# Deploying advanced data engineering to accelerate investment data processing

> An asset management firm · Financial Services

How QuantSpark cut an asset manager's end-to-end investment-data processing time by 70%, giving portfolio managers faster access to the data behind their decisions.

## At a glance

- **70%** reduction in data processing time

## What was the problem?

The asset manager relied on manual processes to collect, clean and validate data from a third-party consumer-spending intelligence provider. The information gave a near-real-time read on consumer spending drawn from card-purchase data, but typically reached portfolio managers several days after release, blunting its value for the time-sensitive, data-intensive quantitative decisions the managers depend on.

## What did QuantSpark do?

QuantSpark ran a three-step engagement: a data audit mapping the existing workflows to pinpoint where optimisation was needed; a data-engineering build combining Python, cloud infrastructure and automated pipelines to integrate provider data directly into interactive dashboards, implemented inside the client's own cloud environment to meet security requirements; and a rigorous quality-assurance layer with automated checks and alerts triggered whenever source data fell outside expected thresholds.

## What changed?

End-to-end processing time fell from 5 days to 1.5 days, a 70% reduction, while the technical run itself dropped from a few hours to under 30 minutes. Faster, validated data freed resources for higher-value work and let portfolio managers act on consumer-spending signals sooner.

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Canonical page: https://quantspark.ai/case-studies/data-engineering-investment-data-acceleration
More about QuantSpark: https://quantspark.ai/llms.txt