Streamlining Excel-Based Workflows with Python Automation

For financial services firms still reliant on Excel, automating manual tasks, like spreadsheet consolidation, can enhance efficiency and data quality.

In many financial services firms, Excel remains the go-to tool for reporting, analysis, and data management. While powerful and familiar, Excel-based workflows often become inefficient when companies rely on manual processes for consolidating data, performing calculations, and validating quality. This can lead to excessive time spent on repetitive tasks, increasing the risk of human error and diverting focus from higher-value activities.

We have addressed this challenge for an investment management client, modernising their workflows without requiring a full digital transformation or abandoning their reliance on Excel. Instead, we introduced Python automation to integrate seamlessly into their existing processes.

Tackling Data Consolidation

Our client faced the time-consuming task of consolidating data from multiple Excel sheets into a master sheet on a quarterly basis. This process involved pulling data from various sources, running calculations, and preparing the final output for analysis. The manual approach not only consumed significant time but also introduced the risk of errors, potentially affecting important business decisions.

The QuantSpark Solution: Python-Powered Automation

Our team built a custom Python pipeline to automate the entire workflow. This pipeline ingests data from various sources, such as:

• APIs and databases for real-time financial and market data.
• The Capital IQ Excel plugin for up-to-date access to financial metrics.
• Existing Excel workbooks from different departments or external partners.

Once the data is ingested, the pipeline applies predefined transformations and calculations, ensuring the data is accurate and properly structured for the final output.

Prior to any data going into production, our pipeline performs extensive data quality checks using Great Expectations, a robust data validation tool. With Great Expectations, we ensure the data is not only properly formatted but also falls within expected parameters based on historical trends and business rules. If any checks fail, automatic notifications promptly alert the relevant parties to address the issue.

The final output is a master Excel sheet, which can be generated on-demand via a web application or scheduled to run automatically, such as at the start of every quarter. In addition, the same data is loaded into a Tableau dashboard for users who prefer visualisations over spreadsheets, all with minimal additional effort from the development team.

The Impact: Time Saved and Errors Reduced

By automating this process, our client saved countless hours previously spent on manual consolidation. The automated system not only reduced the time needed to generate the master sheet but also improved data accuracy by implementing rigorous quality checks before finalising the output.

An added advantage is that this approach allows the client to maintain control over their data while introducing automation where it is most valuable. They didn’t need to invest in a new platform or completely overhaul their systems—our solution integrated with their existing Excel workflows.

Key Benefits of Our Python Pipelines

• Increased Efficiency: Time spent on manual tasks like data consolidation is drastically reduced.
• Improved Data Quality: Automated checks ensure consistency, reducing errors in the final output.
• Flexibility: Users can trigger the process either on-demand or via a scheduler.
• Cost-Effective: Firms can continue using their familiar Excel tools without needing to invest in complex, expensive systems.

For companies looking to enhance their Excel-based processes without abandoning familiar tools, Python automation offers an ideal middle ground. By automating repetitive tasks and embedding data quality checks, we allow teams to stay focused on what truly matters: Analysis and decision-making.