5 Steps to an Accurate Bloomerang Import
For many organizations, Bloomerang is a data management system that spans many functions. It holds donor, fundraising, development, volunteer, and other critical operational data. However, there are times when data is gathered, collected, and aggregated in systems external to Bloomerang. That data must be brought into Bloomerang, but entering it manually can be too time consuming. This is where Bloomerang imports come into play and make the integration of data systems much easier.
However, the import process shouldn’t be underestimated. If your Bloomerang import is done inefficiently or doesn’t follow the proper steps, you can spend more time importing data into Bloomerang than you would entering the data manually by hand. Follow these five steps to efficiently and accurately import data into Bloomerang.
1. Develop your import file template
The first step of any import is to develop an import file template. An import file template is an Excel file with column headers that match the fields on the data object you want to import. Like a blueprint for a house, an import file template can help you make sure nothing gets left behind.
Create an import file template with these steps…
- Determine what you are importing. Are you importing constituents, donations, interactions, or notes?Are these records for individuals or organizations?
- Outline all of the fields on the data object you are importing. Review the data entry screen for that object in Bloomerang and outline the fields you want to import. For example, if you are entering donations for individuals, go into Bloomerang and list the fields on the individual constituent object. Then go into a blank donation record and list the fields on the donation object as well. Pay particular attention to required fields for the data object because those will be required in the import.
- Take the list of fields for each data object and add them in the top row of an Excel spreadsheet with each field in a separate column. Save your import file template for the next step in the process.
2. Map your data to the template
You have two options when mapping your data into the import file template. The option you choose will depend on your comfort level manipulating data in Excel.
You can either…
- Copy and paste the data you have in a separate Excel spreadsheet into the import file template you created by matching the column headers with the data you want to import.
- Or, you can arrange your data in the current Excel file to match the column headers in your import file template.
The end goal is to produce an import file that matches the template you created in step one. Don’t forget to include fields that are required in Bloomerang to create a specific data object.
3. Review and clean your import file
After your data is fully mapped to your import file and you have a single file with your data, you need to review and clean up your data prior to import. Reviewing and cleaning your import file will mitigate Bloomerang import errors and inaccuracies.
Reviewing and cleaning your import file should include the following steps.
- Review each column and confirm the name of each column header matches the field name in Bloomerang (or is closely named to the field in Bloomerang).
- Review each column and confirm the field type and data style of your data matches the field type and data style of the same field in Bloomerang.
- Remove blank rows in your import file and remove blank columns (if no data is in that column).
- After all of the data is cleaned, you may need to separate your import file into multiple import files. Bloomerang can only accept 2,000 rows of data at any one time. This includes the column header in row one, so only 1,999 rows of actual data can be imported in a single file.
4. Run a test file
Imports cannot be undone. Once you run an import, you cannot easily delete or recover your database’s state prior to the import. User import errors can only be corrected manually. If you run a batch of 100 records or even 1,999 records and make a mistake, you will be manually updating your database for many hours. That is why it is important to run a small test batch of 3-5 rows of data before you import your full import file. Start with a small number of records as a test, so you can manually recover if you make any mistakes. Run your full import file once you are confident the import will do what you want it to do.
Running a test file is practical because it mitigates errors, but it also streamlines subsequent imports. Once a successful import is run through Bloomerang’s system, it is saved and can be easily accessed for future imports that use the same import file template (i.e. order of column headers).
Don’t forget to spot check the imported test data to make sure everything was imported into Bloomerang properly.
5. Run full file and test accuracy
After the test file import is successful, run your full import data. Then test your import for accuracy.
First, check the import statistics as you run the import. Then run a report and double-check that the number of records created via import match your expectations. This can be done by filtering on a common data element like a specific field in your imported data or the creation date of the record.
In addition to running a report, it is also recommended that you do some spot checking of your data to make sure it was imported properly. Even though you ran your test earlier, it is important to confirm the quality of your import. Verifying the validity of the data in your import will help you sleep better at night and minimize the potential for long-term reporting issues from bad or inaccurate data.
How to Value Imports
Imports can save your organization time and energy if you follow these five steps. However, using the Bloomerang import tool should always be based on a cost to benefit analysis. The purpose of an import is to save you time. If you don’t save time with an import, don’t import the data. Enter the data manually instead.
You can make this determination by multiplying the time it takes to enter one record in your file manually times the number of records in your data set. Estimate how much time it will take to run your import and find where the two calculations hit a break-even. Now you know when an import is a positive return on time and when it isn’t.
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