South Africa Data Warehouse for PEPFAR Presented by: Michael Ogawa Khulisa Management Services

Context PEPFAR South Africa  USG-funded  Over 100 implementing partners, many with multiple sites  Need to monitor up to 80 indicators twice a year  2008 Targets for SA Prevent 1.75m HIV infections Treat 500,000 AIDS patients Care for 2.5m OVCs & HIV infected individuals

Monitoring and Evaluation M&E provides strategic information to USG and implementing partners to:  Plan and monitor programs;  Document and report on progress; and  Ensure that resources are used effectively and efficiently

Challenges SA PEPFAR program has a large number of partners who need to report and plan  Reporting burden on USG MS Word and MS Excel templates were historically used for capturing reports and annual plans Office of The US Global AIDS Coordinator (OGAC) regularly makes changes to the PEPFAR indicator model Access to the Internet varies among partners

SHARING Website Secure web portal for accessing:  Web-based forms for reporting and planning templates  Indicator definition guides  Data extracts (CSV files)  Adjustment tool to eliminate double counts  User administration  Budget/target grids

Solutions Web-based data warehouse designed using Open Source software  Only Internet browser software required  Data warehouse can be accessed from anywhere in the world (some partners have parent organizations outside of SA) Forms are database generated  RTF downloads (paper forms)  HTML interface (web forms) “Live” extracts for reporting indicators  Reduced reporting burden for USG Content Management System  Indicator definitions  Track changes for planning

Solutions Speeding up web page loading  Web page data are compressed before upload  Data for a web page is separated so that formatting information is not repeated One file contains formatting information Second file contains data that needs to be formatted  Asynchronous saving of data Users can carry on capturing data while the save for a previous page is being processed Users do not have to wait for new form pages to load

Data Warehouse Approach On Line Analytical Processing (OLAP)  Measures Numeric facts (e.g. No. of condoms distributed)  Dimensions Categories under which measures can be grouped (e.g. Gender, age, etc.)  OLAP Cubes OLAP cubes allow users to quickly retrieve queries and reports on summary data for the organization The summary information being retrieved typically aggregates detailed transactions at a lower level The aggregated totals stored in the OLAP cube can represent thousands of rows of transactions within the data source from which it originates

Data Quality Improvements Input data is checked on client side (cannot enter non- numeric data into a numeric field) Full transactional audit history Data must be verified before it is submitted Forms locked after submission Forms can be invalidated for changes by partners Double count adjustment tool adjusts aggregate total but does not adjust partner data System designed to capture down to site level (source data) and at monthly periods

Future Features: Pivot Table/Cube Viewer

Future Features: Online Mapping

Future Features Electronic transfer of data  From partner systems to data warehouse  From data warehouse to USG system in Washington and SA health information systems Offline data capture Customizable indicators/dimensions for partners Workflow

Lessons Learned If time is available, understand your user base  Web capturing interface = paper form Data warehouse needs to move beyond its current role as a one-way conduit for USG results reporting  Need to improve data utilization tools for complex, analyses and basic, menu-driven reports, graphs and maps Need more features (RSS feeds, warehouse more health data sources, etc.) to bring users to the website on a regular basis

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