COST03-BP05: Add organization information to cost and usage

Enhance cost and usage data with organizational context to enable meaningful analysis and attribution. Adding organizational information transforms raw cost data into actionable business intelligence that supports decision-making and accountability.

Implementation guidance

Adding organizational information to cost and usage data involves enriching raw AWS billing data with business context, metadata, and organizational structure information. This enrichment enables more meaningful analysis, better cost attribution, and improved decision-making capabilities.

Information Enhancement Principles

Business Context: Add information that relates cloud costs to business operations, such as customer segments, product lines, and revenue streams.

Organizational Structure: Include organizational hierarchy information such as business units, departments, teams, and cost centers.

Operational Context: Add operational information such as environment types, application classifications, and service levels.

Temporal Context: Include time-based information such as project phases, business cycles, and seasonal patterns.

Types of Organizational Information

Hierarchical Information: Business unit, department, team, and individual ownership information that reflects organizational structure.

Financial Information: Cost centers, budget allocations, project codes, and financial reporting categories.

Operational Information: Environment classifications, service levels, compliance requirements, and operational procedures.

Business Information: Product associations, customer segments, revenue attribution, and business value metrics.

AWS Services to Consider

AWS Resource Groups

Organize resources with organizational metadata. Use resource groups to apply consistent organizational information across related resources.

AWS Systems Manager Parameter Store

Store organizational metadata and configuration information. Use Parameter Store to maintain centralized organizational data for cost enrichment.

Amazon DynamoDB

Store complex organizational relationships and metadata. Use DynamoDB for fast lookup of organizational information during cost processing.

AWS Lambda

Implement automated organizational information enrichment. Use Lambda to process cost data and add organizational context.

AWS Glue

Transform and enrich cost data with organizational information. Use Glue for large-scale data processing and enrichment workflows.

Amazon S3

Store organizational data files and enriched cost datasets. Use S3 for scalable storage of organizational metadata and processed cost data.

Implementation Steps

1. Define Organizational Data Model

Identify organizational information needed for cost analysis
Design data model for organizational metadata
Define relationships between organizational entities
Plan for data model evolution and maintenance

2. Collect Organizational Information

Gather organizational structure and hierarchy data
Collect financial and operational metadata
Integrate with HR and financial systems for organizational data
Establish data quality and validation procedures

3. Implement Data Enrichment Pipeline

Create automated data enrichment processes
Implement data transformation and mapping logic
Set up data validation and quality assurance
Create error handling and exception management

4. Integrate with Cost Data

Combine organizational information with cost and usage data
Implement real-time and batch enrichment processes
Create enriched datasets for analysis and reporting
Set up data lineage and audit trails

5. Create Enhanced Reporting

Build reports and dashboards using enriched data
Implement role-based access to organizational cost data
Create automated reporting with organizational context
Set up alerting based on organizational dimensions

6. Maintain Data Quality

Implement ongoing data quality monitoring
Create processes for updating organizational information
Set up validation and reconciliation procedures
Establish data governance for organizational metadata

Organizational Data Model

Hierarchical Structure

Financial Context

Operational Context

Data Enrichment Implementation

Organizational Data Storage

Automated Enrichment Pipeline

Business Intelligence Integration

Enhanced Reporting with Organizational Context

Data Quality and Governance

Organizational Data Validation

Common Challenges and Solutions

Challenge: Incomplete Organizational Data

Solution: Implement data collection processes from multiple sources. Create default values for missing information. Use automated data discovery and inference. Establish data governance processes for maintaining organizational information.

Challenge: Organizational Structure Changes

Solution: Design flexible data models that can accommodate changes. Implement versioning for organizational data. Create automated processes for detecting and handling structure changes. Maintain historical organizational context.

Challenge: Data Integration Complexity

Solution: Use standardized data formats and APIs. Implement robust data transformation and mapping logic. Create comprehensive error handling and validation. Use managed integration services where possible.

Challenge: Performance Impact of Enrichment

Solution: Optimize data processing pipelines for performance. Use appropriate caching strategies. Implement parallel processing where possible. Consider using managed analytics services for large-scale processing.

Challenge: Data Quality and Consistency

Solution: Implement comprehensive data validation and quality checks. Create automated data quality monitoring. Establish data governance processes and ownership. Use data lineage tracking for audit and troubleshooting.