Alternative Data
Provider of Third-Party Financial Reports to Brokerages
A leading provider of financial reporting services to brokerages, faced significant challenges related to data integrity and reliability in their reporting processes. With a system reliant on third-party data streams to compile quarterly reports on all publicly traded stocks, maintaining high data quality was critical. Inefficiencies and errors in third-party data streams were impacting the accuracy of financial reports, potentially leading to misguided investment decisions.
The project aimed to enhance data quality controls within the firm's existing infrastructure. By developing sophisticated data quality reports, the goal was to identify and correct outliers and errors in third-party data streams, ensuring high standards of data integrity and accuracy.
Team
Data Analysts, Software Developers, Quality Assurance Specialists, Project Manager
Python for scripting and data analysis.
Apache Spark for handling large-scale data processing.
SQL for data querying and manipulation.
Third-party data errors. Inconsistent data quality and frequent errors from third-party data providers led to inaccuracies in financial reporting.
Detecting outliers and anomalies. Existing systems lacked the capability to effectively identify and manage outliers and anomalies in data streams.
Impact on client trust. Data integrity issues were eroding client trust and jeopardizing company's reputation as a reliable data provider.
Data quality framework development. Established a comprehensive data quality framework that included automated checks and balances to scrutinize incoming data streams.
Outlier detection system. Implemented an advanced outlier detection system using Python and Spark, which automatically flagged data points that deviated significantly from expected patterns.
Regular data quality reports. Introduced regular data quality reporting that provided insights into data integrity, pinpointed recurring issues, and guided corrective actions.
Results
Enhanced data accuracy. Significantly improved the accuracy of financial reports by reducing the incidence of data errors and anomalies.
Improved operational efficiency. Streamlined the data verification process, reducing the time and resources required for data cleanup.
Restored client confidence. Bolstered client trust through demonstrably higher standards of data reliability and regular transparency in data quality.
Lessons
Reinforced the critical role of data integrity in financial services, where data errors can have substantial repercussions.
Learned the effectiveness of automating data quality checks to proactively manage large datasets and prevent issues before they affect end-users.
Highlighted the need for closer collaboration with third-party data providers to enhance data quality at the source.