AIR Forum Reflects Big Themes, Big Opportunities in Higher Education

The 2019 AIR Forum in Denver was the educational event of the year for higher education professionals who support data-informed decision making.

With over 300 sessions, there was something for every interest, learning style, and experience level. It’s no surprise that many engaging and inspiring themes came out of these sessions. However, what may be a surprise is that some of the most thought-provoking themes came out of hallway chats, networking events, and dinner groups with new friends.

Addressing all of the themes from the Forum is beyond the scope of this article. However, some of the most frequently heard themes are covered here:

1. The changing role of IR: How are factors like big data, access to data across departments, technology, and shifts happening at institutions themselves affecting IR?
2. Data access/data ownership: Who owns the data? Departments are doing their own research and data collection/analysis, which can cause some discord.
3. Peeking around the corner: How can IR professionals be positioned to see what’s coming around the corner, identify trends, and influence policy?
4. Equity, diversity, and inclusion (EDI): Equity, diversity, and inclusion  are increasingly part of conversations and work taking place in IR/IE offices and beyond. How do we best pursue equity-related research?
5. Artificial intelligence (AI): What do data-related professionals need to know to keep up with these new technologies?

Thank you to the following IR professionals for sharing their perspectives and insights: Eric Atchison, Arkansas State University System; Marlene Clapp, Massachusetts Maritime Academy; Maggie Dalrymple, Indiana State University; Mike Le, Humboldt State University; Bethany Miller, Cornell College; David Robledo, Purdue University-Main Campus; Craig This, Wright State University-Main Campus; Bill Tobin, DePauw University; and Minghui Wang, Stevens Institute of Technology.

The Changing Role of IR

Marlene: Like many fields, IR is not immune to the impact of technology and how it is changing the profession. Data requests that used to depend on one-off analyses can now be generated with just a few clicks on a dashboard. Some states are even creating centralized user-friendly analytics environments that provide some of the data IR offices traditionally would offer. In order to stay relevant, IR offices will need to evolve beyond simple data provision and offer expertise and guidance in using data for decision support/planning.

Maggie: A while back, we implemented a BI tool that granted wide access to the student data across campus. As expected, academics, program directors, and others could now answer their basic questions and it encouraged them to become more familiar with the data. However, an unexpected consequence has been the volume of requests for more complex research analysis. Our roles in IR have essentially evolved from reporting to researching, which is a challenging -  yet exciting - development.

Mike: The role of IR has been so stable that the roots of the field can be traced back to the first IR study in 1701 at Yale University. Some say the field is changing, I say it has changed. New and emerging technologies such as natural language processing, artificial intelligence, and big data for descriptive, predictive, and prescriptive analytics have changed the role of IR.

Bethany: Just like everything else in higher education, IR is changing and must continue to change. The advent of big data (which many institutions do not have), more access, technology, and shifts on campus necessitate an agility in IR that feels new to many people. IR has become more about access and understanding of data, data coaching, building capacity as data is more available, and providing strategic decision support to campus leadership and various divisions.

David: At Purdue, we’ve experienced a noticeable expansion of IR’s role in the data stewardship in the past couple of years. Our data environments are such that different groups manage data entry, data processing, data warehousing, operational reporting, and public dashboards. This creates a complicated data pipeline where contextual continuity is important but difficult to maintain, and by our distributed nature we end up with fragmented ownership. This opens opportunities for IR to identify and work with key stakeholders to identify and correct information quality issues.

Bill: One trend many of us have seen is the increasing need for IR professionals to be involved in program assessment. Different institutions respond in different ways. Some give most or all assessment duties to IR, others pair their IR offices with an assessment officer (or single assessment person). This is largely dependent on the backgrounds and training of the individuals involved. My institution recently designated a faculty member to work on assessment in concert with IR, which is great because faculty involvement is critical to success.

Minghui: IR is undergoing a transformation from a traditional data hub to a strategic thinktank with critical thinking and strategic planning. The concept of big data and advanced technology is rapidly changing the way data is currently collected and leveraged in my institution. It requires our IR office to rethink process and priority in light of our ability to facilitate data collection and break down silos across departments. By acquiring the hard power, we also need to gain soft power and apply human intelligence to data analytics at work.

Data Access/Data Ownership

Eric: Expanding data access shouldn’t be viewed as a problem by higher education data professionals, but rather an opportunity to educate and lead efforts by partnering with offices in utilizing the proper data elements and helping them contextualize this information appropriately. Data is one of an institution’s greatest assets (along with students, personnel, and infrastructure) and shouldn’t be held in silos. This expanding data-use network is needed, along with guidance, to provide information to a wider audience and take data usage to a deeper level.

Marlene: With both increasing amounts and complexity of data, effective data governance is more critical than ever. It is important to clarify who "owns" certain data so that everyone knows who is responsible for maintaining its integrity and accuracy.

Bethany: This is a tough conversation for people to have. The truth is that the institution owns the data. The various departments and divisions are stewards of the data. Conversations about who owns data and access are important; however, we must bear in mind that in the end everyone at the institution is on the same team. These conversations can be aided by having policies around data governance and procedures for appropriate access and use of data.

David: Data ownership can be a fuzzy topic and boundaries are not always clear. However, what is clear, is that our data environments and the questions being asked of our university and college leadership are only increasing in complexity. We are at our collective best, when IR (both central and distributed) and these central data offices have a shared sense of success. This requires a great deal of trust, communication, and partnership.

