Building a Data and Assessment Culture that is Collaborative, Inclusive, Responsive, and Transparent


Literacy is Key; Strong Translators Build Literacy.


Without data, you may become just another person with an opinion. And while your opinion is unquestionably important, in a world where data is the currency of society, you’ll likely want to be able to back it up. Data is everywhere, after all. In an environment where any statement can be met by a reasonable expectation of evidence, off-the-cuff assertions or beliefs simply don’t cut it the same way they used to. If you want to measure it, prove it, or analyze it, you can. And this runs true across all sectors of society. We must—as a result—understand what data is, how it can empower our thinking, and how to best communicate about it with others.

Yet it isn’t just about the actual data. It’s how data is collected and why that will drive our ultimate success or failure. In higher education, assessment is a key vehicle for data collection. Yet, when assessment—whether academic, student affairs-based, or administrative for continuous improvement—is viewed as a nothing more than a requirement to be completed episodically to satisfy a regional accreditor or institutional expectation, it loses value—and, more importantly, power. Assessment needs to be meaningful and relevant for all campus stakeholders. Understanding that it takes effort to assess, the end result needs to be tangible information returning to providers to shape activities related to student success, program (or unit) effectiveness, or institutional progress.

Accomplishing this, however, means making sure our faculty, staff, and community are comfortable asking provocative questions: those that would help guide us forward but seem almost too difficult to answer to even justify asking. The questions that push us beyond our comfort limits and challenge us to truly think about what we do and why we do it. And, if we are asking provocative questions, we also must be willing to consider courageous answers. Rather than falling victim to what we have always done or approaching problems the way we historically have, we should be looking to innovate—even when it can mean disrupting the status quo.

To do this, we need to emphasize literacy. Literacy in our understanding of data and assessment. More than anything else, this literacy builds from strong translators: individuals on campus capable of both conducting advanced analyses with data while also being able to explain what they mean and how they can and should be used. We may have to help campus stakeholders learn how to ask those provocative questions. We will unquestionably need to push the same people to think beyond the typical canned answers and instead aim to be courageous in approaching concerns and opportunities.

For institutions of higher education today, it is essential that data is informing every decision occurring on campus. We don’t want algorithms to make decisions automatically though, so it’s not data-driven entirely. Nor should it be. Instead, it’s mixing data insights with human intelligence and understanding to reach the most reasoned decisions possible. And more importantly, it’s about feeling confident in the decisions we ultimately reach. We should be data-informed in all we do. If we can’t be, we should strive to be better.


My vision is simple: help campuses understand how to make the best use of the data they have and improve the quality of data they gather moving forward. From implementing tools that lead to more efficient and effective data usage to increasing data literacy across campuses, I strive to ensure institutional progress and student success are enhanced. This happens when we focus on four key things: collaboration, inclusion, responsiveness, and transparency. We must work together, include all potentially relevant parties, respond to the needs of our stakeholders, and allow for others to know what we are doing at all times.


New technologies and processes are only half the battle. Both are rendered meaningless if the culture is not in place to maximize utilization. As a diehard Chicago Cubs fan, I find it hard to believe anyone understands the importance of a healthy data culture more than Theo Epstein. After overcoming quite the deficit to win the World Series in 2016, Epstein remarked at Yale’s Commencement:

Early in my career, I used to think of players as assets, statistics on a spreadsheet I could use to project future performance and measure precisely how much they would impact our team on the field. I used to think of teams as portfolios, diversified collections of player assets paid to produce up to their projections to ensure the organization’s success. My head had been down. That narrow approach worked for a while, but it certainly had its limits. I grew and my teambuilding philosophy grew as well. The truth—as our team proved in Cleveland—is that a player’s character matters. The heartbeat matters. Fears and aspirations matter. The player’s impact on others matters. The tone he sets matters. The willingness to connect matters.

While Epstein had started to recognize the spreadsheets of data should only be part of the equation, his view of the data and analytics landscape in professional sports matured during the subsequent season:

Most organizations are operating with basically the same data streams and numbers…you have to look that much deeper to find that proprietary source of information or some data that another team doesn't have. You have to get really creative either whether that's neuro-scouting or some stuff going on in some office somewhere that they protect deeply that top, double-secret confidential info that the team can maybe keep for a competitive advantage for a couple of years before it becomes publicly available… Since everyone has really advanced data, it's really important now to find the right people and the right process to get your manager involved, get your coaching staff involved and ultimately the players because a lot of the information is only as impactful as it can be if it's actually put into play on a nightly basis knowing the team across the field is making adjustments on you… And the last issue is really sort of to go beyond the numbers and remember the game is played by human beings. So if everyone's got the same information you really want to put a premium on a humanistic approach, understanding people, putting them in a position to succeed, supporting them as human beings and individuals, and the chemistry of the group overall.


And there it is. A way to think about a healthy data and analytics culture that

  • recognizes everyone at least thinks about how they could be innovative with data (even if what they are envisioning isn’t truly innovative)

  • recognizes how essential strong data translators are to successful usage

  • recognizes how important the human touch truly is.

Possessing data isn’t enough; it’s everywhere today. But if we are expecting campuses to increase their data literacy capabilities, it is essential that we help pave a path from which they can be successful. Making good use of data in higher education, after all, depends on more than introducing a new technology or process. We need to strive to create a healthy data culture. We need to help campuses push their current comfort levels with data, find individuals on the staff and faculty (maybe even from within the student body) that can assure data is understood by those who use it to improve institutional progress or student success, and remember at the end of the day that every piece of data on a spreadsheet related to higher education ties directly or indirectly to a student—a living, breathing student.