2022 Most Valuable Performer: University of North Texas System

For the University of North Texas (UNT) System, early efforts at long-range planning were complex, time-consuming, and entirely dependent on a single employee’s skills to run Excel projections. Today, UNT System uses Syntellis’ Axiom™ Long-Range Planning (LRP) to evaluate potential new initiatives, quickly run scenarios, and enable an integrated financial planning structure.  

From the impact of COVID-19 campus closures to new dormitory requests and even billboard advertising opportunities, UNT System uses Axiom LRP to build different scenarios that arm leadership with meaningful insights and data to guide decisions. Currently, UNT System is preparing Legislative Appropriations Requests to put forth to their state legislature in hopes of securing additional funding. But before making the official requests, finance leaders will use Axiom to run scenarios and determine which requests to submit.  

“With Axiom Long-Range Planning, we’ve grown what we can do as an institution,” says Kerry Romine, Senior Director of Strategy and Planning. “In the past, we might have made decisions that were ill-informed. Now we don’t have to worry about that because we make decisions based on data that we can stand behind.” 

University of North Texas System logo

For UNT System, the ultimate measure of success was when the CFO and President began asking the finance team for data before making an important strategic decision. “Axiom has enabled us to answer the ‘what if’ questions much quicker and with confidence,” says Paige Smith, Associate Vice Chancellor for Budgeting, Planning & Analysis. “UNT System's most senior leaders rely heavily on Axiom data — and they trust it — to make critical financial and strategic decisions.” 

With timely, integrated financial data and scenarios built in minutes, instead of days, the UNT System finance team can easily meet requests from various campus leaders — maintaining stakeholder satisfaction and the system's overall financial health.