Institutions are increasingly asked to support decisions with data, but continue to struggle with delivering those analytics, facing some common pitfalls:
Data Literacy of Staff: Data analytics skills are rapidly evolving, and getting the right staff in place to lead initiatives can be challenging.
Focusing on Regulatory Data Needs: Often, institutions are focused on providing regulatory reporting, leaving less resources to focus on internal metrics that can result in improved admissions, student retention, or expense reduction.
Disorganized, Siloed Data: Data is often siloed in separate technology systems spread across multiple departments. If metric gathering occurs solely at the department level, multiple departments seek answers to similar problems but can return different results, impeding decision making.
Lean Centralized Data Team Works Best
A McKinsey study reports that a centralized data analytics team headed by a “Data Czar” that reports to senior administration leadership provides the most efficient approach in resolving those common challenges. Working with senior leadership gives a centralized data team insight into the top issues the institutions is facing. A lean centralized data team can also work with multiple departments for more agile data gathering and data sharing, creating more unified data, reliable results, and less duplication of effort.
Metrics that Achieved Results
The same study found centralized teams working with unified data provided results.
A centralized data team at one university quantified how service learning programs impacted student retention, and enabling the university to make an informed decision about retaining the program.
In another example, a university dealing with declining enrollment found that though advertising was successfully generating leads, their call-center was unable to process call volume. The university invested in new call-center technology while also reducing advertising, and realized a 20 percent increase in new student enrollment.
