1. What is your job title?

Director, Analytics, Planning and Performance at Notre Dame University.

2. Briefly, how would you describe your role in your institution?

I oversee the enterprise data warehouse, business intelligence, reporting (internal and external), TCSI, data governance, and insights.

We have two dedicated institutional researchers in the team who collaborate to work on strategic insights, program reviews, analysis of the QILT suite, and partner with many departments across the institution to assist with developing new products.

We also have two incredible business intelligence and data warehousing specialists who manage and look after all the data, ETL, and visualisation in PowerBI.

Finally, we have two data specialists, a manager of data governance, and a data assurance specialist, all of whom I can’t do without.

3. From your perspective, what will be the key skills, capabilities, and knowledge required for institutional research moving forward?

A while ago I read a paper about institutional research moving from IQ to EQ. Data analytics and technical skills are still pertinent, however, it is the soft skills, including listening, collaborating, navigating, and understanding the political, internal, and external context involving talking to stakeholders that are coming to the forefront.

Knowledge about context is key as well, and institutional researchers still need to ensure that they read up on what is occurring in higher education; however, I believe it goes beyond this. An interdisciplinary education where you can take the key learnings from other areas is critical.

Continued knowledge of how to present data is also crucial. Far too often we see charts that we need to look at for far too long to understand the message they are portraying. Simplicity is vital.

Finally, embracing new ideas, technologies, and concepts – working with AI and finding new ways of drawing insights is a great way to increase efficiency. Having said that, the human component is still needed to ensure the correct interpretation of results. It is good at this point to have high-level insights, but IR is needed for the interpretation.