TBM Data Architecture & Strategy Guide
Volume 1
The data companion to the TBM Modeling Guide, written using TBM Taxonomy 5.0.1. This guide covers how to source, shape, tag, identify, and govern the data that feeds a TBM model, from first GL export to AI-ready data estate.
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Data you can trust, join, and explain
The model is only as reliable as the data beneath it. The guide gives sources, identifiers, tags, and outputs one shared architecture.
One shared architecture
Finance, IT, and data teams use the same six layers and entity names.
Data that joins
Standardized IDs, crosswalks, and effective dating keep one entity one entity.
Numbers you can defend
Contracts, lineage, and versioning trace any figure to its source.
Ready for AI
AI Solutions get their own data requirements, and AI tools and agents get a clear bar.
Where should you start?
The TBM Data & Architecture guide is extensive. Use the following guidance to determine the appropriate route for your role.
TBM Practitioner
Your job:
Get trustworthy source data into the model and keep it that way, starting with what you have.
Read:
Part I (Ch 1 to 3), then Ch 4 to 6 for sources and Ch 12 for tagging. Use Ch 3 to place yourself on the arc, and Appendix A for terms.
Do:
Baseline your sources with Appendix B, tag early using Appendix D, and log the gaps you find.
Watch for:
Waiting for perfect data, or treating tags as a substitute for identity.
Data Architect or Engineer
Your job:
Build the layers, contracts, and identifiers so data lands once and joins everywhere.
Read:
Part III (Ch 9 to 13) first, then Ch 8.6 on governed outputs and Ch 25 on identifiers and reconciliation.
Do:
Adopt the six-layer model, write data contracts (Appendix M), choose patterns (Appendix E), and set standardized IDs and crosswalks (Appendices J to L).
Watch for:
Joining on names, silent schema changes, and rebuilding layers instead of extending them.
Finance, IT, and Governance Leaders
Your job:
Own source quality, approve ownership and policy, and sponsor the shift from reporting to design.
Read:
Part I, Ch 4 and 5 to see who owns which source, Ch 8, and the Part VI overview in Ch 23.
Do:
Name data owners, agree the identifier of record for each entity, and fund remediation at the source.
Watch for:
Source teams that never hear the model’s feedback, and AI pilots built on unreconciled data.
Follow the data through six layers
Every chapter uses the same vocabulary. Data enters at the source, is normalized and identified, becomes a governed model, and is exposed as measures, views, and actions.
Six semantic layers, tagged and governed throughout
The layers are defined in Chapter 9.6 and made concrete by the standardized data model in Chapter 13. Chapter 12 tagging runs through all of them as evidence tied to standardized IDs.
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What each part helps you do
Parts I, II, III, and VI are in Volume 1. Parts IV and V (shown dashed) arrive in the next volume. Part numbering is continuous across both volumes.
Foundations · Ch 1 to 3
Scope, principles, and the maturity arc, set before any data is touched.
- Ch 1: Scope, Purpose & Audience: what the guide covers, what it leaves to the Modeling Guide and Playbooks, and how to read it by maturity stage.
- Ch 2: Core Concepts & Principles: data as the foundation for all six Organizational Value Drivers, the data philosophy, the virtuous cycle of quality, and how taxonomy, schema, data model, semantic model, and platform differ.
- Ch 3: Data Capability Progression: Ramping, Accelerating, Innovating, assessed per dimension with indicator questions.
What data exists, where it lives, and what a well-formed extract carries.
- Ch 4: Financial: GL, fixed assets, invoices and AP, vendor and contract data, labor and HR, benchmarks.
- Ch 5: Operational & IT: CMDB and ITAM, service desk, catalogs, portfolio data, utilization, network.
- Ch 6: Cloud & Vendor Billing: provider formats, account hierarchies, commitments, multi-cloud, FinOps hand-offs.
- Ch 7: Extended Value Domains: sustainability, cyber and risk, customer experience, with maturity prerequisites.
- Ch 8: TBM-Generated Data: outputs, rules, and mappings as governed assets, versioned and protected (8.6).
The structure that lets data land once and join everywhere.
- Ch 9: Architecture Patterns: deployment and integration options, plus the six-layer semantic architecture (9.6).
- Ch 10: Formats & Interchange: file types, key fields, normalization, multi-entity handling, and data contracts with their eleven fields (10.6).
- Ch 11: Integration & Pipelines: batch to streaming, ETL vs ELT, close cycles, observability, integration inventory, contract testing.
- Ch 12: Tagging Strategy: taxonomy by layer, enforcement, cloud and on-prem, and tags as evidence tied to standardized IDs (12.9).
- Ch 13: Data Models & Alignment: entities, keys, relationships, hierarchies, record-level detail, effective dating, lineage, taxonomy sync, FOCUS interoperability.
Quality, ownership, lifecycle, and security for the data estate.
