CRM Analytics: Information Safety to Management Entry to Rows

Final Up to date on November 7, 2022 by Rakesh Gupta

Huge Thought or Enduring Query:

• How do predicate filters safe your CRM Analytics datasets based mostly on safety necessities?

Aims:

After studying this weblog, you’ll have the ability to:

• Use predicate filters to safe datasets
• Apply sharing inheritance so as to add further safety to datasets
• and far more

Up to now written just a few articles on CRM Analytics. Why not examine them out if you are at it?!

1. CRM Analytics: Write Recipe Results to Multiple Datasets
2. CRM Analytics: Create a Dataset Using a CSV File

Donna Serdula is a System administrator at Gurukul On Cloud (GoC). After studying this article, she began studying CRM analytics and created a dataset (OpportunityWithAccount_1).

Now she obtained a brand new requirement to safe the dataset rows based mostly on the next situations: 

1. Customers ought to solely have the ability to see the data they personal.
2. Create a configuration that permits the administrator to grant ‘view all rows’ of the dataset to any customers.

Automation Champion Strategy (I-do):

Safety is exclusive to every dataset in CRM Analytics. Customers can see all rows and fields in datasets they will entry by default. To limit entry to data, you’ll be able to implement row-level safety on a dataset while you use sharing inheritance and safety predicates.

CRM Analytics offers numerous options to manage the entry to dataset rows. 

1. Integration Consumer – By default Integration Consumer has to view all information entry. Contemplate eradicating the sector entry from the profile if you wish to prohibit entry to specific objects and fields that comprise delicate information. 
2. Predicate Filters – Predicate filters run when a person accesses a dataset that’s used to return solely rows that match a particular situation. 
   1. File Possession-Based mostly Row-Stage Safety – Use an Possession-based safety predicate to show solely rows the place the worth within the Proprietor Id column (within the dataset) is similar logged-in person.
   2. Function Hierarchy-Based mostly Row-Stage Safety – If you wish to use document sharing based mostly on Salesforce position hierarchy, create a safety predicate to show rows the place the logged-in person’s Function Id is in an inventory of guardian roles above the position of the document proprietor. For instance, to limit entry to every document within the lead dataset, you’ll create a safety coverage the place customers can view solely leads that they personal or which are owned by their subordinates based mostly on the Salesforce position hierarchy.
   3. Territory-Based mostly Row-Stage Safety – If you wish to management entry to dataset rows based mostly in your outlined territories, create a safety predicate to show rows the place customers can view solely information acceptable for the territory to which they belong.
   4. Staff-Based mostly Row-Stage Safety – Use team-based row-level safety to manage the entry to dataset rows based mostly on the chance group.
   5. Customized Row-Stage Safety – Making use of row-level safety might end in no entry to information for some customers. To beat this, you should utilize a safety predicate to provide particular customers entry based mostly on a price in a customized subject on the Consumer object. We are going to study extra about it whereas implementing enterprise necessities.
3. Sharing Inheritance –
   1. Apply Sharing Inheritance to a Dataset – Sharing Inheritance permits CRM Analytics to inherit the sharing setup from a Salesforce Object and apply it to a CRM Analytics dataset with out altering its contents. This function will be utilized to the next objects:
      1. Accounts
      2. Circumstances
      3. Contacts
      4. Leads
      5. Alternatives

To study predicate expression syntax for datasets, try this assist information. 

Guided Apply (We-do):

There are 6 steps to unravel Donna’s enterprise requirement utilizing CRM Analytics. We should:

1. First, create a customized subject on the person object to grant ‘view all rows’ entry to any person.
2. To sync the newly created customized subject, navigate to the connection tab.
   1. Choose the default native connection, i.e., SFDC_LOCAL. 
   2. Choose the Consumer object.
   3. Choose the checkbox subsequent to the CRMA Information Entry subject you wish to sync.
   4. Click on Save.
   5. Ultimately, be sure to Run Information Sync.
3. The subsequent step is to replace the recipe OpportunitiesWithAccount, which you created as part of this article, to be part of the person object.
4. Now we now have to have a price within the dataset that matches the logged-in person. For this, we are going to create a components transformation utilized in information prep so as to add a derived subject (View) to the dataset.
   1. For this, we are going to use the Transformation factor.
5. Nearly there! As soon as all the pieces appears good, click on the Save and Run button.
6. After the dataflow has been up to date to incorporate the brand new derived subject, the safety predicate will be added to the goal dataset.

Formative Evaluation:

I wish to hear from you!

What’s one factor you realized from this put up? How do you envision making use of this new information in the actual world? Be happy to share within the feedback under.