Importing Current Person Data

Before making Slate a system of record, there may exist a need to import person data from an old prospect system, a student information system, or an institutional database system into Slate. To accomplish this, use Upload Dataset.

Segment the Data

Before using Upload Dataset to migrate data, take some time to develop a data migration strategy. This strategy may include the decision to not import all records or data points from external systems into Slate, at least at the present time. 

A better practice is to upload records that are relevant to current business process needs. Additionally, upload data points that are required for any new, Slate-based business methods.

Segmenting data into a hierarchy of priority, similar to the one here, is recommended:

External Data  
Current Prospect Data
Records with a current or future entry term and no application.
Make this data the primary focus so Slate can successfully be "turned on" as the system of record.
Current Applicant Data 
Records with a current or future entry term and at least one application.
After populating Slate with prospect records, then attention can be turned to current applicant data.
Historical Applicant Data 
Records with a past entry term and at least one application from a previous cycle.
This data should be addressed at a later date (near the end of year one in the Slate cycle).
Historical Prospect Data 
Records with a past entry term and no application.
This data should be addressed at a later date (near the end of year one in the Slate cycle).

Begin by focusing on current prospect data, and divide it into manageable batches.

Take Baby Steps. Not only is segmenting the initial batch of external data an effective way to learn about Upload Dataset, it also makes a large task feel more manageable. After learning the basic skills needed to manage Upload Dataset, uploading prospect and application data in a combined fashion is more easily accomplished. 

Migrating current prospect data that resides in non-Slate systems should be the first priority. Once that data is shifted into Slate, it becomes easy to start using Slate as the new system of record.

A best practice is to upload current prospect data through five separate, strategically organized uploads. Each upload contains a unique identifier ID field, so Slate can match incoming data to the correct person record in Slate.

Current Prospect Data  
1 - Core Data
Unique field, Name, Date of Birth, Email, Gender, Address, Phone, etc. (other destinations in “Student Record”)
This upload will establish the base of the applicant record in Slate, including the unique field that will be used as a key for subsequent data uploads.
2 - Qualitative Data
Unique field, Entry Term, Race, Academic Interests, Athletic Interests, etc. (other destinations in “Fields”)
Once the unique field value is stored in Slate, that value becomes the only required data point needed to match additional information with a student record.
3 - School Data
Unique field, School Information
4 - Test Score Data
Unique field, Scores
5 - Interaction Data
Unique field, Interactions

Uploading prospect data can seem like an intimidating experience in the beginning! Start by uploading data for 5-10 prospect records and learn how the data comes over and appears in Slate.

When this process becomes more comfortable, use the same steps to upload larger data files.

Create a Unique ID Person-Scoped Field

To allow Slate to interface with any external system, first create any new fields in Slate that will contain the unique ID of records from an external system. This field should be configured to contain a unique value. This makes it possible to match records in Slate with records in other systems.

Make a list of the unique ID fields needed to create–and match–within Slate. Some common examples include:

  • Banner ID
  • EmplID
  • Colleague ID
  • Campus ID
  1. Select Database on the top navigation bar and select Fields.
  2. Select Insert.
  3. Enter the following configurations:
    • Status: Set field to Active.
    • Scope: Unique fields are person scoped and associated with an individual record.
    • ID: This should be lower case and contain no spaces.
    • Name: This will be the display name of the field.
    • Prompt: As these fields will be unique, they will not be associated with a prompt list.
    • Tab: Include these fields on a custom tab for easy viewing.
    • Value: Set to Store Value (bit/language/state/country/user prompts and text fields only).
    • Multiple: Set to Single Value since these fields will contain only one data value.
    • Unique: Set to Value contains a unique ID which identifies a single record for merging. This setting is crucial for all person ID fields in order to differentiate unique records.

    Once a unique field exists, it can be used as a key to match with external system records with internal Slate records.

  What if no application ID is stored in an external system?

The round configurations created in Slate will be used to match imported data with applicant records.

Exporting Core Data from an External System

The core data import is essential because this data will:

  • Build the base of the person record
  • Establish the unique key that will be used for future dataset matching

Export core data from any existing systems as Excel spreadsheets, tab-delimited text files, or CSV text files.

Unique ID First Middle Last Email DOB
653451 Alexander Stephens Hamilton 2/2/1999
854278 Sarah Wilbur Washington 10/24/2000
453780 William Howard Taft 9/15/1985
147934 Tammy Jay Jefferson 3/21/1997

The first column of the export should be reserved for the unique ID used to identify records in an external system, and core data should include:

Unique ID First Name
Last Name Email

These data points ensure that Slate can match future dataset uploads as well as form submissions (such as events or interviews) with a prospect record.

Consider adding optional core data exports from this list:

Prefix Suffix Preferred Name Middle Name
Mailing Address Street Mailing City Mailing Postal Address Effective
Address Expires Permanent Street Permanent City Permanent Postal
Citizenship 1 Citizenship 2 Permanent Resident Phone Number
Mobile Number Evening Number Sex  

Importing Core Data from an External System

  1. Select Database on the top navigation bar and select Upload Dataset.
  2. Upload the file.
  3. Enter the following configurations:
    • File Format: New Spreadsheet/Data File.
    • File Type: Excel Spreadsheet (or other file appropriate for the data file).
    • Destination Scope: Persons/Dataset Record.
    • Record Type: Persons/Applications.
    • Unsafe: Leave this cleared at this time.  Unsafe uploads will be covered in the next few sections.
    • Update Only: Leave this cleared at this time. "Allow record creation" means that Slate will create a new record for any records in the dataset that do not match records in Slate. "Update only" means that the Source Format will not create new records, and the dataset will only update records that it matches.
    • Dedupe Records: By default, Slate creates a new record for every row if Allow record creation is selected above, and an existing match cannot be found. If Dedupe records is selected, Slate will evaluate the source file and dedupe records with the source based on an exact match of First Name, Last Name, Email, and Birthdate.
    • Hide: Leave as Create source interactions unless the source format will be used for a daily feed from an SIS, for example. With only few exceptions, it is recommend to create source interactions for display on a timeline.
    • Upload/Add File: Select the file for upload and select Upload.

After the upload processes, the file upload details page appears.


  • Edit (upper right): Select Edit to rename the file, adjust or assign a folder to the file, or delete the file once it has processed.
  • Download (middle right): Select to download the uploaded file.
  • Build Import (middle right): Select to begin the process of mapping data points to the appropriate fields in Slate.

Additional File Data Points Shown:

  • Folder: If the file has been placed into a folder, it is listed here
  • User: The user who uploaded the file
  • Format: The format of the file (such as spreadsheet, or CSV file)
  • Status: Displays the current status of the file import
  • File Name: Displays the name of the source file uploaded for import
  • File Size: The size of the data file
  • File Uploaded: Displays the date and time of the initial file upload

Once a file processes, the following data points also appear:

  • Rows Imported: Indicates the number of rows (excluding the header row) imported (commonly one row per record)
  • Load Runtime: When the file began processing
  • Remap Runtime: When the file finished processing

When the file processes correctly, a section called Sampling of Imported Records displays the first 50 rows or related records imported from the file.

Uploading the file is only the first step. Data must still be mapped to the destination fields in Slate by building the import.  Refer to the Upload Dataset Stages article for guidance on building the import and field mappings.

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