“Built for Clear Business or Analytical Purpose”

What DPCH12 is really asserting​

DPCH12 is not asserting that:

“Someone might use this data” or “it supports analytics.”

It is asserting that:

A Data Product is intentionally designed, evolved, and governed around explicit consumer needs and business outcomes, not around source systems, pipelines, or producer convenience.

Intent is the north star of the product.

The Essence (HDIP + Data Mesh Interpretation)​

A Data Product satisfies DPCH12 if and only if:

1. There is a clear, declared purpose for the product
2. That purpose is defined in consumer and outcome terms
3. The product’s design decisions can be traced back to that intent

If the product exists “because the data is there”, DPCH12 is not met.

Positive Criteria — When DPCH12 is met​

DPCH12 is met when all of the following are true:

1. Intent is explicitly declared​

The product clearly states:

• who it is for (consumer archetypes, roles, systems, AI agents)
• why it exists (business decision, regulatory obligation, analytical insight)
• what outcome it enables (decision quality, risk reduction, efficiency)

This intent is part of the product definition, not an afterthought.

2. Product design reflects consumption needs​

Evidence includes:

• schemas shaped around consumption, not source structure
• aggregations, semantics, and freshness aligned to use cases
• output ports chosen to match consumer modes (API, query, visual)

This is where DPCH12 ties directly to DPCH10 and DPCH11.

3. Evolution is driven by consumer feedback​

Changes to the product are triggered by:

• new or evolving use cases
• consumer feedback and adoption signals
• changes in business priorities

Not by:

• upstream system changes alone
• platform migrations
• engineering refactors

Negative Criteria — When DPCH12 is not met​

DPCH12 is not met if any of the following are true:

❌ Product is source-driven​

Examples:

• “We expose everything from system X”
• “Schema mirrors the source database”
• “We publish raw extracts and let users figure it out”

This optimizes for producers, not consumers.

❌ Intent is vague or generic​

Examples:

• “For analytics”
• “For reporting”
• “For downstream use”

Such statements provide no design guidance.

❌ Consumption intent is implicit or tribal​

Examples:

• use cases exist only in people’s heads
• consumers discovered after the product was built
• product justified post-hoc

This leads to accidental, fragile reuse.

Edge Cases (Important Guidance for Agents)​

Case 1: “Single known consumer, clearly defined”​

⚠️ Partial

Rationale:

• intent exists
• but breadth of consumption unproven
• acceptable early state

Case 2: “Multiple consumers with different intents”​

✅ Met

Rationale:

• product designed to serve multiple use cases
• intent-driven abstraction validated

Case 3: “Intent exists but ignored in design”​

❌ Not met

Rationale:

• intent must shape the product, not just decorate it

Evidence Signals an Agent Should Look For​

Authoritative evidence:

• explicit product purpose statement
• named consumer personas or archetypes
• declared outcomes or KPIs

Supporting evidence:

• links to business processes or decisions
• consumption patterns mapped to CSxx archetypes
• backlog items tied to consumer needs

Red flags:

• “future use cases TBD”
• no named consumers
• design discussions focused only on sources

How an AI Agent Should Decide​

Decision rule (simplified):

If the Data Product’s structure and evolution cannot be clearly traced back to explicit consumer needs and business outcomes, DPCH12 is not met.

Why DPCH12 Is Non-Negotiable​

Without DPCH12:

• products drift into generic data dumps
• reuse becomes accidental
• cost grows without value signal
• governance cannot prioritize meaningfully

DPCH12 is what keeps productization value-driven, not platform-driven.

Canonical Statement​

DPCH12 is satisfied only when a Data Product is intentionally designed and evolved around explicit consumer needs and business outcomes, with its structure and interfaces directly traceable to declared consumption intent.

DPCH13 — Testable & Versioned

“Version-Controlled and Contract-Tested”

What DPCH13 is really asserting​

DPCH13 is not asserting that:

“The code is in Git” or “there are tests.”

It is asserting that:

A Data Product evolves through explicit versions protected by automated tests that preserve its declared intent, semantics, and contracts for consumers over time.

DPCH13 is about protecting trust during change.

The Essence (HDIP + PMDD Interpretation)​

A Data Product satisfies DPCH13 if and only if:

1. Change is intentional and visible through versioning
2. Consumer-facing contracts are explicit and tested
3. Product evolution does not silently break consumers

If consumers discover changes by surprise, DPCH13 is not met.

Positive Criteria — When DPCH13 is met​

DPCH13 is met when all of the following are true:

1. Product versions are explicit and meaningful​

The Data Product:

• has a clear version identifier (semantic or equivalent)
• distinguishes breaking vs non-breaking changes
• communicates version changes to consumers

Versions represent product meaning, not just pipeline changes.

2. Contracts are testable and enforced​

The product defines and tests:

• schema contracts (structure and types)
• semantic contracts (meaning, units, invariants)
• quality contracts (completeness, freshness thresholds)
• policy contracts (access, residency, retention)

These tests run automatically as part of product lifecycle (pre-publish, post-publish).

3. Tests protect intent and semantics​

Tests verify that:

• declared business assertions still hold
• semantic mappings remain valid
• trust signals meet minimum thresholds

This is where DPCH03 (declarative intent) becomes executable protection.

Negative Criteria — When DPCH13 is not met​

DPCH13 is not met if any of the following are true:

❌ Versioning exists only at technical level​

Examples:

• pipeline versions
• table versions without product context
• infra releases without consumer meaning

Consumers care about product behavior, not pipelines.

❌ Tests focus only on technical correctness​

Examples:

• tests validate SQL execution
• row counts without semantic meaning
• infrastructure health checks only

These do not protect consumers from semantic breakage.

❌ Breaking changes occur silently​

Examples:

• schema changes without notice
• semantics altered without version bump
• quality thresholds relaxed unnoticed

This destroys trust and reuse.

Edge Cases (Important Guidance for Agents)​

Case 1: “Schema tests exist, semantics untested”​

⚠️ Partial

Rationale:

• some protection exists
• intent still fragile
• common early stage

Case 2: “Versioned releases + contract tests”​

✅ Met

Rationale:

• consumers can rely on stability
• evolution is controlled and observable

Case 3: “Producers coordinate changes manually”​

❌ Not met

Rationale:

• manual coordination does not scale
• violates self-service and automation principles

Evidence Signals an Agent Should Look For​

Authoritative evidence:

• product version history
• automated contract test results
• pre-publish validation gates

Supporting evidence:

• changelogs linked to versions
• consumer compatibility policies
• rollback or coexistence strategies

Red flags:

• “latest” as only version
• tests owned only by engineering
• no record of breaking vs non-breaking changes

How an AI Agent Should Decide​

Decision rule (simplified):

If the Data Product can change in ways that break consumer expectations without being detected or versioned, DPCH13 is not met.

Why DPCH13 Is Non-Negotiable​

Without DPCH13:

• reuse collapses under change
• trust signals decay
• governance becomes reactive
• platform adoption stalls

DPCH13 is what makes long-lived, reliable data products possible.

Canonical Statement​

DPCH13 is satisfied only when a Data Product evolves through explicit versions protected by automated, consumer-facing contract tests that preserve intent, semantics, quality, and policy expectations over time.