Keeping all financial data accurate and up-to-date can be a logistical nightmare for institutions of all sizes; from small liberal arts schools to massive universities that care for tens of thousands of students each year. There are a number of different systems that higher education institutions use for receiving, compiling and making use of information on their prospective and current students. BANNER and Colleague are two of the most popular systems which track information on financial, academic and personal information for each of these students. These systems serve as a central database and can create various reports, including Accounts Receivable information for students who owe money on their accounts. The information supplied by the system must be applied to each student’s individual account to ensure that their financial activity is up-to-date.
BANNER outputs this specific Accounts Receivable report in a semi-structured TXT format, meaning that there are only a few options for accessing the data trapped within the report.
- Manually re-type all of the data from the Accounts Receivable file into an Excel workbook
- Manually copy and paste all of the data into an Excel workbook
- Find someone in IT who can write a script that will extract the data in the format needed for reconciling the payment information to the student’s account
None of these options are tasks that anyone would want to take on. Numbers 1 and 2 are both incredibly time-consuming and error-prone. Number 3 can take an undetermined amount of time, depending on the current workload of IT.
The best option to address this roadblock is to introduce data preparation tools to speed up the report extraction and account reconciliation processes. Monarch can both ensure the quality and integrity of the data being used in official records and reduce the amount of time it takes to run these processes on a recurring basis. In addition to streamlining the extraction and formatting of data, Monarch enables institutions to quickly redact or mask sensitive information on these reports, such as student names, IDs or Social Security numbers, without losing the original data.
BANNER and Colleague house other financial information, including paid invoices, scholarship and grant awards. Different offices on campus also gather information on admissions status, academic performance, housing, health records, program performance, graduation rates, etc. Much of this information may be collected by disparate systems, but ultimately have to be reconciled and uploaded to the central BANNER system. Manually reconciling data from disparate systems or formats can be a job in itself. However, with Monarch’s join capabilities, joining disparate files based on like-information can be done in a matter of minutes. A project that may have taken several man-hours can now be completed in less than 5 minutes with a higher degree of confidence in the final report.
With Monarch, data extraction can be highly automated. This can be accomplished in two ways.
First, models can be created to capture the right data out of a regularly-recurring report. Each time the report is created, the end-user will pull up Monarch, open the pre-built model, apply the new report and have their data ready for analysis instantly in rows and columns. Applying new reports to existing models enables end-users to automatically apply all extraction, blending, cleansing and manipulation in a matter of minutes. One higher education institution currently uses Monarch to complete roughly 50 projects that must be dispersed to 120 different departments on a weekly basis.
Second, Monarch Server is a highly scalable and powerful automation platform that can capture, cleanse and deliver reports to the proper location without the need to ever open Monarch again after it is configured. For example, one of our customers currently uses Monarch Automation Server to automate 70 processes which include over 100 different files in disparate formats.
- Data Fabric: Stitch Up Your Data Strategy With Visualization - September 13, 2019
- Data Fabric: Save a Stitch in Time with Predictive Analytics - September 5, 2019
- Data Fabric: Skip the Patchwork with Powerful Data Prep - August 14, 2019