Evidence Base
Research Papers
Structured, evidence-based publications on data systems. Each paper focuses on a single T-dimension or cross-T interaction, with explicit methodology and reproducible analysis.
Bi-temporal Modeling as a First-Class Data Architecture Pattern
This paper establishes the case for treating temporal modeling as a structural requirement rather than an optional feature. We analyze 14 enterprise systems where the absence of bi-temporal modeling created irrecoverable audit failures.
The Reconciliation Gap: Measuring Truth Deficits in ERP Integrations
A quantitative analysis of truth failures in enterprise ERP integration scenarios. We introduce the Reconciliation Gap Index (RGI) as a measurable proxy for T1 dimension health.
Shadow Data as a Trust Indicator: Organizational Patterns and Remediation
Shadow spreadsheets are not a user behavior problem — they are a Trust dimension failure. This paper analyzes 22 organizations where shadow data emerged and maps each case to specific T5 and T7 deficiencies.
Transformation Logic Sprawl: Causes, Measurement, and Structural Remediation
Business rule duplication is the most common Transformation dimension failure. We define Transformation Sprawl Score (TSS) and present a remediation framework for consolidating distributed business logic.
Automated Lineage Coverage: Completeness Metrics and Regulatory Alignment
Lineage completeness is rarely measured. This paper introduces four lineage coverage metrics — breadth, depth, granularity, and freshness — and maps them to regulatory requirements including BCBS 239 and GDPR Article 30.
Data Ownership Structures in Federated Organizations
Federated organizations face unique challenges in establishing clear data ownership. This paper analyzes ownership models across decentralized enterprises and identifies structural patterns that preserve accountability without centralizing control.
Technology Selection Without Dimension Analysis: A Failure Pattern Study
Technology is the most visible data dimension but rarely the root cause of data problems. We document 9 cases where technology replacement failed because underlying Truth and Traceability deficits were not addressed first.
Data Literacy as Infrastructure: Measuring Team Dimension Maturity
Data literacy is frequently treated as training rather than infrastructure. This paper proposes a Team Dimension Maturity Model (TDMM) with four levels and measurable criteria for each.