THE FRAMEWORK

5 Pillars

1. Data: The Source of "Why"

Utilizing the 5 Files framework to extract the business rules and intent behind the numbers, transforming raw data into actionable intelligence.

2. Validation: The Proof of Truth

Real-time, verifiable checks that provide objective "proof" that work is accurate, ensuring every logic step is auditable and correct.

3. Governance: The Structural Guardrail

The uniform standards and framework that maintain consistency and compliance, hardening the "Logic DNA" of the enterprise.

4. Persona: The Point of View (POV)

Capturing the diverse perspectives of every stakeholder—from Marketing to AI—to ensure the system serves every human and machine persona involved.

5. Instruction: The Definition of Accuracy

Detailed "how-to" guidance that establishes the final seal of quality, defining exactly when a task has been finished correctly.

What goes where?

1. Semantics Layer: The "Business Why"

• Definition: A natural language manifest that anchors every metric to a specific Stakeholder Persona.

• Constraint: Define inclusion/exclusion logic and business intent in plain text.

• Outcome: Eliminates "Hallucination Risk" by providing the ground truth for AI agents to interpret data.

2. Logical Data Dictionary (LDD): The "Engine & Proof"

• Definition: The authoritative source of truth for all mathematical calculations and Validation Logic.

• Constraint: Must contain the Logic DNA™ Signature, the "Hardened Math," and the Validation Rules (the "proof" that the calculation is accurate).

• Outcome: Achieve Tool-Agnostic Integrity with built-in veracity; the math is not only consistent across tools but carries its own "proof of accuracy" in real-time.

3. Logical ERD: The "Blueprint"

• Definition: A conceptual architecture of entity relationships focused on logic, not physical storage limitations.

• Constraint: Must explicitly define join paths and cardinality (e.g., 1:N) to prevent fan-out errors.

• Outcome: Extinguishes "Logic Drift"—preventing data integrity errors caused by improper joins.

4. Data Dictionary (DD): The "Map"

• Definition: The high-fidelity physical source-to-target mapping.

• Constraint: Maps every logical attribute in the LDD to its exact physical location in the database.

• Outcome: Provides the Technical Provenance required for 2026-standard audits and system migrations.

5. Lookup Tables: The "Universal Translator"

• Definition: Centralized reference data for all categorical labels and hierarchies.

• Constraint: Must be decoupled from primary fact tables to allow for NLP-optimized labeling.

• Outcome: Ensures a Unified Vocabulary for consistent, readable labels across all reporting interfaces.