the knowledge, even if it amplifies attention to the topic.

Goal: Facts

Goal: Messages

Goal: Meaningsassessment

yz

x

analysis

collection

© 2014 Malcolm Ryder / archestra research

Indication, Inference, Hyperbole

Even in small supply, data identified in information can be critically distinctive, allowing some meanings to emerge at high levels of confidence, such as through a process of elimination orprojection.

assessment

yz

x

analysis

INFORMATION

KNOWLEDGE

Goal: MessagesConstraint: SourceProduct: Statement

Goal: MeaningsConstraint: ContextProduct: Value

DATA

Goal: FactsConstraint: FormProduct: Argument

collection

© 2014 Malcolm Ryder / archestra research

INFORMATION

KNOWLEDGE

Resolution, Granularity, Acuity

Multiple separate data, derived from the same information supply, may increase data volume; and the data may have interrelationships, but those might be merely circumstantial ones.

Goal: MessagesConstraint: SourceProduct: Statement

Goal: MeaningsConstraint: ContextProduct: Value

assessment

yz

x

analysis

DATA

Goal: FactsConstraint: FormProduct: Argument

collection

© 2014 Malcolm Ryder / archestra research

P.S. - Thinking about Content

The functional perspective on the “field of interest” allows us a certain further understanding of the management of ideas.

We know that ideas are represented at various levels of language, itemization, and specificity. Yet we also know that all of these representations are addressable as “content”.

By understanding content as “the actor in the role” of message, fact or meaning, it is understandable how the diversity of material seen in a given role by a population of thinkers is neither unusual nor problematic.

Instead, it aligns comfortably with the diversity of “occupations” in that population – while making each role variable and more widely approachable through differing iterations via content. This also explains why content management is increasingly a higher-level executive function in the community of interest.