Data Insights Informing Process Decisions

An integral component of the Enterprise Project’s business process transformation (BPT) workshops is harvesting and analyzing current data to gain perspective into how the university currently operates. Data insights enable business owners and workshop participants to make informed recommendations and design decisions. The work of leveraging these data insights is driven by the project’s Business Process and Business Intelligence, Analytics and Reporting (BAR) workstreams.

The Business Process teams that support each functional area of the project (Finance, Human Resources, Payroll and Student) include an embedded data scientist. The data scientists serve as resources by supplementing the ongoing work with data pulled from current systems. During BPT workshops, they also prompted workshop participants to consider what outcomes should be measured in determining success.

“Their goal in those workshops was to facilitate and mostly listen, but also drive the metrics conversation. A key part of all these process workshops is ‘Okay, how do we measure this? What is most important to measure?’” said Matt Mitchel, Data Insights lead.

An important role of the Data Insights team is to ensure we are designing processes around desired and measurable outcomes. For example, in the Recruit to Hire BPT workshops, one of the key discussions was how to move to a candidate sourcing model that strategically attracts the highest quality candidates.

Current State

Consider a scenario in which 10 candidates apply for a position. One of the 10 candidates is qualified for the role, and that candidate is hired.

In the current state, the only measure of sourcing performance is the percentage of candidates hired. Given that a candidate was hired, the above scenario would be deemed successful.

Future State

This scenario shares the same starting point as the current state scenario: 10 candidates apply for a position. However, now there are six candidates out of the 10 who are qualified for the role due to optimized HR advertising and a developed candidate pipeline. One of the six qualified candidates is hired.

The future state is designed around the need to systematically evaluate candidates via a rubric, which will unlock the true performance measure.

The outcome is identical – a qualified candidate was hired – but the path portrayed in the future state positions the university to employ a talented and diverse workforce that is retained over time.

An example of using data to anchor decisions can be found in the Accounts Receivable BPT workshops.

Current State

In the current state, the university fines students who are late in making payments. The Bursar’s office decided to pilot a program which allowed students to waive the fine by completing a financial wellness training. Prior to introducing the training, 57% of student payments were late and were on average 34 days late.

Future State

To determine if the financial training was more effective than the fines in preventing future late payments, a test/control experiment was conducted. It was found that the training decreased the percentage of late payments to 47% with an average of 14 days past the due date.

This data supports the policy decision to require all incoming freshmen to complete a financial wellness training. In the future state, this mandatory training will help improve student financial wellness from the onset.

Changes in student behavior following financial awareness training.

One of the lasting impacts of the Enterprise Project is to build and cement a data-driven culture, including an ongoing commitment to data insights in university operations. The modern systems and more efficient processes implemented as a result of the project will better support faculty and staff to maintain and interpret institutional data.