Venerable Used Datalore to Reduce Eight-Hour Excel Analysis to Two Minutes
Caught in a spiderweb of spreadsheets
Like many financial organizations, Venerable faced hurdles due to its dependence on Excel. Changes in a single spreadsheet often triggered a chain reaction of adjustments across countless others, leading to mistakes and a lot of manual work. This setup resulted in a time-consuming, error-prone process that was amplified when these spreadsheets were used as input for other teams' processes.
"One of the major problems facing our effectiveness was this interrelated web of spreadsheets. Now imagine one of these spreadsheets gets an error and you didn't realize. So three spreadsheets later, now you have to go back, fix that sheet, and all the other sheets that relied on it, and hopefully, you didn't send it out internally or, even worse, externally."
— Alexandria Morales-Garcia, Investment Risk Analyst at Venerable
Furthermore, Venerable’s internal landscape was also experiencing shifts. Classic business personas gave way to “citizen developers”, as finance experts increasingly took up roles of data scientists and quantitative analysts. These citizen developers included data scientists, quantitative analysts, and stakeholders who wanted more than mere manual spreadsheet updates. Their aspirations included automating processes, reducing errors, streamlining workflows and upgrading data analysis methods, maintaining data autonomy, ensuring data accuracy, and facilitating collaboration.
"Our partners are changing. It's really no longer just a classic business person who is sitting at a desk and working. They are becoming what we're calling 'citizen developers'. They need autonomy, collaboration, and they need to work in a very safe way. We knew we needed a solution that allows them to do that, and spreadsheets were not a good tool for that."
— Steven Skarupa, Enterprise Architect at Venerable
In analyzing their organizational requirements and user needs, Venerable discovered they needed to shift from their existing standalone Excel model to a more comprehensive, real-time, and scalable tool. A solution that wouldn't require formal IT projects but could offer a semi-automated way to help them break free from the clutches of spreadsheets while satisfying stakeholders' growing needs.
Transitioning from manual Excel processes to automated Datalore notebooks
Recognizing Jupyter notebooks technology as a crucial component missing in their data workflow, Venerable turned to Datalore – a collaborative data science platform that is based on top of Jupyter technology.
Datalore equipped Venerable's “citizen developers” with key features, enabling a smooth transition from Excel. These features include:
- Low-code features: Datalore gave Venerable quick access to data quality metrics with the Statistics tab, while the Chart cells feature made visualization creation effortless, requiring no code. The Report Builder simplified the generation of reports, and the Scheduling feature automated recurring analyses, removing the need for manual updates.
- Support for SQL, Python, and R: Users can work in their preferred languages and perform data retrieval and research using a single tool.
- Pre-configured data science environments: Datalore came pre-installed with most Python and R packages, providing users with automated access to CPU and GPU machines.
- Security and compliance: By installing Datalore in their AWS account, Venerable centralized its architecture. Along with this, the single sign-on (SSO) authentication built into Datalore ensured that Venerable's data governance requirements were fulfilled.
- Team collaboration: Workspaces in Datalore provided a central hub for code, documentation, data, and visualizations. The real-time collaboration feature allowed multiple team members to work together on the same project.
- Integrated versioning: This feature simplified the tracking and management of changes, minimizing inconsistencies and promoting efficient workflow management.
"With Datalore, I am able to use the Statistics tab to make sure all of the data is loaded correctly and find any errors before diving deeper into my coding processes."
— Alexandria Morales-Garcia, Investment Risk Analyst at Venerable
Achieving measurable productivity gains
Adopting Datalore into their workflow allowed Venerable to automate routine tasks, effectively freeing up valuable analysts’ time and allowing them to focus on more pertinent tasks. Datalore enabled a seamless transition from Excel to a more advanced data analysis strategy using Python and SQL. Importantly, it also fostered collaboration within the team, enhancing productivity and coordination.
One significant area of improvement was the company's cash flow review. Previously, the process took eight hours to generate hundreds of plots. With Datalore, the task was reduced to just a few minutes of a Datalore report computation with only relevant metrics and valuable insights.
8 hours
To complete a manual Excel-based process of a cash flow review
2 minutes
To run a Datalore notebook and update an interactive report
"I was able to reduce our runtime from 8 hours to just a couple of minutes. This efficiency could be experienced across many of our operations, improving our data quality and saving massive amounts of time."
— Alexandria Morales-Garcia, Investment Risk Analyst at Venerable
By modernizing its analysis process with Datalore, Venerable has established greater operational efficiency, encouraged team synergy, and, most importantly, improved data quality, reinforcing its commitment to lead and innovate in its field.
Want to learn more details and hear this story from the Venerable team? Watch this pre-recorded webinar.
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