Data Requirements - Locations
Notes:
All locations with addresses.
File name: Locations_YYYYMMDD.csv
Format: Excel file or .csv
Optional Location Characteristics that may be included: Size in square feet, number of classrooms, etc.
Sample:
LOCATION_NBR | LOCATION_NM | ADDRESS1 | CITY | STATE | ZIPCODE | TYPE | STATUS | OPEN_DT | CLOSE_DT |
1 | Buxton | 2651 S. Polaris Dr. | Fort Worth | TX | 76135 | Freestanding | Open | 1/1/1994 |
|
2 | Capitol | 1100 Congress Ave. | Austin | TX | 20500 | End Cap | Open | 7/4/1976 |
|
3 | Location | 123 Fake St. Unit 1 | Fort Worth | TX | 76135 | Inline | Closed | 6/2/1986 | 1/9/1986 |
Overview:
Location_Nbr: Unique identifier for each location that will match up to performance or student/consumer files.
Location_Nm: Unique name for each location. This will be the name that is displayed in the Buxton platform (SCOUT) and any analysis reports.
Type: Location type that may be relevant for analysis. This might include types for real estate type, number of classrooms, square footage, etc.
Status: Open, Closed, Pipeline
Latitude: Optional field, locations’ latitude
Longitude: Optional field, locations’ longitude
Sqft: Optional field, size in square feet
Data Requirements - Monthly Performance
Notes:
Summed monthly performance per location. This is typically enrollment counts.
File name: Performance_YYYYMMDD.csv
Format: Excel file or .csv
All locations listed in this file should be in the locations file.
Need at least 1 full year of performance data.
Sample:
LOCATION_NBR | MONTH | DEPARTMENT | TOTAL | UNITS |
1 | 1/1/2025 | Business | 8558 | Students |
1 | 2/1/2025 | Technology | 6135 | Students |
2 | 1/1/2025 | Business | 3017 | Students |
2 | 2/1/2025 | Technology | 2556 | Students |
Overview:
Location_Nbr: Matching unique identifier for location from Locations table.
Department: Any performance breakouts you have, e.g. department name, daypart, etc.
Units: Enrollment counts
Data Requirements – Consumers (if applicable)
Notes:
Consumers are students from prior 12-month period.
File name: Consumers_YYYYMMDD.csv
Format: Excel file or .csv
All locations listed in this file should be in the locations file.
Sample**:
LOCATION_NBR | DEPARTMENT | FIRSTNAME | LASTNAME | ADDRESS | CITY | STATE | ZIPCODE |
1 | Technology | Emma | Reynolds | 2914 Oakwood Dr | San Diego | CA | 92104 |
1 | Technology | Liam | Foster | 1258 Maple St | Austin | TX | 78701 |
2 | Technology | Ava | Mitchell | 473 Lakeview Ave | Orlando | FL | 32801 |
2 | Technology | Noah | Carter | 847 Willow Lane | Denver | CO | 80203 |
** Sample data is illustrative only and does not represent actual customer data.
Overview:
Location_Nbr: Matching unique identifier for location from Locations table.
Department: Any performance breakouts you have, e.g. dayparts, departments, etc. These should match the departments in the monthly performance file. If unable to provide students by department, this column/field values can be denoted as “Overall”.
Data Audit Compliance Checklist
The following checklist denotes common points of failure for the data acquisition process. By confirming that your data complies with each of the items below, you can help ensure that your data is accepted and moves forward in the data transfer process. If you have any questions or concerns regarding any of the items below, please contact your Buxton team.
General
File headers are included in all files
Locations
Address information is complete and provided for each location
Open date is provided for each facility, if possible
Monthly Performance
All open locations are present in the monthly performance file
Each location present in the monthly performance file is also present in the location file
At least 12 months of monthly performance data is provided
Locations are not missing any monthly performance data
Data is not provided for future months
Any required breakouts by Type are included
Consumers
Duplicate student records are not present
Address information is complete for at least 90% of consumer records
All location numbers in this file join to the locations file
Tags: Education, Education Data Requirements, Data Requirements, Education Data
