In continuation of our year-end Q&A series, we asked one of our most dedicated Ed-Fi Community members, Daniel Retzlaff of the Wisconsin Department of Public Instruction, recipient of the 2017 Technical Team of the Year award, to share his proudest accomplishments of 2017, as well as what it is top-of-mind for him as we approach the new year.
Through our WISEdata project In Wisconsin, we are using a continuous integration methodology with our implementation of the Ed-Fi API. The Ed-Fi API data pipeline we implemented has now been running successfully for over a year, starting with data collections, such as enrollment, attendance, SPED, and high school completion. This year, we implemented our roster and discipline data collection and now have 19 certified vendors integrated with our Ed-Fi system! The vendors we work with include not only Student Information Systems, but also those that provide LEAs with specialized products in special education and discipline. Additionally, we’re working with vendors through roster implementation, which enables districts to benefit from a truly interoperable system.
One of the greatest accomplishments that our team has been a part of this past year has been the collaborative effort within the Ed-Fi Community to resolve performance issues discovered in the software. Of note, these were not trivial issues and were highly difficult to diagnose and resolve. But through a strong team effort, the Wisconsin Ed-Fi pipeline is running strong – at times handling up to 18 million transactions a day!
Looking ahead to next year, the Wisconsin team will be knee-deep in the development of using the Ed-Fi API and data model for receiving career and technical education data. This will greatly streamline processes for both LEA staff members and the WDPI.
I’m also looking forward to continuing to participate in the “API Reliability and Data Quality” SIG to create solutions through the Ed-Fi API that will improve error messaging. This will make life easier for not only vendors who provide products that feed the API, but also for users at LEAs who diagnose data quality issues.