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I used Mito to automate a critical monthly reporting process that is sent to the C-suite. This was my first foray into Python, but I’m now Python-literate, and have continued to automate other manual reporting processes throughout my team.
Greg, Analyst in Wealth Management at large North American Bank
A Critical Monthly Deliverable
Every major bank is required to track and report how well their various funds are performing, and compare these funds to benchmarks.
As an analyst at one of the largest North American banks, Greg’s team was responsible for building these benchmarks. To do, they used a 10-year-old Excel file with 200+ tabs to track fund performance over time. This report was updated monthly, and was distributed to all asset managers and bank leadership.
Due to the complexity and size of the Excel file, opening the file took minutes and required a dedicated virtual machine. Updating the file took multiple hours per month, and required lots of manual review to avoid human error.
We didn’t just automate this process to save time. It was either we automate this process or this Excel file was going to just grind to a halt due to its size. We could barely save a new copy of the file without encountering issues.
So Greg decided to use Python, the first-choice-language for data work, to handle the increasingly large datasets, reduce the chance for human error, and save his team two days a month.
Automating the Process with Mito
Greg had never written Python before, so he decided to automate this process with Mito. By using the Mito spreadsheet, Greg wrote the Python code to automate this benchmark reporting without having to go through a multi-week Python training course - he could just use an interface that is similar to Excel.
His automation connects to up-to-date databases, pulls the required data, creates the benchmarks, and compares each fund to it’s benchmark.
Updating the benchmark calculations for the new month now takes less than a minute. It also reduces the chance of a manual error.
To Automation and Beyond
Since automating this use case, Greg has been using his new-found Python automation skills daily.
It’s not just that I saved my team time automating this single use case. I am now using Python and Mito to automate more deliverables my team currently handles manually. We have the whole department on Mito!
With this use case as a success story, Greg’s team is now increasingly bought-in on Python - and Greg’s teammates are now starting to transition to Python using Mito as well.
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