We live and work in highly complex times, and institutional research is directly impacted. It used to be there was a major innovation every decade or so followed by a period of stability. Not so anymore. Today, the world is changing constantly, in large part driven by technology developments and their impacts across sectors—artificial intelligence, machine learning, automation, and more. The impacts are extensive and advance far beyond the technology itself, as we confront outcomes and equity gaps, wrestle with the declining perceived value of a college degree, and prepare ourselves for the future of work. The COVID-19 pandemic is further accelerating change, driving rapid adaptation. All of this raises multiple questions, and one of them is this: What is the future of IR?  

Let’s start by considering data collection. Do we collect the right data to support student success into the future? There are core fields we all collect (for example, gender and race/ethnicity), but are those fields truly providing the data we need? Although these data are important, there will be an increasing need to gather additional information that may be more predictive regarding student progress and outcomes. In particular, limited data collection through the admission process can create a barrier for IR offices to obtain and analyze important data points. For example, while we look at student progress and outcomes by race/ethnicity, do we all have the ability to assess student performance based on parents’ education level or their grades in high school? Those factors may be important in the success of our students, and knowing that information may improve our ability to identify interventions that will be more effective for different populations. In the increasingly competitive education landscape and, more importantly, with our shared commitment to student success, we must ensure that we are collecting the right data to support student needs, progress, and outcomes. 

The world is changing rapidly regarding technology, and there are clear impacts for IR. Timely, accurate, and clean data availability is more important to IR offices than ever, and Business Intelligence (BI) will continue to be imperative. Without BI, it is impossible to navigate and answer the data questions we receive, while continuing to function with expanded IR responsibilities. As the questions we need to address grow and expand, it will necessitate the development of more complex data modules in our BI systems. Also, we must keep in mind that BI functions are typically based on standard data definitions and business processes, and frequently generate descriptive data. It is an area ripe for broadscale automation and potential outsourcing. It’s a clear possibility that all routine data processing, cleaning, and restructuring could be constructed using rules in our data systems, with limited—if any—human intervention.  

Predictive analytics is another existing competency that we must strengthen and maintain. For example, some analytics toolkits in this space—such as Python’s TensorFlow—were only released in 2015. If we are not revisiting and refreshing our technical skills regularly in the analytics space, we will fall behind.  

Outside the traditional IR topics of BI and predictive analytics, many institutions are focusing on the four technology superpowers of artificial intelligence (AI), the internet of things (IoT), mobile technology, and the cloud. While many of us have a deep familiarity with machine learning within artificial intelligence, are we considering the implications of the IoT for our field? Beyond these disruptions, there are other emerging fields that may drive the next transition in technology of which we need to be cognizant. For example, we are carefully monitoring quantum computing to understand the implications for us. Are you? While it is not at the point—yet—where it is significantly impacting the education space, it has the potential to lead to significant disruption, and we have to anticipate that impact so we can be ready.  

Given the pace of change, what skills do we need to develop to be successful moving forward? The bottom line is that we all must adapt, learn, and change. Are you an IR professional who primarily prepares only descriptive statistics? If yes, it’s time to adapt and change to ensure you remain relevant in the future. Start the transition to analytics now and establish a practice of regularly monitoring external trends in technology and the education space to ensure you are knowledgeable about emerging trends that will impact you.  

Finally, remember that in the increasingly automated world, there are core human values that—currently—are not easily automated. For example, the absence of institutional knowledge in IR could present a key weakness. Every institution’s data landscape is unique, and sometimes it only makes sense (or not) when you have the institutional knowledge and the background to interpret the results in context. Ensure that you focus on how you can help your stakeholders connect with, understand, and apply the data. In the future, we anticipate that will continue to be a critical role for IR. 

We live in a time of monumental change. A deep understanding of our field, coupled with constant monitoring of external trends, will position us for the changes ahead. Adaptability and flexibility are key to how we approach the future, and we must seek the human value in the work we do.