When a higher education institution is implementing a new student information system, one of the biggest decisions is determining the best data migration process for the existing data. Porting all the legacy data into a new system usually requires time, money and resources. But there are different approaches depending on your school size, budget and data integration needs.
The first place to start when developing a data migration strategy is understanding how your data needs differ for each group (prospects, students, parents or organizations) in the database. The type of data can be separated by the following:
- Biographical (name, email, phone, etc)
- Admissions (application forms and activity history)
- Student Record (transcript, billing, financial aid , student attendance)
- Career Development (career tracking, gifts and contributions, activity history)
Each data type might require a different treatment. For instance, biographical data is something most vendors will import for no cost because it’s a relatively standard process. But for the other bits of information -- relating to admissions, student records and alumni -- there are multiple options to consider.
#1 Manual Data Entry
For small schools, with approximately 100 students, entering the information manually can be very manageable. This option generally works best when the historical data isn’t needed by the organization immediately. What usually happens is that the full records for current active students are put into the system before the system goes live and after go-live the older less critical information is entered piece by piece over time.
#2 Attach Files (.pdf, text or .jpg formats)
Another option that can be implemented in conjunction or in lieu of manual data entry is to upload individual files (like a transcript or resume) in a .pdf, Word or Excel format. These files can be attached to the student record so they can be downloaded or printed. The downside to this is the data isn’t accessible for reporting purposes but sometimes, like in the case of a alumni transcripts, all you might need is a copy filed in case there’s a document request in the future.
#3 Data Conversion
The third option to consider is data conversion, which is the data cleansing process of mapping, scrubbing, de-duping and porting the data into the new system. Schools with more than a few hundred students that matriculate over multiple years of study find this option to be the best return on investment.
Chances are, if you are looking for a new Student Information System, one of the main reasons is your data is not as accurate as it needs to be. The right SIS system should help fix the root causes going forward and the data clean-up process can eliminate errors, duplicates, inconsistent formatting, and missing information.
Conversion costs should be looked as an investment since it helps the bottom line. Strong data integrity is a benefit for making better decisions and greater accuracy and timeliness for government reporting. The latter of which can be a heavy burden to a higher education school.
The conversions costs mainly depend on the following factors:
- Volume: How many data fields need to be convert. (not to be confused with the number of records, which is less important)
- Type: Is it student biographical information, billing, student attendance, student grades, career placement etc.
- Cleanliness: How much duplicate data, mixed type fields, errors etc.
After evaluating the above factors, the data conversion process includes multiple steps.
Every data field needs to be mapped so that the old database fields are placed in the corresponding fields in the new database. Data mapping usually requires a significant amount of time since it might involve hundreds or thousands of fields per student. Also, every organization structures their database differently and various fields can have different purposes based on their operational process.
For instance, one organization might use three different fields for a Phone Number: Direct Phone, Cell Phone and Company Phone. While another might just use just a single field called Phone. So sorting out what goes where is important.
But the great thing about the mapping process is it forces your organization to rethink its processes or change some bad habits. That’s where formatting comes into the equation.
Data Formatting and Data Structure
While algorithms and SQL database scripts are generally used to speed up the process, human decision-making is also a critical factor. It’s important that the SIS vendor understands the various intricacies of your workflow process and the purposes of any specialized fields. During these discussions, insight about substandard data formatting and the structure of your database is usually identified.
A good example of this is course registration data. Courses can be structured in terms, cohorts, semesters or individually with unlimited start and end dates. I’ve seen instances where schools put the course start date, in the course title. Essentially combining two fields into one. This is not an ideal structure for formatting courses, since the title and date should be in separate fields, which makes for more seamless reporting.
The time spent upfront re-formatting the fields so they are in the proper structure will save significant headaches and man hours in the long run.
Cleansing data is the process of removing inaccuracies, errors and ensuring that the data is consistent.
Sometimes data is accidentally entered differently, like a phone number with or without a parenthesis around the area code. This is a simple example, but if your database is structured with many free-form fields so there are unlimited options vs defined drop-down menus (limited options), the level of inconsistent data becomes greater and the ability to find what you need becomes more difficult.
The final stage of the process s to identify duplicate records. If your existing database isn’t integrated and you have multiple databases for various departments, the likelihood of having multiple records for the same person is high. Those records will need to merged. A good SIS system will have an algorithm that can identify possible matches by cross referencing multiple fields. These records are then placed in a holding table to be evaluated manually before integration.
While implementing a new Student Information System, the data conversion process is a great opportunity to clean up your historical data and develop new processes to ensure it stays clean in the future. Although there are upfront costs associated with conversions, in the long run it’s worth it, since the process will save time and money with government reporting, audits and day-to-day management of your organization.
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About the Author
Joe Stefaniak has been a leading expert for almost 30 years in the development and implementation of software solutions for higher education. His expertise is in helping colleges and schools streamline operations and manage information for better decision making through analysis and application of best practice software. He founded SCAN Business Systems in 1986. Its flagship product, Campus Café, has grown into a leading provider of educational student information systems. He holds a degree in Business Administration from Northeastern University.