Building a data and analytics culture in higher education means equipping key stakeholders with the skills necessary to analyze and leverage insights extracted from data. Doing so can drive faster, more accurate decision-making.

When I hear “data and analytics culture,” I immediately think of the work Jason Simon and his team are doing at the University of North Texas (UNT). Jason Simon, Ph.D., Associate Vice President of Data, Analytics and Institutional Research (DAIR), leads a team of 12 with a broad array of institutional research, business intelligence, data modeling and data governance experience.

When I initially met Simon several years ago, he explained that UNT had fundamental issues with data integrity, data management and data governance, which plagued the university’s analytics department, relegating data to silos and making enterprise analytics difficult.

Given UNT’s more than 5,000 employees and countless stakeholders, Simon recognized his task went far beyond organizing data to meet executives’ needs. There were so many important factors to take into account – attitudes about data, communication norms and politics, to name just a few. Data are just an obvious factor. So many other factors lie below the waterline, so to speak.

With this important recognition, Simon and his team engaged their anthropology faculty to conduct research that might inform their project. Together they asked, “What would an ideal data landscape look like in an institution of higher education?”

I was curious about what best practices UNT could share from their journey in instituting a data and analytics culture and here’s what they shared:

1. Assess current resources and skillsets within the organization. How can others – even beyond the analytics department – contribute to the data governance ecosystem? Leveraging the expertise among, say, faculty or the communications department, will not only improve data outcomes but will also involve a wider audience earlier in valuing and understanding data.
2. Step back and ask what your institution needs from you as a data leader through stakeholder interviews. Then build a comprehensive plan that will fulfill those needs – a plan that includes resources, staffing, platforms, training, etc.
3. Monitor and document questions to the institutional research office about what something means, as these questions represent an area of curiosity for the campus community but can hinder the institutional research team in developing more critical research.
4. Establish goals and thresholds for success at the beginning of the process. What does good look like, and how will you know you’ve achieved it? We must practice what we preach. So the program roadmap should be evidence-based, including KPIs (perhaps related to training, a finalized data dictionary, platform completion), any relevant revenue returns, etc.
5. Track all quantifiable cost savings and ROI, including improvements in time savings, productivity and operational efficiencies by business units. Proactively share results along the way so leadership recognizes the value a culture of data governance and analytics adds to the organization. Provide executives with new reports that influence decision-making and give them success stories they can evangelize.

Since building a strong data and analytics culture, with the help of SAS, increased efficiencies at the university have led to cost reductions of more than $1 million. You can be sure Simon shared that with his leadership as proof of value.

The team added one more tip for good measure: Be prepared to pivot, as data and expectations are always changing.

It was great to catch up with Simon. Hopefully, the experiences he and his team have shared will benefit other institutions embarking on a similar journey of implementing a data and analytics culture.

Want to learn more? Download this free whitepaper and see the six guiding principles that led to UNT's data governance success.