Project Tools & Methodology
Fuse’s six-step HR and payroll data migration methodology: data matrix, extraction, density report, transformation, loading and validation.
Fuse Analytics Data Migration Methodology
Our proven approach for archiving sensitive data in compliance with legal requirements and privacy standards
Project Timeframes
- Small Projects: 4-6 weeks for single instance projects
- Large Projects: 4-6 months for larger, more complex implementations
- M&A Clients: Provides a repeatable process for frequent M&A clients
Project Timeline
| Step | Duration |
|---|---|
| 1. Define Data Matrix | 1-2 weeks |
| 2. Data Extraction | 1-3 weeks |
| 3. Data Density Report | 1-2 weeks |
| 4. Transformation | 2-4 weeks |
| 5. Loading | 1-2 weeks |
| 6. Validation | 1-3 weeks |
1. Define Data Matrix
Fuse and the client collaborate to create a detailed data inventory for each source system, identifying what data needs to be migrated and preserved.
- Requires access to underlying data planned for migration
- Tracks issues found during testing
- Source systems can mostly be processed independently
2. Data Extraction
Using the Data Matrix as a guide, we extract the necessary data from source systems through various methods.
- Leverage query tools and APIs to access underlying data sources
- Execute reports on the source system(s)
- Output includes data sources and attachment files (PDFs, Word docs, Excel, etc.)
3. Data Density Report
We analyze all extracted data to identify where valuable information resides and focus our efforts efficiently.
- Shows where data resides in source system structures
- Focuses efforts on where real data lives
- Identifies data privacy sensitive fields like SSN
- Allows browsing data to find inconsistencies
- Improves data quality
- Provides examples of each column and field
4. Transformation
Fuse develops ETL scripts to convert and prepare data for loading into the Fuse Archiving platform.
- Input: data files or APIs from Extraction step
- Output: Fuse system populated with transformed/staged data
- One model view created in the Fuse platform for all data and documents
5. Loading
Data is loaded into the production system and made accessible to the customer for validation.
- Data accessible via reports and employee profiles
- Typically arranged as module & file batches (Employee, Payroll, Time, etc.)
6. Validation
The final step ensures data quality and accuracy before formal sign-off.
- Customers review data in density reports, spot checks, and front-end reporting
- Fuse reviews and reloads where necessary if corrections needed
- Final customer sign-off of data scope and implementation
Tools We Bring
- Fuse platform for archiving HR and Payroll (Cloud warehouse)
- Data Matrix Templates
- Data density (profiling) tools
- ETL, APIs, RPA, Document Processing software
- Box.net as Data Transfer Service (Optional) or SFTP
Project Roles
Data Extractor
Handles extraction of data from source systems
Data Processor
Takes extracted data and transforms it into templates Fuse Analytics provides
Data Loader
Takes processed files and builds packages to load into Fuse Analytics platform
Validator
Approves data quality and completeness; clients validate with Fuse support tools
Ready to learn more about how we can help with your data migration needs?
Visit Fuse Analytics Compliance for more information