One of the advantages of a project such as this is the opportunity to take a fresh look at business practices or, simply, how we do things. One of the hurdles we face is making sure the data are clean across multiple systems and platforms.
For example, we’re finding folks with multiple netid’s versus no netid. New employees, for example, may be “in the pipeline” but aren’t far enough along to have a netid generated. That holds up our posting, among other things, a new faculty member’s syllabus for a class. We continue to find others who still have more than one netid, a holdover from a previous process whereby a student who became an employee (or vice versa) could wind up with an extra netid.ÂÂ
Similarly, we find that with the use of multiple data entry points for, among other things, class schedules and/or faculty course assignments, occasionally something slips through the cracks. We might have cross-listed courses that are not so marked (they meet in the same room at the same time with the same instructor, but the classes aren’t marked), and we occasionally (still) find a 6xxx class cross-listed with a 3xxx class, and each of those instances must be reviewed to determine whether or not that is really a cross-listing or a mistake or a legitimate use of coding.ÂÂ
We’re making some basic assumptions about how data can be used, as well. For example, if a class is a “lecture-based” class, we assume there’s a syllabus to be found. If a class is an independent study, we assume there is likely not a syllabus. Usually that works; sometimes it doesn’t. That’s not so much a matter of clean or dirty data; rather it’s simply about how people interpret their own workloads.
At least with a new set of eyes viewing all the data sources, we’re finding folks are generally welcoming the fresh input.  All we really want is good data with which to work; it makes all our work lives simpler.ÂÅ