3. From your perspective, what are the emerging areas of interest in institutional research?

• Large-scale data integration: One of the major challenges for me has been wrangling multiple sources of data to derive meaningful analysis and insights. Our business intelligence (BI) mantra is to ‘transform the university’s information into a meaningful intelligence asset, available anywhere, anytime and on any device’. There is huge analytics potential using the data we already have, while making this information available when and where people need it. Sometimes it’s not the best analysis that’s used, it’s simply the one that’s available. For me, the relationship between time and value in data is always a pragmatic concern.
• Machine learning and automation: The robots are taking our jobs! Well, maybe not entirely, but I think automation is having a massive impact on the changeover of skills for institutional researchers, which at the same time opens many new doors for deep analysis. There is a lot to be excited about in machine learning, institutional researchers can be at the forefront of implementing this in their institutions.
• Adding value: Like all corporate services, the provision of information, data and BI comes at a cost. A major focus for us has been the idea of ‘adding value’. We do this through being proactive and more deeply understanding user needs, while striving to work in partnership. Our value proposition (‘to be a vital source of business intelligence and a strategic partner in planning, quality and risk’) is an enduring aspiration, but it’s also always at risk, because needs and expectations are continuously evolving. The changing nature of higher education policy means we need to stay ahead of the curve and continue to ask ourselves: what value does this add, what’s the return on investment, who are our clients, is there a better way, can this be automated?