Technology Category
- Application Infrastructure & Middleware - Data Exchange & Integration
- Functional Applications - Enterprise Resource Planning Systems (ERP)
Applicable Functions
- Quality Assurance
Use Cases
- Personnel Tracking & Monitoring
- Visual Quality Detection
Services
- System Integration
- Testing & Certification
About The Customer
The customer in this case study is a major financial and leasing provider. They were dealing with the challenge of managing over 27 million records across two legacy systems. The presence of a large number of duplicate records was affecting their business operations and increasing their overhead costs. They were also facing compatibility issues among the legacy systems, platforms, and databases. The simultaneous running of both systems was causing slow processing speeds and necessitating an increase in staff. The company needed a solution that would not only migrate their data but also consolidate duplicate records and update and purge outdated or deficient data.
The Challenge
The company, a major financial and leasing provider, was grappling with the challenge of managing over 27 million records across two legacy systems - Microsoft Dynamics NAV and Target. The presence of a large number of duplicate records was affecting business operations and increasing overhead costs. The simultaneous running of both systems was causing slow processing speeds and necessitating an increase in staff. Furthermore, the company was facing compatibility issues among the legacy systems, platforms, and databases. The daily management of critical data assets was often met with these compatibility issues, further complicating the situation.
The Solution
To address these challenges, a detailed data migration solution was developed using CloverDX. The solution aimed to migrate all the data, consolidate duplicate records, and update and purge outdated or deficient data, causing minimal impact on business during the transition. The solution included built-in data cleansing checks and dedup capabilities. These data quality tools and a set of rules were used to filter outdated and inconsistent records, reducing the data volume by 25% before the migration began. After the cleansing process, the cleansed data set was enhanced with supplemental information from both units. The final data was then transferred into a new SAP ERP system, replacing the two outdated ones.
Operational Impact
Quantitative Benefit
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