When importing data to Silverbucket it is vital to keep the structure identical to the example datasheet to ensure a flawless import:

- First row contains the names of the headers - it is not necessary to edit the first row.
- Second row in your file is where your data starts to get imported
- Columns need to be in the same exact order as in the examples
- Rows after the default structure's last column are never read / handled when importing
- Never have hidden columns in your data sheets

- People with extended data: first name, last name, email, default role, supervisor, username, external ID, user rights, external/own, organizationNodeCode, organizationNodeName, workday length, UI language, cost price, hourly rate, usergroup (10 max)


All the metadata added can be found from the added Person's Dashboard afterwards. This metadata can be used to filter your users in various manners in Silverbucket and with this integration you can add a multitude of users at once with the correct rights and information.

col. H	I	J	K	L
Give 'Admin' rights for person? Y/N	Give 'Full Access' rights for person? Y/N	Is the person an external user? Y/N	Code of the organizationNode the person is in	Name of the organizationNode the person is in

col. M	N	O	P	Q
Workday Length (format: 7,5)	User's UI language (format: EN, FI, SWE)	Cost Price	Forecast Price	Site Code

col. R	S-AB	AC
Site Name	UserGroups	Person's Country

1) A person called 'Adam Adamson' with the following metadata:

email = adam@email.com
role = HR Representative
supervisor = boss@email.com (seeks for a user with this email from Silverbucket. In this example boss@email.com was already added and therefore added as the person's supervisor.)
username = Adamson.adam
external ID = XYZ123
user type = admin (column H has the value 'Yes')
employment type = Own (column J has the value 'No')
organization = [HR] Human Resources
workday length = 7,5h
user's language = English
cost price = 40€ (currency = your Silverbucket's default currency)
forecast price = 50€ (currency = your Silverbucket's default currency)
user groups = HR1 & HR2

2) A person called 'worker' with the following metadata:

email = worker@work.com (the email prefix is where the person's name "worker" comes from)
role = Subcontractor
supervisor = extBoss@email.com (seeks for a user with this email from Silverbucket. In this example extBoss@email.com wasn't created before the import and therefore this cell was not handled at all; the person gets no supervisor.)
external ID = EXT_009
user type = Basic User (columns H & I are empty so their value is 'No')
employment type = External (column J has the value 'Yes)
organization = [EXT1] Externals
cost price = 50€
forecast price = 50€

3) A person called 'Peter Peterson' with the following metadata:

email = peter@email.com
role = 2nd Line Support
supervisor = adam@email.com (added in the same file as row1, therefore the supervisor is found in the system and added as the person's supervisor)
username = Peterson.peter
external ID = XYZ400
user type = Full Rights (column H = 'No' & column I = 'Yes')
employment type = Own (column J has the value 'No')
organization = [TRE1] Tampere_1
workday length = 8,0h
user's language = Finnish
user groups = TRE1

Silverbucket matches rows to Silverbucket's data case-sensitively ("SilverBucket_Support" is not considered the same as "silverbucket_support").
However, matching email fields is done case-insensitively ("Support@Silverbucket.Com" is considered the same as "support@silverbucket.com" when creating a user, for example)

If the data structure was correct, you can see the outcome in Silverbucket right after the upload. You can check this by navigating to any of your personnels' dashboards. In this example we have navigated to Adam Adamson's dashboard: