2019 HEDW Conference – University of Michigan

16th Annual HEDW Conference
April 14 – 17, 2019
Hosted by University of Michigan
Sheraton Ann – Arbor, Michigan

***Conference information available on the guidebook app – Search for 2019 HEDW Conference ***

The Higher Education Data Warehousing Forum is a network of higher education representatives dedicated to sharing knowledge about data management, best practices, data warehousing designs, institutional reporting strategies and more. HEDW members are technical developers, data access and reporting systems administrators, data custodians, institutional researchers and consumers of data representing a variety of internal university audiences. The annual conference sponsor is HEDW and is open to attendance by all interested parties employed in the focus area.

The 2019 HEDW Forum Conference includes three days of sessions and events with an optional Sunday Pre-Conference Training. Typical conference topics include data governance, BI strategy, data-informed culture, data quality, metadata and data definitions, data modeling, and project and program management, among other topics. The majority of sessions are prepared and presented by higher education peers, offering attendees a unique prospective.

The 2019 HEDW Conference is SOLD OUT!  If you have questions about HEDW, please be sure to access the Contact Us form and send us a message.

Presentations

2019 – “Super User” Communities: An Extension of the IR Office
2019 – 28 Months Later – A Data Modeling Journey Through Knowns and Unknowns
2019 – A Student Data Framework
2019 – Advance data-informed decision making on U-M Campus
2019 – Adventures in Data Modeling Part Deux – Retention Model Expanded
2019 – Architecting (Logically) for Student Success
2019 – Automating Your Data Warehouse Lifecycle Using Azure DevOps
2019 – Avoid Surprises on Census Day—Track Retention Daily!
2019 – Big Ten Data Governance’s Journey – A Panel Discussion on Successful Collaboration
2019 – Bridging the Gap: Connecting the Path Between Data Warehouse and Analytics