Statement of Work

Data Conversions

1. Purpose

The purpose of the Data Conversion Process is to ensure accurate, timely, and secure migration of data from legacy systems and sources into the target system(s). This process will ensure data integrity, business continuity, and compliance with project requirements.

2. Scope

This process applies to all legacy data sources identified in the project scope and covers data extraction, cleansing, transformation, validation, and loading into the designated target environment.

3. Data Conversion Phases

  • Phase 1: Data Assessment & Planning
    • Identify all source systems and data owners.
    • Define data mapping and transformation rules.
    • Assess data quality and gaps.
    • Determine data volumes and complexities.
    • Establish data security, privacy, and compliance requirements.
    • Finalize the Data Conversion Strategy and Plan.
    • Develop a detailed Conversion Schedule and resource plan.
  • Phase 2: Data Extraction
    • Extract raw data from all legacy systems and external sources.
    • Securely transfer extracted data to the conversion environment.
    • Document extraction processes, formats, and encryption methods.
  • Phase 3: Data Profiling & Cleansing
    • Profile extracted data to understand structure, consistency, and quality.
    • Remove duplicates, correct errors, and fill missing values as defined by the business.
    • Standardize data formats (e.g., dates, numeric values, codes).
    • Document all cleansing actions and decisions.
  • Phase 4: Data Transformation & Mapping
    • Apply transformation rules to convert source data formats to target system specifications.
    • Map source fields to target fields according to the approved Data Mapping Document.
    • Document all transformation logic, including conditional processing and derivations.
  • Phase 5: Data Loading
    • Perform trial data loads into a test environment.
    • Conduct initial load validations and reconcile against source systems.
    • Address any transformation errors, truncations, or mismatches.
    • Load data into the production environment following approval.
  • Phase 6: Data Validation & Reconciliation
    • Validate data completeness, accuracy, and integrity in the target system.
    • Reconcile record counts and sample data back to the source systems.
    • Conduct user acceptance testing (UAT) with business stakeholders.
    • Resolve any discrepancies and obtain formal sign-off.
  • Phase 7: Final Cutover & Go-Live
    • Freeze legacy data prior to the final cutover.
    • Perform final data extraction, transformation, and load (ETL) activities.
    • Validate post-load data and release the target system to production.
    • Conduct immediate post-go-live monitoring and validation.

4. Deliverables

  • Data Conversion Strategy & Plan
  • Data Mapping Document
  • Extracted Raw Data Files (if required)
  • Data Cleansing & Transformation Logs
  • Validation & Reconciliation Reports
  • Final Data Conversion Sign-Off Document

5. Roles and Responsibilities

Role Responsibilities

  • Project Manager Overall planning and coordination of data conversion activities
  • Data Architect Define data structures, mapping, and transformation rules
  • Business SME Validate data mapping and cleansing rules
  • Data Conversion Developer Develop and execute ETL scripts
  • QA/Test Team Validate conversion results and reconcile discrepancies
  • System Administrator Support environment setup and data load processes

6. Tools & Technologies

Specify the ETL tools, scripting languages, data profiling tools, and validation utilities to be used (e.g., SQL, Python, Informatica, Talend, AWS Glue, Power BI for reporting, etc.).

7. Risks and Mitigation

Risk Mitigation Strategy

  • Data quality issues in legacy systems Early profiling and cleansing
  • Incomplete data mapping Collaborative mapping workshops with SMEs
  • Tight conversion window Dry runs and rehearsal cutovers
  • System downtime during cutover Schedule during low-usage periods; rollback plan

8. Assumptions

  • All required legacy data is available and accessible.
  • Business SMEs will be available for data validation and sign-off.
  • Necessary system environments are provisioned and accessible.
  • Security and compliance requirements have been communicated.

9. Acceptance Criteria

  • All data loads completed without critical errors.
  • Reconciliation reports match agreed thresholds.
  • Business stakeholders provide written acceptance of conversion results.

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