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Data Governance Transformation at SEA's Largest Digital P2P Lending Platform
Technology Category
- Application Infrastructure & Middleware - Data Exchange & Integration
- Application Infrastructure & Middleware - Middleware, SDKs & Libraries
Applicable Industries
- Buildings
- National Security & Defense
Applicable Functions
- Quality Assurance
- Warehouse & Inventory Management
Use Cases
- Inventory Management
- Object Detection
Services
- System Integration
- Testing & Certification
The Challenge
Funding Societies | Modalku, a licensed digital peer-to-peer (P2P) lending platform in South East Asia, faced several challenges related to data governance due to its regulatory and compliance requirements. The company recognized the increasing value of data and its potential to provide significant competitive advantages in the FinTech world. However, without high-quality data and upward reporting of meaningful management information, the company was unable to identify and monitor risks or understand the performance of various business functions. The company faced daily operational and regulatory challenges, including understanding and classifying data, applying flexible governance and security policies, and integrating across different applications. The company also needed to ensure data was organized, accessible, and compliant.
About The Customer
Funding Societies | Modalku is a licensed digital peer-to-peer (P2P) lending platform in South East Asia. The company is subject to several regulatory and compliance requirements which factor into its data strategy. The company recognizes the increasing value of data and its potential to provide significant competitive advantages in the FinTech world. However, the company also recognizes the challenges associated with data governance, including understanding and classifying data, applying flexible governance and security policies, and integrating across different applications.
The Solution
To address these challenges, Funding Societies | Modalku adopted the DAMA — Data Governance Framework. The company recognized that data management and governance was not a one-time project, but a journey to become data-centric. The company started by getting buy-in from leadership and defining the policies for data governance, data classification, data loss prevention, and access control. The company then created an Information Asset Inventory, identifying the Critical System, Processes, Data, and Assets. The company also set up a domain-driven architecture, assessing the existing data warehouse (Snowflake) architecture and restructuring it to domain(data) driven layered architecture. This framework helped the company categorize the datasets and figure out how the data will be stored, processed, and managed. The company also started the process of curating a business glossary after defining the data domains and subdomains with the respective owners.
Operational Impact
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