Institutional Analytics

Institutional Research develops and advises on analytics and business intelligence solutions for executive leadership, colleges, and administrative units at Augusta University. We help identify data needed for planning and decision-making, coordinate with data stewards of source systems, and connect with appropriate IT resources to ensure timely, consistent, and accurate data delivery. We also perform data analysis to help uncover the “why” behind the numbers.

Uses for Analytics Tools

Ad Hoc Query

 Ad-Hoc Query

  • Internal reporting
  • Data Exploration/Analysis
  • Lists for survey populations
Data Presentation

 Data Presentation

  • Interactive dashboards
  • Scheduled reports
  • Data "mashups"
Data Governance

Data Governance 

  • Data element dictionary
  • Report & data resource catalog


Data Sources

Official Census (Snapshots)

Official Census (Snapshots) 

AU Data Warehouse

AU Data Warehouse 

Cloud Hosted Data

Cloud-Hosted Data 

File Sources

File sources (Delimited Text, Excel, Access, XML, etc.) 


 Other RDMBS

Current Tools



Data Cookbook


AU Data Warehouse

AU Data Warehouse


 AU Data Warehousing Development Cycle

IR IT Graphic

The AU Data Warehouse is an important part of our analytics toolkit. Founded in 2006, this database is the product of close collaboration between IR, data stewards, and the ITS University Analytics team.

We regularly extract data from a variety of authoritative systems – including Banner, InfoEd, and PeopleSoft HR and Financials -- to populate the warehouse database. Unlike those data sources, the data warehouse is optimized for reporting trends and summaries across multiple institutional data domains. Data are structured for fast retrieval. Calculations and other derived data elements are defined consistently and adhere to institutional reporting standards.

Data warehouses are never “finished”.  They evolve through iterative development to address changing institutional needs. IR leads the data warehouse change process. We evaluate and prioritize efforts, collaborate with data stewards to ensure proper definitions and sourcing, and coordinate with ITS to validate accuracy and performance. A typical change request follows the cycle illustrated here. 

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