2. From your perspective, what will be the key skills, capabilities, and knowledge required for institutional research moving forward?
From my perspective, the landscape of institutional research is rapidly shifting from numerical reporting to predictive and qualitative insights. Technically, a strong foundation in cloud architecture, advanced SQL, and Python will remain essential and important for data analysts and data scientists. In addition, the true differentiator moving forward could be the capability to ethically deploy machine learning and generative AI to unlock unstructured data, such as student comments and qualitative evaluations, at scale.
Beyond the technical toolkit, IR professionals would also need highly developed ‘translation’ skills. We need the ability to act as a conduit between complex data models and university leadership. This requires strong data storytelling, a rigorous understanding of data governance and security (especially when integrating LLMs), and deep contextual knowledge of the higher education sector. We are no longer just reporting numbers; we are building systems that interpret nuance, so critical thinking, resilient data pipelines and ethical AI management will be paramount.
