This topic provides a list of frequently asked questions about user insight.
How can I use data imported by others for analysis?
Answer: All members, except the administrator and data importer, need to be authorized to use data.
The authorization unit is tag, AIPL model, or RFM model. Currently, authorization cannot be performed by table. Tags come from the imported user tag table (including custom tags and user attributes based on the imported data). The AIPL model and RFM model are created from the user behavior table, order details table, or order summary table, respectively.
The authorization method is divided into three categories:
Use permission: the permission to use tags in scenarios such as perspective analysis, crowd filtering, Kafka push, and marketing.
Management permission: the permission to edit, authorize, and use the authorized tag.
Row-level permissions: grants permissions on specified rows to achieve data isolation.
To grant permissions, you must be an administrator, a data importer, or a member who is granted the management permissions. For more information, see Authorize.
How do I synchronize the latest data in a computing source?
Answer: After the AIPL model, RFM model, and population are created, if the data in the computing source has been updated, you can import the data table again, update the AIPL model, RFM model, custom label, and population to synchronize the latest data to Quick Audience. Otherwise, only the lagging data can be used for user analysis and population analysis, and the analysis results may not reflect the latest user situation.
Re-import the data table:
You can manually schedule tables, user behavior tables, statistics tables, order details tables, and order summary tables to be imported. You can schedule tables by using the following methods: daily scheduling, hourly scheduling, and API-triggered scheduling. For more information, see Scheduling Tasks.
Update the model:
RFM models and AIPL models can be manually updated and updated based on underlying data scheduling. For more information, see Manage AIPL Model and Manage RFM Model.
Update custom tags:
Custom tags can be manually updated or updated on a daily, weekly, or monthly basis. For more information, see Update Custom Tags.
Update Audience:
You can manually update a group, schedule an update based on underlying data, or update a group on a weekly or monthly basis. For more information, see Audience Updates and Snapshots.
NoteWhen you update an RFM model, AIPL model, custom tag, or crowd, the import scheduling of the source data table is not automatically triggered. Therefore, you need to schedule the import data table first, and then update it after the import is complete. This way, the latest data in the computing source can be successfully synchronized to the RFM model, AIPL model, or audience.
The following are our suggestions for reference only:
We recommend that you start with the scheduled import of the bottom table and set the parameters based on your bottom table processing tasks:
If the bottom table is periodically updated, we recommend that you set the import task to periodic scheduling.
If the bottom table is not updated periodically, you can use manual scheduling, or use the interface trigger scheduling method to write your own code to call the interface trigger scheduling after the processing task of the bottom table is completed.
After you set the scheduling and import of the bottom table, we recommend that you set the population, RFM model, and AIPL model to follow the underlying data scheduling and update. In this way, each time the data table is successfully imported, the audience, RFM model, and AIPL model will be automatically updated to the new data.
What are the causes the failures of data table import, AIPL models, RFM models, custom tags, and audience updates?
A: The data table fails to be imported. If the AIPL model, RFM model, custom tag, or audience fails to be updated, the failure icon 
is displayed. Move the pointer over the icon to view the specific failure cause.
The following list describes common causes of this error:
If you do not have permissions on the corresponding data, the account authorization settings of the computing source may have been changed. Please log on to the computing source as an administrator to check the account authorization.
The underlying data table does not exist. The data table may be renamed or deleted. Please check the underlying data table.
The password of the computing source is changed, but the new password is not updated to the computing source configuration of Quick Audience. In this case, all data tables and groups based on the computing source fail to be updated. Ask the administrator to update the password for the compute source.
SQL statements fail to be executed. The execution may fail because resources are occupied. We recommend that you try again later.
What are the differences between the ID encryption method options when I import a data table and the ID encryption method options when I push an ID to a data bank or a Damengpan?
When you import a data table to Quick Audience, you can set the ID encryption method to Unencrypted, MD5, SHA256, or AES. You must specify whether the raw data is encrypted and the corresponding encryption method before the data is imported.
When you push an ID to a data bank or a Damengpan, you can select MD5 or SHA256. You need to specify the encryption method for the ID.
If the ID raw data is in the original text, Quick Audience will encrypt it with MD5/SHA256 when pushing it.
If the ID raw data has been encrypted by AES, Quick Audience will first decrypt it and then encrypt it by MD5/SHA256 during push.
If the ID raw data has been encrypted by MD5 encryption, you can only select the push encryption method to MD5 when pushing. Quick Audience will keep the MD5 encryption status when pushing.
If the ID raw data has been encrypted by SHA256, you can only select the SHA256 encryption method when pushing. Quick Audience will keep the SHA256 encryption status when pushing.