One of the top global fund and ETF data providers.
The client’s internal risk assessment teams had observed significant discrepancies and mismatch in the data from internal tool versus the online portal. The client wanted to validate, convert and communicate core product data to third party information providers in order to accurately represent their products.
SG Analytics formed a dedicated offshore team with domain experts and data processing analysts. This team defined the approach by discussing observed errors with client and third party information providers and ran a structured process on sample ETFs.
Based on this, the team designed a mechanism to execute the data cleanup across all funds under the scope of the activity. Our team’s mechanism analyzed probable issues such as data gaps, data triangulation issues, data variances and data duplicity.
The team structured the various data in a format that facilitated spotting erroneous data and generate alerts. Next, the team defined a resolution methodology for each of the alerts and executed in consultation with the client. Based on the resolution steps, the team wrote data-rules to process the data and then compiled the final data set for each fund product.
As a final step for quality assurance, the team verified all the data and prepared an overall resolution report of the alerts identified. The team submitted this report to the client, who then shared the data with end clients as required. For third party information providers, the team converted the data into required currencies using rule-based data automation.
Fund parameters such as daily NAV values, AuM, outstanding units, dividend, etc.
Currencies in which data is available for third party information providers.
Improved internal data processing systems by forming better processing rules.
Reduced business risks by minimizing incorrect data disclosure.
Ensured accurate and consistent data on third party information provider websites.