7 Strategies for Building a Culture of Analytics with Dr. Mark Milliron

Establishing a culture of analytics within a higher education institution can be challenging due to the complexity of data infrastructure, the need to modify institutional practices, and the requirement for collaboration across various departments and stakeholders. Dr. Mark Milliron, Co-Founder of Civitas Learning, recommends seven strategies to build a culture of analytics at your institution.

1. Anchor Student Success Efforts in Your Institutional Data

Anchor your student success efforts in your institution’s specific data. Grounding your work to help students succeed in historic, real-time, and predictive data is essential. Using comprehensive information can focus your efforts as you move away from one-size-fits-all recommendations to a richer understanding of your students, their pathways, and how successfully they engage with your policies and practices. 

Although it would be nice to have a one-size-fits-all model, working with our partner institutions affirms that such a model does not exist. Relying on other people’s data to guide your innovation, interventions, or inspirational outreach can be imprecise and may pose real challenges for your students. While external data and best practices can offer direction and information, using multiple institution-specific predictive models across multiple outcomes will enable you to provide more precise support for students and improve outcomes, 

2. Adopt a Try & Test Mentality

Predictive models are only a starting point. Once you have access to detailed insights specific to your institution, the next phase of work begins. Trying and testing different policies and practices across institutions is the key to improving student learning and success. This approach is the opposite of seeking a silver bullet. Instead, it involves continually fine-tuning strategies and leveraging data as an asset rather than relying on it as the sole solution.

3. Accept Analytics as Mission-Critical

Analytics infrastructures have moved from nice-to-have to mission-critical. More institutions are developing infrastructure and teams to drive educational analytics strategy, moving beyond just reporting, accreditation, and planning. These efforts are becoming core to how the organization operates day-to-day. From early warning systems to advanced planning tools to weekly stat-chats, analytics are becoming mission-critical resources used daily. 

The move is similar to the transitions in educational infrastructures that took place as integrated ERP systems—the technology tools that undergird finance, HR, student information, and financial aid functions—entered education in full force in the ‘70s, ‘80s, and ’90s; and to how the LMS systems moved from a side-note innovation in our distance learning departments to a core instructional delivery platform for on-ground, blended, and online courses over the last 20 years. As the shift happens, we must intentionally leverage this mission-critical function and infrastructure across the institution.

4. Use Design Thinking on the Front Lines

The use of analytics in education has become increasingly important in recent years. However, simply having data is not enough. Four key elements must be considered: the right infrastructure, the right data, the right people, and the right way of presenting the data. While all four elements are important, the most crucial is identifying the right people to use the data and ensuring that the data is presented in a way that is helpful to them. 

One potential pitfall is presenting data that harms students rather than helps them. For instance, if a student is flagged as being at risk by a predictive model, alerting them could end their education journey rather than motivate them to seek support. To avoid this, design thinking is essential. This means considering how to present data in a way that is useful to front-line users like advisors, instructors, and co-curricular activity leaders. This means creating visualization strategies, simplifying outreach, and ensuring people are effectively connected and involved in the data process. By doing so, we can ensure that data is used in a way that helps students rather than harming them.

5. Catalyze Conversations About & With Analytics

It is important to keep the conversation about analytics in education going. This includes discussing important topics such as student privacy and data breaches and broader dialogues on how to make the most of the already available data. Our partners have demonstrated that diving into these discussions is crucial. 

Students have shared that they are comfortable with institutions using their data as long as it is being used to help them. For example, it helps them make better choices, choose the right course, understand their options more clearly, connect with the right support, or master a key concept. Faculty members are also open to data use if it enables their instruction and improves learning rather than focusing on simplistic data points that do not align with their goals. 

Furthermore, next-level conversations are taking place about exciting new directions and innovations in educational analytics. These include exploring toxic and synergistic course sequences or combinations, examining the impact of non-cognitive factors and student agency, combining adaptive pathways with adaptive learning, and optimizing course scheduling based on graduation pathways.

6. Take Systems & Culture Alignment Seriously

Analytics can either flourish or fail due to the systems and culture surrounding them. The most effective analytics systems must function well within the operational and social framework in which they are implemented. Ideally, they should support and reflect the systems and culture as they strive to improve. In the worst case, they can be obstructed by restrictive policies or cultural immune systems that actively resist change. 

Based on our experience, two core cultural issues must be addressed for analytics efforts to succeed: 

1. Moving from a primary focus on accountability analytics, where data is predominantly used to serve the needs of administrators, accreditors, trustees, and legislators, to placing equal or greater emphasis on action analytics. Action analytics employ data to assist frontline educators, advisors, support services, and learners.

2. Shifting from a culture of blame, where the immediate reaction to hard data is to find and shame those who are “responsible,” to a culture of wonder. A culture of wonder is open to thoroughly exploring the data, including determining if incorrect correlation/causation assumptions are being made and avoiding personal attacks.

7. Leading in the Age of Analytics

Leadership and learning are shifting in the age of analytics. Presidents and provosts are under pressure to become literate in analytics, just as they had to develop technology literacy over the last two decades. However, this shift goes deeper than just presidents, provosts, and the traditional bastions of data work in IR, IT, and institutional planning. 

Our partners focus on embedding the skills and will to use analytics across their institutions. While better tools and engaging strategies can make this easier, there is core work to be done to enable current and next-generation leaders to make the most of analytics in their efforts to help students on their journeys. There are implications for academic leaders, advising, career counseling, student life, housing, finance, financial aid, and more. Faculty, in particular, can be leaders in this work. Analytics tools can empower faculty as learning and research professionals in many ways. Regardless of role, it is clear from our work that creating a broad leadership culture that understands, appreciates, and is careful with data is essential for those looking to make the most of analytics on the road ahead.