Association Management Platform Streamlines Financial Data with AWS
An association management company was looking to leverage AWS resources to collect, refine, and extract accurate financial data. This POC focuses on Stripe transaction data, with the possibility of expanding to other payment services in the future.
Industry
Nonprofit
Teams & Services
Tech & Tools
AWS, Snowflake, Airbyte, On-Prem SQL servers, Hubspot, Stripe, Github
Key Data Points
The Vision
As this association management company expanded in the payment processing space, its existing financial reporting infrastructure could no longer keep up. Leadership envisioned a cloud-native data architecture that could collect, refine, and surface accurate financial data in near real time, eliminating manual validation processes and replacing unreliable pipelines with a single source of truth. The foundation would start with Stripe and scale to support additional payment services as the business grew.
The Goal
The objective was to prove that AWS could power a reliable, automated financial data lake capable of solving the company's most pressing reporting challenges. The POC aimed to consolidate Stripe transaction data across parent and child accounts, resolve discrepancies arising from unsettled ACH transactions at month-end, and replace manual Excel-based validation with automated data quality monitoring. Success meant delivering a functional QuickSight dashboard that gave the finance team accurate, real-time visibility into payment data and a clear path toward broader adoption.
The Challenge
With expansion in the payment processing space, this company was finding it more challenging to build accurate financial reports in near real time. They currently use a custom PowerBI dashboard for their GZpay revenue. Their Stripe data pipelines are unfortunately unreliable due to transactions not showing up for prolonged periods, as well as unsettled ACH transactions at the end of month, which can create large discrepancies in reporting.
The association management financial team currently does manual validation of financial data with custom Sigma reporting from Stripe, that is copied over into Excel and compared against numbers being reported in PowerBI. In addition to inaccurate financial reports, the company had other issues linking child-level accounts in Stripe to their parent accounts in HubSpot.
The Solution
Protagona built a data lake and QuickSight dashboards as a POC for new Stripe financial reporting. This data lake takes into account merging child accounts in Stripe, and linking them to a unique customer ID in HubSpot.
Additionally, forecasting was implemented to account for ACH transactions that had not yet settled, to remove that discrepancy from their current reporting. Other items included in the data lake were a data validation framework that detects schema inconsistencies, automated data quality monitoring alerts, and QuickSight AI-driven insights.
.png)