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ISO/WD 21926 Semantic Data Model for Audit Data Services – Draft for Commenting Period Now Open
We are excited to announce that the draft of the “Semantic Data Model for Audit Data Services” is now open for public commenting. This draft outlines the framework, methodologies, and key elements for establishing a standardized approach to audit data services. Below are the key details and milestones related to this important project.
1. Project Overview
1.1. Title and Scope
Title: Semantic Data Model for Audit Data Services
Scope: This document establishes a semantic data model for audit data services, detailing how audit data is defined, represented, and recorded within the model.
1.2. Timeline
Registration Date: 2023-08-07
Timeframe: 24 months
Time Since Registration: 9 months
In Stage: 20.20 (Working Draft – Study Ongoing + Expert Commenting)
Status: Working Draft (WD) study ongoing with expert commenting
1.3. Stage Details
| Stage | Version Description | Target Date | Limit Date | Status |
|---|---|---|---|---|
|
20.00 |
New project registered in TC/SC work programme |
– |
2023-08-07 |
Closed |
|
20.20 |
Working draft (WD) study initiated |
2024-05-24 |
2024-05-24 |
Current |
|
20.60 |
Close of comment period |
2024-07-18 |
2024-07-18 |
Awaiting |
|
20.99 |
WD approved for registration as CD |
– |
– |
Awaiting |
|
40.00 |
DIS registered |
2024-08-07 |
2024-08-07 |
Awaiting |
|
60.60 |
International Standard published |
2025-08-07 |
2025-08-07 |
Awaiting |
1.4. Ballots
| Type | Version | Started | End Date | Status | Result |
|---|---|---|---|---|---|
|
WDS |
1 |
2024-05-24 |
2024-07-18 |
OPEN |
– |
|
NP |
1 |
2023-05-05 |
2023-07-29 |
CLOSED |
– |
2. Framework Extensibility and Background Requirements
The semantic data model framework is designed with extensibility and adaptability at its core, ensuring it can meet the diverse requirements of various business environments and regulatory frameworks. Below are the key components supporting its extensibility:
2.1. Introduction
The semantic data model for audit data services establishes uniform definitions of accounting data elements and facilitates the extraction of relevant audit data. This standard primarily focuses on enabling access to audit data, bridging the gap between various stakeholders in the audit services, including auditors, auditees, software developers, and IT professionals. By providing a framework and methodology that expresses accounting information independently of specific accounting and application systems, the standard supports data extraction efforts in critical areas such as General Ledger Journal Entry, Accounts Receivable, Sales, Accounts Payable, Purchasing, Inventory (both stock and movement data), and Property, Plant, and Equipment.
As the domain of Audit Data Services (ADS) has grown increasingly complex over the years, there is a pressing need for sophisticated tools to manage, understand, and accurately represent audit data. This document introduces the framework and methodology tailored to ADS, which balances theoretical robustness with practical applicability, ensuring the model remains relevant and adaptable to the dynamic data landscape.
ISO AWI 21926 comprises several components, each addressing different aspects of the semantic data model for Audit Data Services (ADS):
— Part 1: Outlines the framework of the semantic data model, providing foundational knowledge necessary for understanding and implementing audit data services.
— Part 2: Describes the methodology for extending the Foundational Semantic Model to the Business semantic model through specialization techniques and details the generation of the Logical Hierarchical Model using the graph walk methodology.
— Part 3: Provides guidance on how to bind the Logical Hierarchical Model to the CSV file format as specified in ISO 21378, enhancing the model’s applicability across various data processing environments.
— Part 4: Describes the process for binding the Logical Hierarchical Model to XML schema syntax, JSON schema syntax, and CSV columns in accordance with ISO 21377, facilitating data interchange and system interoperability.
2.2. Foundational Semantic Model (FSM)
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Core Architecture: Utilizes Unified Modeling Language (UML) to define classes and their relationships.
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Base Definitions: Establishes a consistent foundation for data representation and ensures interoperability.
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Abstract Classes: Serves as a base for further specialization without being instantiated on its own.
2.3. Business Semantic Model (BSM)
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Specialization: Extends the FSM to accommodate specific regional or organizational requirements.
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Regional and Sectoral Adaptation: Integrates local data standards, regulations, and practices.
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Customization: Allows the addition of new properties and modification of existing ones to meet specific needs.
2.4. Logical Hierarchical Model (LHM)
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Hierarchical Representation: Organizes data into a structured hierarchy, facilitating clear communication and data integrity.
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Graph Walk Methodology: Transitions the BSM into the LHM through systematic navigation of data structures.
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File Format Binding: Maps the LHM to various data formats (XML, JSON, CSV), ensuring practical implementation and usability.
2.5. Layers of Shared, Aligned, and Distinct
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Shared Layer: Contains universally applicable classes and properties for consistent audit practices.
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Aligned Layer: Adapts to regional requirements by refining the shared layer’s classes and properties.
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Distinct Layer: Supports sector-specific content and unique auditing requirements for specific sectors or user groups.
2.6. Extension by Specialization
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Process Overview:
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Identification of Core Elements: Begins with FSM, identifying core abstract classes and elements.
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Specialization: Extends and refines these classes to create the BSM, accommodating regional and sector-specific needs.
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Aligned and Distinct Extensions: Develops shared models and includes unique attributes specific to certain domains.
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Graph Walk Methodology:
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Hierarchical Organization: Organizes data elements hierarchically, capturing relationships and dependencies.
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Syntax Binding: Prepares the LHM for practical implementation by binding it to specific syntax rules of XML, JSON, and CSV formats.
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This comprehensive framework ensures that the semantic data model remains robust, adaptable, and capable of supporting the diverse and dynamic requirements of audit data services across different regulatory environments.
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