Case Study

Optimizing Data for Seamless System Migration

Challenge

Our client faced significant master data issues during a system migration. The existing system was cluttered with extraneous information, lacked clear designation of primary data points, and had ambiguous ownership details for various data sets, including supplier owners and primary addresses. This disorganization posed a substantial risk to the accuracy and efficiency of the migration process.

Objective

To ensure a smooth migration, the goal was to cleanse and enrich the existing data, making it complete and accurate, to meet migration requirements and eliminate errors in the new system in four months.

Industry: Healthcare

Approach

To address the data issues, AffinityCore proposed a multi-phase approach focused on data cleansing and enrichment:

Implementation:

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    Cleansing Phase:

    The client exported the data from the old system, and AffinityCore’s team meticulously updated and organized over 500,000 data entries, ensuring 98% of redundant or outdated information was removed, and critical data points were properly designated.

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    Enrichment Phase:

    Ambiguous data was clarified and completed by cross-referencing with available system documents, resulting in a 95% reduction in incomplete or misclassified records and ensuring that all required data was accurate and migration ready.

Expertise Provided by AffinityCore

Project Management

Data Management

Data Cleansing

Data Enrichment

System Integration

Process Optimization

Regulatory Compliance

Results

Improved Data Accuracy:

Data quality improved by 97%, with clear designations of primary data points and ownership details.

Streamlined Migration:

The migration process was completed with zero critical errors related to data discrepancies.

Project Completion Ahead of Schedule:

The project was successfully completed 30% faster than the client’s original four-month timeline.

Operational Efficiency:

Post-migration, the client reported a 60% improvement in data retrieval speed and a 40% reduction in manual data corrections required after migration.

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