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Data Extraction & Management Challenge
A global credit analytics firm needed to improve the process of extracting data from the many disparate sources it relied upon. The mostly manual methods used were inefficient and error-prone. In addition, the extracted data was stored sub-optimally with little auditability, impacting data quality for asset management efforts.
Solution
Based on a comprehensive review of the firm’s information requirements and workflow, DataArt built a next-generation data intake and analytical platform.
It features:
- Automated data extraction from tabular and textual sections of PDF financial reports
- Data mapping to a standardized taxonomy, with accommodation for non-standardized data, ensuring improved data quality for asset management
- Greater availability of services and resources (built-in disaster recovery)
- Custom processing engine incorporating machine learning to support advanced asset management analytics
- Full auditability of data back to the original source, maintaining transparency and improving data quality for asset management
- Harmonized platform including a web based user interface
Benefits
- Quicker processing yet twice as precise as the previous system.
- Non-standard data captured and available for analytic purposes, enhancing asset management analytics capabilities.
- More efficient taxonomy mapping, critical for maintaining data quality for asset management.
- Harmonized platform is ‘shovel ready’ for other business lines.
- Reduced risk and increased regulatory compliance due to robust audit features that support data quality for asset management.
