Building a Data Warehouse and Analytics Capabilities Challenge

Challenge

Common App is a not-for-profit member organization committed to the pursuit of access, equity, and integrity in the college admissions process. Each year, more than one million students, one-third of whom are first-generation, apply to college through the Common App’s online application. In January 2019, the Common App united with Reach Higher, the college access and success campaign started by former First Lady Michelle Obama during her time at the White House. Founded in 1975, Common App serves over 900 member colleges and universities worldwide. A key component of Common App’s commitment to ”access, equity, and integrity” relies on data to identify students that qualify as underprivileged, typically defined as those with low income, minority status, or first-generation college applicants. To best unlock this data and begin to analyze it, Common had to overcome challenges that many organizations face:
  • Disparate systems and sources of data, making it time consuming to retrieve the necessary data required to satisfy the needs of internal and external constituents
  • Insufficient capacity from staff to deliver on internal and external requests for reporting
  • Lack of software, infrastructure, and tools to support data warehousing and analytic needs
  • Shifting the organization towards a data-driven mindset to measure performance and drive decisions

Solution

Infinitive supported Common App in developing a strategy and plan to build an initial version of the data warehouse and analytics capability including:
  • Interviewing internal and external constituents to understand their needs and the questions that could be answered by available data
  • Analyzing and prioritizing constituent needs to provide quick wins and a roadmap for analytic capabilities
  • Architecting, securing, and implementing AWS environments and services to provide a scalable and extensible foundation for the data warehouse and analytics ecosystem
  • Executing an agile process to iteratively ingest data sources and visualize the data utilizing AWS QuickSight
  • Providing ongoing change management and training to ensure adoption and maintainability of the solution
  • Engaging executives, stakeholders, and constituents throughout the project to ensure delivery of the highest value needs

Outcome

A cost-effective, secure, scalable, and extensible AWS-based solution that:
  • Centralized core data and external data sets in a secure and economic manner using AWS services including: DMS, Lambda, CloudFormation, Glue, S3, Athena, DynamoDB, and Redshift
  • Automated data pipelines to ingest data updates to reduce ongoing operations and maintenance needs while meeting GDPR and CCPA requirements
  • Leveraged AWS AI services, Textract and Comprehend, to extract raw text from PDF files to address higher ed analytics use cases
  • Simplified access to organizational KPIs regarding trends toward meeting the mission in near real time
  • Significantly reduced the amount of time required to produce standard reports and dashboards through self-service, on-demand reporting capabilities, versus former manual processes that took up to two weeks
  • Supported early adoption of the data warehouse environment which has already provided demonstratable results for both external research and internal operational use cases