Data modeling can help universities measure their performance better, but there are some obstacles that can get in the way.

Here are some key challenges:

1. Data Quality Issues:

   • Sometimes, different departments use different data that don’t match up, leading to mistakes in the models.
   • It's also common to have missing information, which makes it tough to see the full picture.

2. Complexity in Model Implementation:

   • Many universities still use old systems that don’t work well with newer data models.
   • There are often not enough skilled data scientists around, so universities might end up using less experienced staff, which can hurt the quality of the models.

3. Resistance to Change:

   • Faculty and staff might not want to switch to new systems, which can result in incomplete or incorrect data being entered.
   • Training for new systems can take a lot of resources and may not be welcomed well.

To help overcome these challenges:

• Establish Data Governance: Set up clear rules to make sure data is consistent, accurate, and easy to get across all departments.
• Invest in Training: Provide thorough training for staff to help them understand data better, which will make it easier to use new models.
• Iterative Development: Use a step-by-step approach to build the data models, allowing for improvements and changes based on what works and what doesn’t.

By following these strategies, universities can greatly reduce the problems that come with data modeling.