User provisioning management in a database system
A central database system receives information associated with an employee from an employer. Using this information, the central database system can provision one or more user accounts for the employee, for instance via an API of an account provider. The central database system can use a machine learned model to identify fields of the API and to translate the information associated with the employee based on information requirements associated with the API. When a characteristic of the employee, such as the employee's title, subsequently changes within the central database system, one or more features associated with the user account can be automatically updated in response to the change.
1. A method comprising:
training, by a central database system, a machine-learned model using information representative of a historical provisioning of user accounts with one or more third-party systems, the machine-learned model configured to translate employee information to satisfy requirements of third-party system APIs;
translating, by the central database system, information associated with an employee using the machine-learned model and based on requirements of a third-party system API to produce translated employee information; and
provisioning, by the central database system via the third-party system API, a user account on behalf of the employee using the translated employee information.
2. The method of claim 1 , wherein the user account comprises a cloud services account operated by the third-party system.
3. The method of claim 1 , wherein the machine-learned model is trained by identifying fields of APIs associated with the historical provisioning of user accounts, by identifying information associated with employees, by identifying information entered into the identified fields, and by determining a correlation between the information associated with employees and the information entered into the identified fields.
4. The method of claim 3 , further comprising determining an information type corresponding to each identified API field and determining a portion of the identified information associated with employees corresponding to the determined information type.
5. The method of claim 1 , wherein the machine-learned model is trained to identify one or more information translation operations performed during the historical provisioning.
6. The method of claim 1 , wherein the machine-learned model is configured to identify a correlation between a portion of the information and one or more fields of the API.
7. The method of claim 1 , wherein the machine-learned model is configured to identify additional information associated with the employee needed to provision the user account, and wherein the machine-learned model accesses the identified additional information from a source external to the central database system.
8. The method of claim 1 , wherein the machine-learned model is configured to identify additional information associated with the employee needed to provision the user account, and wherein the central database system is configured to request the identified additional information from the employer.
9. The method of claim 1 , wherein the machine-learned model is configured to translate the information before receiving a request from the employee to provision the user account.
10. A central database system comprising:
a hardware processor; and
a non-transitory computer-readable storage medium storing executable instructions that, when executed by the hardware processor, cause the hardware processor to perform steps comprising:
training, by the central database system, a machine-learned model using information representative of a historical provisioning of user accounts with one or more third-party systems, the machine-learned model configured to translate employee information to satisfy requirements of third-party system APIs;
translating, by the central database system, information associated with an employee using the machine-learned model and based on requirements of a third-party system API to produce translated employee information; and
provisioning, by the central database system via the third-party system API, a user account on behalf of the employee using the translated employee information.
11. The system of claim 10 , wherein the user account comprises a cloud services account operated by the third-party system.
12. The system of claim 10 , wherein the machine-learned model is trained by identifying fields of APIs associated with the historical provisioning of user accounts, by identifying information associated with employees, by identifying information entered into the identified fields, and by determining a correlation between the information associated with employees and the information entered into the identified fields.
13. The system of claim 12 , further comprising determining an information type corresponding to each identified API field and determining a portion of the identified information associated with employees corresponding to the determined information type.
14. The system of claim 10 , wherein the machine-learned model is trained to identify one or more information translation operations performed during the historical provisioning.
15. The system of claim 10 , wherein the machine-learned model is configured to identify a correlation between a portion of the information and one or more fields of the API.
16. The system of claim 10 , wherein the machine-learned model is configured to identify additional information associated with the employee needed to provision the user account, and wherein the machine-learned model accesses the identified additional information from a source external to the central database system.
17. The system of claim 10 , wherein the machine-learned model is configured to identify additional information associated with the employee needed to provision the user account, and wherein the central database system is configured to request the identified additional information from the employer.
18. The system of claim 10 , wherein the machine-learned model is configured to translate the information before receiving a request from the employee to provision the user account.
19. A non-transitory computer-readable storage medium storing executable instructions that, when executed by a hardware processor, causes a central database system to perform steps comprising:
training, by the central database system, a machine-learned model using information representative of a historical provisioning of user accounts with one or more third-party systems, the machine-learned model configured to translate employee information to satisfy requirements of third-party system APIs;
translating, by the central database system, information associated with an employee using the machine-learned model and based on requirements of a third-party system API to produce translated employee information; and
provisioning, by the central database system via the third-party system API, a user account on behalf of the employee using the translated employee information.
20. The non-transitory computer-readable storage medium of claim 19 , wherein the machine-learned model is configured to translate the information before receiving a request from the employee to provision the user account.