E2 · Publication Volume 24

Data Is Not Meaning

values, semantics, context, entities and business processes

Learning objectives

  • Explain why values, semantics, context, entities and business processes require explicit semantic modelling.
  • Design identities, relations and constraints that preserve data is not meaning across exchange.
  • Separate hard release gates from diagnostic metrics and interpretation choices.
  • Produce a semantic envelope and completeness report for one synthetic observation set from synthetic evidence.

The lesson is complete only when the learner can defend both the model and the release decision. A neat schema without evidence, tests or declared limitations is an unverified design. The assessed artefact must make assumptions visible and distinguish source assertions from derived conclusions.

Decision context

The first design decision is therefore not which table or file to use, but which real-world question the record answers. A consumer must be able to reconstruct what was observed, about which entity, under which procedure and for which decision. If that sentence cannot be written without guesswork, the record is semantically incomplete.

Start with a decision record: name the intended use, the evidence required, the consequence of error, the accepted uncertainty and the role authorised to accept residual risk. Then ask whether the proposed model can answer the decision question without relying on filename conventions, row order, undocumented defaults or someone’s memory. This prevents technology selection from concealing a missing semantic requirement.

The same record may be fit for one use and unfit for another. A rapid exploratory view can tolerate conditions that a released exchange package cannot. Fitness is therefore stated against a use, contract version and quality gate rather than attached permanently to the data.

Core concept

A stored value becomes usable evidence only when its property, subject, unit, method, time, spatial support and quality state are known. The same number can represent a concentration, an interval boundary, a confidence score or a category code. Treating those possibilities as interchangeable destroys meaning even when every byte is copied correctly.

The working scope is values, semantics, context, entities and business processes. For each item in that scope, distinguish the thing itself, the label used by a source, the claim made about it and the record that carries the claim. Identity is not a display name; a value is not its unit; an observation is not a model; current is not the same as valid. These distinctions create explicit places for correction, uncertainty and competing interpretations.

A good semantic design can be explained as a set of sentences before it is encoded. Each sentence identifies a subject, a property or relationship, an object or result, and the context under which the claim holds. Physical tables and files are then projections of those sentences, not their source of meaning.

Semantic model

Represent an observation as a relation between a subject entity, an observed property, a result and a procedure, then attach temporal and spatial context. Keep the result value separate from its unit and qualifier. Keep the record identity separate from the identity of the physical object. This separation permits correction without silently changing what the record claims to describe.

Test every proposed record against seven questions: What has identity? What type is it? Which property or relationship is asserted? Which spatial and temporal context applies? Which state or qualifier modifies the assertion? Which evidence supports it? Which version and activity produced the stored representation? Missing answers become explicit contract gaps.

Normalisation is used to separate independent facts, not to maximise the number of tables. A compact nested object can be semantically sound if the same identities, constraints and provenance remain explicit. Conversely, a highly normalised database can still be ambiguous when relationships and units exist only in documentation or application code.

Constraints and invariants

| Invariant | Executable or review test | | --- | --- | | Every value declares a property | Reject a value column whose intended property is only implied by a filename. | | Context is explicit | Require unit, method, time and spatial support where they affect interpretation. | | Entities precede attributes | Attach attributes to stable entity identifiers, not row position. | | Raw and interpreted claims differ | Preserve observations separately from classifications or modelled conclusions. |

An invariant is a condition that must remain true across storage, export, correction and reprocessing. Implement it as close to the authoritative boundary as practical and repeat the check at exchange boundaries. Record rule identifier, version, severity, evaluated scope, observed value and outcome so a failure can be reproduced.

Hard gates protect identity, semantic validity, required provenance and authorised use. Diagnostic checks reveal unusual values or patterns but require interpretation. Never convert a diagnostic threshold into deletion or correction without a reviewed rule and preserved source evidence.

Quantitative reasoning

Use a semantic-completeness ratio C_s = n_c / n_r, where n_c is the number of required context fields that are present and valid and n_r is the number required by the contract. The ratio is diagnostic, not a licence to average away a critical omission: a missing unit or subject identifier can still be a hard failure even when C_s is high.

Every reported ratio states its numerator, denominator, exclusions and evaluation time. Stratify results by source, entity type, contract version or processing run where aggregation could hide a local failure. Counts accompany percentages so a seemingly large change based on a tiny denominator remains visible.

Precision is part of meaning. Do not add decimal places merely because a storage type permits them, and do not round identity, interval or coordinate fields without a declared tolerance and test. Quantitative summaries support a release decision; they do not replace semantic review.

Evidence and uncertainty

Distinguish uncertainty in the measured phenomenon from uncertainty about metadata. A numerical uncertainty interval does not repair an unknown method, and a complete method description does not prove the value is accurate. Record both dimensions and let the downstream decision state which combinations are acceptable.

