# Automating a global asset manager's data pipeline to streamline decisions

> A global ESG-focused asset manager · Financial Services

Replacing a sprawling legacy spreadsheet with an automated data pipeline cut refresh times from hours to minutes and freed analysts to focus on investment advice.

## At a glance

- **Minutes** to refresh data, down from four to five hours by hand

## What was the problem?

The asset manager relied on a legacy spreadsheet of more than 20 sheets to calculate enterprise-value metrics across roughly 160 companies and 260 time periods. Data refreshes and logic changes were carried out by hand, leaving figures prone to error, frequently out of date and impossible to refresh in a single pass. Maintaining the tool consumed several working weeks a year and created a bottleneck in decision-making.

## What did QuantSpark do?

QuantSpark mapped the flow of data through every sheet and documented years of accumulated logic. It then rebuilt the tool as a single Python pipeline inside the client's own coding environment, drawing data directly from the firm's cloud data warehouse through SQL, replicating the spreadsheet's calculations in Pandas and uploading results to the internal database. A rigorous regression and reconciliation process matched the original outputs to within one per cent on all key metrics, with residual differences traced to the two source systems. Loggers and version control were added so the pipeline is transparent and easy for the client to maintain.

## What changed?

Data that previously took four to five hours to update by hand now refreshes in minutes, and historical records are corrected in full. Automated daily refreshes are now possible, allowing metrics to be modelled with greater sensitivity, and version control gives full visibility over every change. The legacy spreadsheet was retired, removing a long-standing bottleneck and saving several working weeks a year.

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Canonical page: https://quantspark.ai/case-studies/asset-manager-data-pipeline-automation
More about QuantSpark: https://quantspark.ai/llms.txt