Case Study

Data Migration
Methodology

Project overview
The project was about migration and masking large-scale enterprise data across Salesforce, SAP ECC/S4HANA, Siebel, and a multitude of custom-built platforms. The goal was to securely extract, validate, mask, and migrate high-volume, sensitive data as part of a system transformation process.

An organized migration process was provided including: data source identification, metadata extraction, reconciliation, validation, test loads, and lastly production migration. High focus was laid on data quality, compliance, and sensitive data masking. Billions of records were migrated successfully in full reconciliation to our target systems to have complete data integrity, security, and confidence.

01 - About our service

Services like Salesforce, SAP (S/4HANA, ECC), Siebel offer end-to-end Data Extraction, Migration, and Data Masking for complex enterprise systems. This involves identifying possible data sources, preparing for data extraction, extracting metadata, validating data, masking, running test loads until they produce a product ready for use, and finally moving to production. This enables reliable, secure, and compliant data movement between legacy and target systems.

02 - Problem statement

In many enterprise application environments, organizations struggle with large volumes of data, inconsistent data quality, missing primary keys, and exposing sensitive data. Migrating the data across platforms like Salesforce, SAP, and Siebel also introduces challenges with data integrity issues, reconciliation gaps, incomplete metadata, and risks related to compliance due to unmasked sensitive data. The coordination of phased migrations across geographies, business units, and divisions adds complexity.

03 - Solutions & Benefits

This service helps customers migrate large-scale enterprise data securely, accurately, and efficiently while maintaining data integrity and compliance. With the use of a structured extraction and validation approach, as well as data masking, the data quality issues are fully visible to the customers and they are resolved ahead of a production migration.

Main Results and Insights of the Case Study

  • Scanned more than 890 million records through Salesforce, SAP, Siebel, and custom databases.
  • 1,444+ database tables scrutinized in Phase 1 applications.
  • Scanned 1.5+ billion records across 285 tables in Phase 2 systems.
  • Identifying and validating 58+ million unmasked data fields in Phase 1.
  • 100% reconciliation and validation before final production load.
  • In particular, protect the data of sensitive organization as per compliance and data privacy requirements by applying secure masking of sensitive data.