Data science is experiencing a boom. Data scientists are in high demand but in short supply. The processes for hiring and onboarding new data scientists are too expensive and time-consuming. Companies are looking for other solutions to solve this challenge for data science.
Many private and public universities are investing in data science degrees and programs to keep up with this demand. With a need for data science across all industries—technology, healthcare, government—universities prioritize interdisciplinary programs. Many of these programs are designed to emphasize reshaping the skills needed in the workforce today. Data analytics, visualization, machine learning, and automation are in-demand skills, regardless of industry.
As universities expand their offerings, they may need to incorporate data analytics into their operations. For example, higher education leaders understand the importance of analyzing student enrollment data or degree completion rates. However, they may face specific challenges in implementing a data-driven approach. For any organization to become data-driven, it’s critical that top leadership champions the cause and prioritizes tangible results over hunches.
Many higher education institutions silo analytics capabilities into one department, and there is little incentive or process for sharing data across departments and university functions. For example, the alumni relations department has different needs and priorities than the institutional research team, and thus each group uses its own data sets and prioritizes its analytical needs.
Another challenge colleges and universities face in their data-driven transformation are connecting the various legacy software systems and databases. As Mckinsey notes, “Even with the help of a software platform vendor, the lead time to install, train, and win buy-in for these technical changes can take time, perhaps two to three years, before institutions see tangible outcomes from their analytics programs.”
As higher education institutions increase their data analytics capabilities, senior leadership must go beyond data champions. Instead, they must reiterate the benefits for the university as a whole, highlighting the improvements to the student journey, new research opportunities, and the ability to build a financially secure institution. Establishing a centralized data analytics department that defines standards for capturing, sharing, and managing data across departments is one step institutions can take.
Creating a culture of data sharing is an arduous task for many businesses, and universities have their complexities adding to this challenge. Nevertheless, these institutions can significantly increase value for students while transforming their operations.
