Using Data to Support Organizational Change at Community Colleges

By Jaime Lester

In the ACE research brief Tools in a Toolbox: Leading Change in Community Colleges, I present multiple recommendations to support organizational change, specifically in community colleges. While community colleges can and do change on a regular basis, leaders can use specific tools from the bottom up and the top down to facilitate strategic change. Change does not need to be a mysterious process, even at large and often decentralized community colleges. By using change plans, being attentive to relationship building and teams, engaging data, and considering new structures and approaches, change can be thoughtful and strategic.

This post digs deeper into one element of the brief—how to use data to inform decision-making. All too often, data are seen as the answer to informing and changing individual and organizational behaviors. However, data are just the beginning of a process of learning. The next and most important step is to engage in the cognitive and often social process of identifying the meaning behind the data; simply, the how and why of each data point.

It’s important to acknowledge that change occurs in institutional contexts that, in the case of community colleges, are often resource constrained. This makes it even more important to consider efficient recommendations that do not overtax an already-constrained system.

The section below presents recommendations for promoting effective, data-driven decision-making in the context of organizational change. The intent is to provide a roadmap for community college leaders to center data in everyday decision-making to support larger-scale change plans. To ground the process in institutional values, I’ve organized the following around two interrelated recommendations that support evidence and reflection.

Recommendation 1: Make data central to institutional culture

I tell students in my graduate-level organizational theory course that the most frustrating recommendation at the end of a manuscript is to “change culture.” Not only is this an unspecified recommendation, but there is often little to no guidance given on how to change culture. I will not leave you with a broad and unspecified recommendation, as the literature on data-driven decision-making provides plenty of advice on the importance and practice of changing culture to be data driven.

To begin, leaders can reframe the use of data in decision-making to what Kathryn Parker Boudett and colleagues call a habit of the mind. Developed after decades of working with K–12 leaders and teachers on school improvement, habit of the mind is an intentional cultural process of changing norms around data from being viewed as simply a plan or project to being a way of thinking and behaving. Habit of the mind is about changing how work is done on a daily basis to center decisions around evidence and data, and not just anecdotes and inferences.

Community college leaders can do this by establishing new initiatives in current collaborations or teams that focus on engaging with data. Leaders may also consider developing new teams with the primary focus on data and assessment. For example, leaders may develop a student-learning team (or leverage an existing committee) with the primary responsibility of taking current institutional data, such as student transcripts, to analyze completion rates in developmental courses and the retention of students who successfully complete those courses. With the data in hand, the committee may then brainstorm how and why students succeed. Disseminating the results and having conversations around campus will put the focus on evidence of success rather than inferences of success. Moreover, when leaders use or support data-driven decision-making they communicate the primacy of data in college-level decisions. A broad focus on data will create a shared commitment that can lead to shared institutional norms about work.

Recommendation 2: Create an intentional data infrastructure

One of the major barriers to engaging with data is the lack of infrastructure—first the technical availability of data, and then the individual knowledge and skills to engage with the data.

To create a data infrastructure, leaders must consider the availability of quality data. The saying “good data in equals good data out” still rings true. Community colleges, like many other higher education institutions, often work in systems from years ago and have significant limitations in data capacity and analysis. These systems were often originally built for other sectors, such as customer service, and were modified to meet the needs of an educational institution, resulting in serious limitations as to what can be easily extracted for data use. There are also issues with how data systems can interface with one another. Leaders should become knowledgeable about what data systems exist and their capabilities, and engage end users and experts on purchasing new data systems. Keep in mind that current uses are not necessarily future uses.

Leaders must also consider the importance of data accessibility. Assume that most individuals do not have a high level of technical skill in data analysis! Data needs to be available in a web-based format to ease access, and made searchable in ways that relate to key institutional values and outcomes, with the option to download to allow for manipulation. Moreover, many, especially smaller, community colleges do not have a full-time institutional researcher to help support data collection, access, and analysis. To ease these barriers, community college leaders might consider identifying resources to support a full-time institutional researcher or providing training for current staff to develop skills in data analysis. Local universities and even online options are readily available for professional development. On a related note, leaders can build data-skilled teams to create more cross-training across the college.

Conclusion

A move toward data-driven decision-making does require investment in new resources in the short term. However, the long-term benefits include more skilled staff, an opportunity to uncover inefficiencies in the college, and a culture that supports evidence. At some colleges, this may seem like a heavy lift, but many small modifications can create new shared norms. Creating and changing culture is not a one-time process—it requires constant vigilance. As the college grows and new people are hired, socializing them into the work norms of data-driven decision-making is key to continuing those habits of the mind.