Craig: There is a phrase we use in our office: “be a traffic cop, not a homicide detective,” meaning, place emphasis on prevention and maintenance, not on what people did or how/why they did it. As more and more units gain access to dashboards and data, we have become the custodians for data governance, the data dictionary, and repositories of university metrics. We keep the traffic flowing, ensuring that people follow (for the most part) the rules and regulations.

Bill: This is particularly true of survey research, where the ready availability of easy-to-use online tools has put the capability to construct surveys into the hands of many people that before would need to rely on IR. The problem now is people asking for help after the fact with analysis of survey data where quality can vary considerably. As a consequence, my office implemented a policy whereby we have the right to decline to analyze survey data for instruments with which we did not have prior input.  

Minghui: My team has been trying to explain to other units the importance of data stewardship through collaboration. In my opinion, IR is more like a data consumer instead of a data owner for most of the institutional data. A good relationship with functional areas and proactive communication is critical on promoting data integrity and ensuring data quality on different levels of data analysis and research. A sustainable data governance structure is truly needed in our institution to facilitate data-informed decision making.

Peeking Around the Corner

Eric: I believe we have entered an era where data professionals are not only informed of the conversation, but are leading the conversation of data needs, trends, and policy issues. The profession should be ready to respond with thoughtful and informed questions about what the needs of an institution are and how we can best serve these needs with agility and depth.

Maggie: In my experience, the IR office is typically involved in many disparate projects across campus. A positive aspect from this involvement is hearing about the different trends and initiatives and then being able to share that information and creatively modify those initiatives to positively impact another project.

Bethany: This is an important time in higher education, and IR professionals cannot afford to sit on the sidelines and take whatever comes without having any voice in the conversation. We need to be visible not only on our campus, but in the field and policy conversations. The Higher Education Act (HEA) Reauthorization and the College Transparency Act (CTA) are just a couple of policy conversations that are happening that involve data and the potential to greatly impact IR and the way that we work.

David: Predictive modeling (looking forward) is a natural step in the analytical maturity curve beyond the foundational descriptive reporting (looking backward). IR professionals should embrace predictive modeling and recognize the requests for such models as a “stamp of approval” that administrators have enough faith in the underlying data and the IR office’s grasp of the nuances to task them with these new predictive challenges. Given the financial pressures many universities are facing and the need for resource optimization, we should anticipate growing demand for our predictive services.

Bill: There is a great deal of interconnected forecasting In the areas of finance and enrollment. Reliable enrollment models based on micro- and macro-trends and forecasts are especially important. An IR professional has a unique perspective from which to take in myriad factors that influence enrollments, such as demographics, history, campus climate, and outreach efforts. The blending of admission and financial aid data is crucial to this effort.

Minghui: I believe it is part of IR’s job to inform stakeholders of the upcoming changes that arise from various analyses and studies. I feel the challenge/opportunity is how IR gets involved afterward. I was often pulled into further conversations or planning in some areas that IR did not usually interact with, simply because I shared what I learned. This requires additional homework on my end to follow through and offer data support when needed. We also want to be careful about the fine line between areas and show respect to colleagues.

Equity, Diversity, and Inclusion (EDI)

Eric: It seems there are counteracting forces related to expanding data collections and research on equity, diversity, and inclusion. While the profession appears to be supportive of these efforts, we have realized that variable limitations exist in mandated data collections and the possible legislative reaction could be restrictive to the institution and possibly harmful to the students/faculty/staff we are trying to serve.

Marlene: I believe I was the only IR/IE person in the room at a recent equity, diversity, and Inclusion (EDI) conference. However, this should change as student populations continue to diversify and IR/IE offices evolve more toward a role based in decision support and planning. For example, my office was charged with authoring a successful grant proposal this past year for a project designed to support the success of underrepresented students on our campus. IR/IE offices will likely find themselves engaged in EDI efforts more and more over the coming years.

Mike: A tolerant society is built with diversity and equality, an appreciative and accepting society is built with inclusion, and a just society is built and measured with equity. The conversation has moved on from tolerance, we're working toward the goal of a just society now.

Bethany: The best way to pursue equity related research is a question that needs to be carefully considered, while also considering how to best approach the work of institutional research with an equity lens. The work around approaching IR work with an equity, diversity, and inclusion lens means that we must also consider the ways in which technology can enhance and hamper this work.

Bill: Efforts to promote diversity and inclusion on campus are of critical importance to institutional health. Institutions must be able to demonstrate that those efforts are effective, often by using mixed methods approaches. Bold and persistent experimentation is necessary as things that work are kept and those that don’t are discarded. Something that works well at one campus may not be readily adaptable at another.

Artificial intelligence (AI)

Mike: Technologies like artificial intelligence, the internet of things, and voice-activated assistants have placed us on the precipice of the 4th industrial revolution. Data-related professionals need a code of ethics that guides them when they find themselves in morally grey territory.

Bethany: Data-related professionals need to be aware of the changes in technology that are happening and must continue to learn and stay abreast of the changes in this area - as it is growing fast. There is evidence that bias exists in these technologies and we need to think about and engage with scholars as they work with AI and machine learning technology.

Bill: IR professionals would be wise to think about the impact of AI on their work. Those of a more technical bent can advise their institutions on best tools, but a great many of us may be more inclined to focus on the reliability and validity of the data produced by new tools. Larger IR offices can task a staff member with this work, but at smaller schools, the connection between IR and IT becomes more important than ever. If nothing else, it can be an invigorating new frontier!

What big themes are currently being discussed at your institution? What challenges can AIR help you with? Keep the conversation going by tweeting @AIR4Data using the hashtag #IRWaterCooler.