Build an evidence packet containing preserved source reference, acquisition or assertion context, applicable method, validation results, reviewer decision and links to derivatives. Classify uncertainty as observational, semantic, structural, parametric or policy-related where that distinction changes treatment. “Unknown” is a valid state when the evidence cannot justify a stronger claim.

Contradictory evidence remains available. The model may select one current assertion, but the reason, competing assertion and effective time are retained. This makes later reinterpretation possible without pretending the earlier evidence never existed.

Interfaces and storage

At an interface, transmit the semantic package rather than the value alone: entity identifier, property identifier, value, unit, qualifier, procedure, event time, location reference, quality state and provenance pointer. Serialisation may change while that package remains invariant.

Design an interface from the logical contract outward. Specify identifiers, types, cardinalities, units, value states, coordinate and time references, version negotiation, validation behaviour and structured errors before choosing a serialisation. The physical representation then declares its mapping to those logical elements.

Storage optimisation may partition, compress, index or cache data, but it must not change identity or silently remove context. A derived representation points to immutable inputs and a processing manifest. A cache carries freshness and contract-version information and is never treated as the only evidence copy.

Governance and access

Assign responsibility to roles: one role defines meaning, another produces records, another validates them and an authorised role approves contract changes. Role separation prevents a convenient local interpretation from becoming an undocumented global rule.

Governance is expressed through named roles, review states and versioned decisions, not through references to a particular organisation. Define who may propose, validate, approve, supersede and withdraw each governed resource. The audit trail records the role and event while avoiding unnecessary personal data.

Apply least-necessary access to source evidence and derivatives. Access controls must not erase identifiers, lineage or quality metadata needed to understand an authorised release. When policy is unresolved, quarantine the output with a precise reason and escalation route.

Integration checkpoint

From stored value to decision-ready evidence
From stored value to decision-ready evidence

The diagram summarises the control flow for this lesson. Read it from source evidence through semantic structure and validation to a decision-ready artefact. Each arrow should correspond to a declared relationship or transformation; each boundary should have a contract; each released node should have an identity, version and provenance pointer.

Integrate the lesson by adding a semantic envelope and completeness report for one synthetic observation set to the evolving synthetic data package. Verify that earlier artefacts still resolve and that the new model does not overwrite observations, identifiers, values or versions introduced in previous lessons. Record every changed assumption.

Synthetic worked example

A synthetic file contains the value 0.18 beside a sample code. The learner first labels it “concentration,” then discovers that neither the analyte, unit nor method is present. The record is quarantined rather than converted. A second source supplies the missing property, unit, method identifier and event time; the merged claim is validated against the sample identity and released with its derivation recorded.

Work the example in four passes:

  1. Preserve the received records and write the intended decision without correcting anything.
  2. Identify entities, claims, context, uncertainties and policy constraints; mark every unresolved item.
  3. Apply the versioned rules, create derivatives and record the exact transformation plus validation evidence.
  4. Issue an accept, reject or quarantine decision and show how an independent reviewer can reproduce it.

Because the example is entirely synthetic, its values demonstrate method only. The important result is the chain from received evidence to justified decision. If a required fact is absent, the worked solution records the gap rather than manufacturing a plausible value.

Practice task

Choose ten fields from a synthetic exploration table. For each field, write the real-world question it answers, identify the subject entity, property, unit or code set, time, support and quality rule, and mark any meaning that still depends on a filename or personal memory. Convert those implicit assumptions into contract fields or rejection rules.

Use the following acceptance criteria:

  • All required identifiers and references resolve to declared types.
  • Every transformation preserves the received evidence and records its derivation.
  • Invalid, unknown and inapplicable states remain distinct and machine-testable.
  • The output identifies the contract, vocabulary and processing versions used.
  • A second reader can reproduce the validation result without private knowledge.

Submit the source snapshot, authored contract or model, validation output, derivative, manifest and a short decision record. A screenshot alone is insufficient because it cannot demonstrate the exact input, version or rule execution.

Common failure modes

  • A number is imported because its column name looks familiar.
  • A category code is mistaken for a measured magnitude.
  • An interpreted class overwrites the original observation.
  • Metadata is stored only in a separate document with no stable link.

These failures share a pattern: convenient representation is mistaken for verified meaning. Diagnose the earliest boundary at which an assumption became implicit. Correct by restoring source evidence, making the assumption a versioned field or rule, rerunning dependent transformations and superseding—not overwriting—the affected release.

Do not repair a failure by adding an undocumented default. A blocked result with a specific missing dependency is safer and more reusable than a complete-looking result whose meaning cannot be reconstructed.

Review questions

  1. Why can byte-perfect transfer still lose meaning?
  2. Which context fields are hard gates for a concentration result?
  3. How should an observation differ from an interpretation?
  4. When should a semantically incomplete record be quarantined?

For each answer, identify the governing invariant, the evidence needed to evaluate it and the appropriate release behaviour when the invariant fails. A strong answer distinguishes scientific uncertainty from missing semantics and distinguishes a recoverable warning from a hard contract violation.

Sources and further reading