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Introduction
The document merge process integrates a document’s domain data into the organization’s existing domain data structure. This ensures a comprehensive business perspective at the organizational unit level, reducing redundancy while preserving relevant, high-quality information aligned with organizational priorities.
Domain data extraction
Purpose
Documents are analyzed to extract domain data, metadata, and key insights at both the organizational unit and business architecture domain levels. The extracted information determines whether integrating the document enhances organizational domain data while maintaining compliance with business architecture principles.
Extraction process
For each affected organizational unit, the system evaluates:
Normalized domain data
Document weighting
Criteria analysis values across business architecture domains
Organizational unit analysis
Structure
Hierarchy navigation: Navigate the existing organizational hierarchy for comparisons at each level.
Domain alignment: Use existing domain mappings within the organizational structure.
Business architecture domains: All
Comparison
Document weighting: Normalized scores per business architecture domain relative to existing data.
Criteria analysis: Normalized scores for relevance, quality, and quantity per domain.
Integration types
Additive merge: Combines complementary data with existing domain data.
Overwrite merge: Replaces outdated domain data with higher-priority content.
Update merge: Integrates updated data while preserving historical context.
Updating normalized values
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Merge types
When merging a document in Orthogramic, you can choose from three types of merges based on how the new information should integrate with existing business architecture domain data. Each merge type ensures that your organization maintains a structured and strategic approach to managing its domain data.
Additive merge
An additive merge integrates the new document’s data alongside existing domain data without removing or replacing any content. This approach is ideal when the new information provides additional insights or expands on existing knowledge without creating conflicts.
Use case:
When the document contains complementary information that enhances an existing domain without requiring changes to previous data.
When multiple perspectives or data sources should be retained for future reference.
Outcome:
The merged document contributes to the domain while keeping all prior data intact.
Overwrite merge
An overwrite merge replaces outdated or redundant domain data with higher-priority content from the new document. This approach is useful when the new document provides more accurate, updated, or authoritative information than what currently exists.
Use case:
When the document contains the latest policy, strategy, or regulatory update that must replace outdated versions.
When previous data is no longer relevant or has been superseded by new business decisions.
Outcome:
Older data is removed and replaced by the new document’s content.
Update merge
An update merge selectively integrates new data while preserving historical context where relevant. This approach balances maintaining past records while incorporating necessary updates.
Use case:
When parts of the document contain updates that should be merged with existing data without completely removing prior versions.
When historical context is important for tracking changes over time, but certain aspects of the domain require modification.
Outcome:
The merged content reflects updated information while keeping historical dependencies intact.
Choosing the right merge type
Before proceeding with a merge, review the proposed integration to ensure alignment with organizational objectives. Orthogramic provides an opportunity to review and edit the proposed merge before finalizing it, allowing you to refine the data integration process based on business needs.