APPA Informatics Workgroup
October 15, 2015 APPA
I.
Purpose
CFTA was invited to participate in the APPA Informatics Workgroup. Job Gibb, Dixie State
University Facilities Planning Director, is serving as a CFTA representative and attended the first
workgroup meeting in Chicago. This document recaps Jon’s participation at the meeting.
II.
Attendees
Chris - University of Virginia (standing and is co-chair)
Marcus - University of Texas Austin (to Chris’ left and is co-chair)
Craig - Clarkson University
Paresh - University of Kentucky
Andrew - University of Kentucky
Douglas - Simon Frasier
Sue - University of New Mexico
David - Arkansas State University
Jon - DSU
Roy - Kent State
Sam Waymire - APPA
Mark - University of Virginia
Jerry - University of Iowa
Ted - Purdue
Lalit - Nebraska
John - APPA
Robert - University of Alberta
APPA Informatics Workgroup
October 15, 2015 APPA
III.
Introductions
I introduced myself as a representative of CFTA. I let the group know that as an institution I
looked for benchmarking resources with regards to GIS implementation on our campus and
found CFTA as an incredible resource. I let them know that Michelle is the current president and
interested in the opportunities that CFTA and APPA have in a relationship. John mentioned that
he is working with Michelle and some ideas.
IV.
Informatics Origination
Marcus and Chris were at the APPA Thought Leaders Symposium in Austin this past summer and
tasked with the concept of data and what APPA could do with it and, as a result, were asked to
co-chair this informatics work group. They want this work group to overlap tech, business
knowledge and curiosity.
V.
Group Tasks
We were divided into four groups with different tasks:
a. Data – standards and data sets. Provide a central reporting tool with APPA for
benchmarking
b. Maturity model – case study, road map, templates. Create a matrix of data categories
and with that define how mature an institution is.
c. Predictive – curious investigation and a business case for informatics. Look at current
best practices, use examples.
d. Virtual University – living lab with data, deploying data and sharing on a simple size
which would grow out to a larger community. Allowing simulation trials with data to see
what happens if a decision is made.