Creating factsheets can be a time-intensive task that requires a lot of an asset manager’s bandwidth. A large global asset manager was producing thousands of factsheets every quarter. However, despite having an automated factsheet product process in place, underlying data processes were still manual. This left a lot of room for errors, which then led to the production of inaccurate factsheets and numerous client escalations. The client wanted to improve their overall process for creating factsheets from the bottom up.
Our team analyzed the client’s complete factsheet process and produced a data dictionary covering all upstream and downstream systems dealing with fund data. We then used this to create and implement a comprehensive solution that automated all the underlying data processes with multiple checks and validations built in to deliver 100% accurate factsheets.
Step 1: Looking at the current process
The client’s process for producing factsheets used manual processes for sourcing data, restructuring data, and updating factsheet structures. This left the client prone to producing error-prone factsheets, which would then have to be re-worked. As a result, our team suggested the use of specific automations to make the process error free.
Step 2: Bringing automation into the mix
After identifying which steps needed to be automated, we then implemented Publishwise along with specific custom automations for each step.
Sourcing Raw Data: During the data sourcing process, we ensured that automated data feeds could be extracted from multiple sources and formats (MS-Excel, CSV, APIs, etc.), and would then go through an automated data validation and processing check.
Restructuring Data: Once the data has been sourced and matched, it would then identify ‘Missing structures’ and ‘Missing values’ and send an auto-generated Email to the production team and data owner for missing data/structures.
Updating Factsheet Structures: Final data files were generated and re-checked for missing data and structures and would then automatically display fund names and field names that do not have data.
Re-working and automating the complete process of manual data handling helped the client reduce turnaround time for data processing from 2-3 days to nearly real-time processing. This allowed the client to have more bandwidth for other value-adding projects and tasks that would have taken them a lot of time. The speed of the factsheet process was also propelled by the multiple layers of automated validations that streamlined and accelerated the final proof-reading process, enabling the team to identify and rectify errors before the factsheets were even produced.
100% Data Accuracy
Reduced Turnaround Time
from 2-3 days to nearly real-time data processing
for other value-adding projects and tasks that the team can focus on
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