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Nonprofit Data Platform Scalability and Automation Enhancement
  1. case
  2. Nonprofit Data Platform Scalability and Automation Enhancement

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Nonprofit Data Platform Scalability and Automation Enhancement

kandasoft.com
Non-profit
Business services
Information technology

Data Management Inefficiencies & Scalability Challenges

Global Aid Network (GAN) faces significant challenges in maintaining a timely, accurate, and scalable database of nonprofits worldwide. Existing data import processes are slow and prone to errors, requiring extensive manual intervention. The API needs improvement to handle growing data volumes efficiently. Data quality issues, stemming from inconsistencies in data formats and website collection complexities, further impede data accuracy and usability. The organization struggles to keep data updated, with a significant lag in data refreshes across various countries, impacting the platform's value and reliability for donors and partner organizations.

About the Client

A global nonprofit organization connecting nonprofits with donors and companies through a comprehensive data platform and aid distribution network. GAN operates in over 175 countries, managing projects, facilitating donations, and providing disaster relief support.

Project Goals

  • Improve the efficiency and scalability of data import processes.
  • Enhance the accuracy and timeliness of nonprofit data.
  • Reduce manual intervention in data management tasks.
  • Optimize API performance for increased data accessibility.
  • Proactively monitor data quality and address emerging data format issues.
  • Develop a robust and automated data validation system.

Functional Requirements

  • Automated data import from multiple sources (web scraping, APIs, data exports).
  • Data validation and cleansing rules engine.
  • Real-time data monitoring and alerting.
  • Improved API performance and scalability.
  • Automated error handling and logging.
  • Proactive data format change detection and adaptation.
  • Data exporter enhancements for seamless data extraction.

Technology Preferences

Python
API development frameworks (e.g., Flask, Django)
Database technologies (e.g., PostgreSQL, MongoDB)
Cloud-based infrastructure (e.g., AWS, Azure, GCP)
Web scraping libraries (e.g., Scrapy)

Integration Requirements

  • Third-party data providers
  • Existing platform API
  • Data visualization tools

Non-Functional Requirements

  • High scalability to accommodate growing data volumes.
  • High performance for rapid data processing and API responses.
  • Robust security to protect sensitive data.
  • Data integrity and accuracy.
  • Reliability and availability.

Expected Business Impact

Successful implementation of this project will significantly improve GAN's ability to provide timely and accurate information to donors, partner organizations, and beneficiaries. This will lead to increased trust, improved program effectiveness, and greater overall impact. Reducing data import time from 45 days to 46 hours, and lowering the total time to gather data for all countries, will free up valuable resources and enable GAN to respond more quickly to emerging needs. A more reliable and up-to-date data platform will enhance GAN’s position as a leading provider of nonprofit data and facilitate more effective aid delivery.

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