As he educates next generation’s business leaders, Yuanyang Liu also works to solve some of the biggest problems in HR management and labor markets using data analytics techniques.
Liu joined the Haslam faculty in 2019 after earning his PhD in management science and business analytics from the University of Iowa. The program prepared him well for his work at Haslam. “My PhD study exposed me to a diversity of academic fields: economics, computer science, statistics, biostatistics, and network science,” he says. “These are all important in my current research.”
When Liu visited Haslam to interview for the position, he got a good sense of what the school was about. “The faculty have a diverse research agenda, including statistics and operations management, data mining and machine learning methodologies,” Liu says. “And Haslam’s undergraduate and graduate degree programs in business analytics are well-developed, with classes tailored to students’ career success.”
While he teaches cutting edge courses in data mining, Liu believes that regression analysis and other traditional skills are still relevant. “The more classic courses, like statistics, are actually very important for understanding the relatively new data analysis methods,” he says. “Students need to know how and why the newer methods work, and how to be mindful of their shortcomings.”
Liu’s current research addresses human capital issues, such as how firms can minimize employee turnover using data analytics. “Applying data analytics for talent acquisition and retention is one of the most urgent challenges facing HR leaders around the world, but one they are least prepared to tackle,” Liu says. “The central topic of our study is competition for human capital in the labor market. That’s important for HR because it can create a more specific, specialized strategy to keep valued employees.”
Closing the “capability gap” involves collecting employee skill data from LinkedIn profiles or electronic CVs and then applying emerging methods such as predictive analytics algorithms, network analysis and text mining. “We’re able to study novel questions that were previously unsolvable, while also looking at classic questions from new perspectives,” Liu says. “It’s a very exciting time for business analytics research.”