- Five quality dimensions, thresholds by use case, and relationship conformance testing.
- Governance roles, RACI patterns, and the model steward.
- Lifecycle and retention, classification and access, residency, and audit.
One shared architecture
- Five quality dimensions, thresholds by use case, and relationship conformance testing.
- Governance roles, RACI patterns, and the model steward.
- Lifecycle and retention, classification and access, residency, and audit.
Data about AI, and data for AI.
- Ch 23: Data requirements across the spectrum of AI Solutions: six lifecycle stages, five starting architectures, minimum viable data, and the AI data inventory.
- Ch 24: AI cost, consumption, and value data: ten source types from model billing and GPU telemetry to contracts and value evidence.
- Ch 25: Identifiers, tagging, and reconciliation: identifiers set at provisioning, an AI tag extension, provider crosswalks, three reconciliation checks, and ownership.
- Ch 26: Preparing TBM data for AI tools and agents: reporting-grade vs action-grade data, the AI consumption layer, and propose, review, commit.
Coming in Volume 2
Chapters 14 to 22: quality, governance, and the plan to run it
Volume 2 extends what Volume 1 builds with the controls that keep the data trustworthy and the strategy that turns it into a practice. Volume 1 restates the concepts it needs, so it can be applied before Volume 2 is published.
Data Quality Framework
Five quality dimensions, measurement approaches, Good/Better/Best levels, thresholds by use case, the virtuous cycle, and semantic validity and relationship conformance testing.
Data Governance Operating Model
Governance roles, governance by scale and maturity, RACI patterns, governing TBM-generated data, enterprise governance integration, and the model steward.
Data Lifecycle Management
Acquisition to retirement, versioning of rates, allocations, and mappings, retention, archive and purge, audit trails, and effective dating for restatement.
Data Security, Privacy & Access Control
Classification, role-based access, PII and confidential commercial data, data residency, and protection of TBM-generated outputs.
TBM Data Strategy Development
The seven pillars of a TBM data strategy, alignment to outcomes and use cases, stakeholder engagement, and keeping the strategy current.
Data Roadmap Planning
Minimum viable data, baselining, prioritizing gaps, sequencing investments, and sample 12 to 24 month roadmaps.
Getting Started: Early-Stage Execution
A six-step starting sequence, quick wins with existing data, initial integrations, basic governance, and common early failures.
Scaling Data for a Maturing Practice
Expanding scope, automating manual work, managing integration complexity, onboarding extended value domain data, and global scaling.
TBM by Design
Launching Solutions with correct metadata, evaluating platforms for TBM compatibility, scoping ESG, cyber, and CX data, embedding governance roles, and a launch-time data contract.
Help shape Volume 2: If you are interested in supporting the creation of these chapters, please reach out to the Standards team.
What the Appendices hold
Nine Appendices released with Volume 1
Reference tables and templates you can use at the point of work.
Key Terms & Definitions
The single alphabetical glossary: every defined term, where it is first defined, abbreviations, and an index by chapter.
Data Source Inventory Template
Document every source: system of record, owner, cadence, key fields, classification, and AI Solution support.
Tagging Taxonomy Reference
29 tag keys in six groups, with required levels, permitted values, governance notes, and the AI tag extension.
Integration Pattern Catalog
Five integration patterns in twelve attributes each, with fit by maturity stage, source category, and AI source type.
Cross-Reference
Maps each section of this guide to the Modeling Guide and Use-Case Playbooks, so nothing is duplicated or missed.
Standardized Entity Dictionary
Standardized entities, common attributes, allowed-value registers, and the mapping and crosswalk record.
Entity-Relationship Matrix
Which entities relate, with what cardinality, constraints, and bridges, in one numbered reference.
Identifier & Crosswalk Registry
A template for identifiers of record, crosswalks, and the events that change them.
TBM Data Contract Template
The field-by-field template for writing, versioning, and registering a data contract for one interface.
Coming with Volume 2: Appendix C (Data Quality Assessment Scorecard), F (Data Governance RACI Template), G (TBM Data Roadmap Template), H (AI Readiness Self-Assessment Checklist), N (Conformance Test Checklist), and O (FOCUS-to-TBM Interoperability Profile). They depend on Volume 2 content.
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Authors and Contributors
The following individuals contributed to the development of the TBM Data Architecture and Strategy Guide. The guide is governed by the TBM Council Standards Committee, which reviews it at least annually.
Matt Erickson
Director, TBM Standards and Practice
TBM Council
Justin Mann
Head of Standards, Research, & Education
TBM Council
Simon Voisey
Managing Consultant
It’s Value
Peter van Loon
CEO
It’s Value
Carollyn Gehrke
Solutions Advisor
Cprime, Inc.
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Feedback, corrections, and content requests are welcome: standards@tbmcouncil.org
