Following on from last week’s blog post on ‘why’ institutions should be delving into Academic Program Financial Management, this post provides advice on the types of data needed to support these models.

Tip 1 – Data

Data extracts are required from multiple siloed systems. These include Financial Management, HR/Payroll, Student Management Systems, Timetabling and sometimes even Facility Management Systems.

The type of data needed includes:

Financial / General Ledger

• Expenditures by object code and organizational unit
• Revenues by source noting any restrictions on use

Human Resources and Payroll

• Salaries and benefits of all employees
• Faculty labor distribution (i.e., salaries and benefits amounts by source)
• Employee context (type, role/position, etc.)
• Full time equivalency (FTE) details

Student data

• Student headcount and program/subject enrollments
• Student tuition payments
• Student fee payments
• Student room and board payments
• Student financial aid

Timetable / schedule

• Classroom to course section data
• Subject section duration (face to face time)
• Instructional method
• Faculty subject assignments

The biggest bit of advice we can provide here is don’t wait for perfect data, because you will never start. Data is never perfect, but you must be willing to improve data as you work through this process. In fact, by integrating these types of disparate data sets together in the one model, data outliers and issues can often be highlighted and brought to the system owners’ attention.

Data Issues Examples

The following as some real examples of data issues we have encountered over the years.

• Room numbers in the facilities system not matching room numbers in the timetabling system.
• Wrong Account Codes being used.
• Incorrect fee types for students (i.e. international offshore students being put against a local campus, or International Onshore students being put against an overseas campus).
• Space being recorded against an old ‘tenant’ rather than the new one.
• Timetabling data not accurately representing teaching effort.
• New campus or account in one dataset that hasn’t appeared in other data sets. E.g. a new project defined in the financial system that wasn’t in the student system.
• Different codes used for school/department/campus across the difference systems – Finance/HR/Students/Facilities.
• Different organizational hierarchies between Finance and Student Systems – hard to align who teaches what.
• Incomplete time tabling data.
  • Rooms not booked for classes
  • Rooms booked for classes that don’t exists
  • Person delivering the class not held/correctly updated.
  • Shared classes not recorded properly in timetable
• No decent workload model for the effort required to prepare and deliver online content.
• Salary dollars held in a central account vice the organizational structure where the employees reside in the HR data.
• Revenue held in a central account and not within the schools.
• Revenue accounts in the GL are not detailed/aligned with the institutions major tuition rates, i.e. CGS v Domestic Fee Paying v International.

Data Issue Mitigation

When these data issues are identified, institutions can then start a rectification process, some real examples are shown below.

• creating new account codes for different fee type revenue or adding in projects to identify different fee types.
• better visibility of how inter-entity payments are recorded.
• better visibility on central overhead internal transfers (i.e. not just one ‘lump sum’, now broken down in to ‘HR’, ‘Finance’, ‘IT’, ‘Students’, etc. allowing the model to use better drivers).
• calculating FTE internal to the university and provided in the raw data, rather than the modelling team estimating FTE from raw salary data based on average salaries.
• Due to gaps commonly found in timetabling data, faculties and schools can use room types (e.g. lecture, lab, tutorial, etc) by subject and class size to approximate face-to-face teaching hours. Being able to systematically scale the hours according to each subject’s headcount is often more accurate and requires less ongoing effort to maintain.

Based on our experience building large numbers of models for universities and colleges we have compiled a detailed list of the key data fields from all of the above-mentioned systems. If you would like a copy, please click the appropriate link and download from our website:

Australian University Data Requirements

US/Canadian University Data Requirements

For more information on Academic Program Financial Management please click here.