When you log on, we can tell you it looks like it might be good for you to start testing next month.
Using data to design a path to licensure.
As the big data trend continues to grow, associations are crunching the numbers to make decisions about their marketing, member engagement, and growth tactics. But the National Council of Architectural Registration Boards is using its data to help aspiring architects design a shorter career advancement path—specifically, to licensure.
To document the health of the architecture industry, NCARB, which represents state licensing boards and develops license standards, provided statistics to its members on the licensure timeline. Most notably, it found that, on average, it took candidates 14 years to go from enrolling in school to earning their first license.
NCARB kept regulators informed, “but we were not really helping the candidates in any way,” Chief Information and Innovation Officer Guillermo Ortiz de Zárate says. “And that was the motivation—to excel at customer service while not bending the rules.”
To help candidates understand their own path to licensure, NCARB dove into its data. “We started using predictive analytics, which was basically just throwing all of the information that we had into a model to see what were some of the forces or indicators that could help us predict how quickly someone would get a license,” Ortiz de Zárate says.
The model helped NCARB identify 50 factors that could influence a candidate’s path. They included factors that could be acted on—like when candidates begin seeking a license in relation to their schooling, when they begin testing in relation to their experiential learning, what degree they earned, and the order in which they take the exams—as well as ones that couldn’t, like a candidate’s gender.
The goal was to provide a tool for candidates “to monitor their current status, their path to licensure, and what are the events or decision-making points in the future that could affect that timeline,” Ortiz de Zárate says.
NCARB first used the variables to create a decision tree, allowing candidates to predict when they would finish based on what they had already done. But to help candidates find ways to shorten their estimated timeline, NCARB developed an online calculator focused on 13 actionable variables.
“When you log on, we can tell you it looks like it might be good for you to start testing next month, because it seems like it’s the most optimal time of testing on average from our model,” Ortiz de Zárate says. The challenge is ensuring that candidates use the tool early enough that they can actually change course in order to shorten their path.
The licensure exam was recently updated, and many actionable indicators are related to the exam. NCARB is planning to relaunch the online tool once it has sufficient data on the new test.