What is the Analytics and Data module?

These configurations pertain to custom attributes and configurations associated with EXI application filters. The primary functionality of such settings is to structure the data handling processes by defining the data model relevant to the custom attributes. This allows you to create a system that’s tailored according to your specific data management needs.

By customizing how the attributes associate with these data models, you improve the system’s ability to process, manage, and interpret data, making it more efficient. In essence, these configurations play a crucial role in streamlining your data management tasks, thus enhancing overall productivity.

Configuring Analytics and Data

Custom attributes

Documentation for Registry Registrations Fields

Model

In the Registry Registrations, you’re required to select a data model. This could either be the User Data Model or the Knowledge Article Data Model.

Example: If you want to store data related to users, you’d select the User Data Model.

The Model field determines how your data will be structured and categorized in the system.
Attributes

Custom attributes are additional parameters associated with the chosen data model.
- Name: Enter the name of the Custom Attribute that you expect for the chosen data model. Ensure this field matches, case-sensitively, with the name in the external system.
  
  Example: If a user is associated with a VIP status in your system, you’d enter “is_vip” as a custom attribute.
- Data Sensitivity Policy Policy Type: Select the type of sensitivity policy that the custom attribute should follow. The policy type option includes Customer Data.
  
  Example: If the custom attribute is dealing with sensitive user data, you’d select CUSTOMER_DATA_USER_DATA.

The Registry Registrations tab allows you to register and manage data models within your system. It provides key details about chosen data models and their custom attributes, helping organize and structure your data efficiently. Importantly, it ensures data matches exactly with the information from the external system, streamlining any data-related processes you execute.

Key use cases of Custom Attribute Registry

When writing DSL rules in the Bot Access Rule, Ticket Filters, or other DSL Rule editors. It is mandatory to register the custom attribute first, or else the DSL Rule Editor will not let you save the rule as it will recognize the rule of syntactically invalid.

Application Config

/service-management/moveworks-setup/advanced-config-editor

Analytics Value Config

The Value Assumptions page lets you review and adjust the assumptions used to calculate the value your organization realizes from your Moveworks AI Assistant. Moveworks uses these assumptions to generate value reporting internally, which is then shared and reviewed with you. Adjusting them ensures that reporting reflects your organization’s actual costs and time savings.

Value Assumptions

Hourly Agent Salary

The dollar value of one hour of a service desk agent’s work. It can be derived from the annual salary of agents, or from the service desk contract value divided by the total number of service desk agents.
Example: If an agent’s yearly salary is $62,400, their hourly wage would be $30 (assuming a 40-hour week over 52 weeks).

Hourly Employee Salary

The dollar value of one hour of an employee’s productive time. It can be derived from revenue per employee or salary per employee.
Example: An employee earning an annual salary of $124,800 will have an hourly value of $60.

Additional Controls

These assumptions capture the time saved when employees and operators use your AI Assistant instead of raising or handling a ticket. All time savings in this section are measured in minutes.

Operator Time Savings

Operator time savings for ticket information (minutes): Time saved by operators when end users retrieve ticket information through the AI Assistant.
Operator time savings for form finding (minutes): Time saved by operators when end users find forms through the AI Assistant.
Operator time savings for ticket interactions (minutes): Time saved by operators when end users interact with tickets through the AI Assistant.

End User Time Savings

End user time savings for search ticket deflections (minutes): Time saved by end users when a ticket-eligible search is answered directly by the AI Assistant.
End user time savings for ticket information (minutes): Time saved by end users when retrieving ticket information.
End user time savings for form finding (minutes): Time saved by end users when finding forms.
End user time savings for people and room lookups (minutes): Time saved by end users when looking up people and rooms.
End user time savings for approval information (minutes): Time saved by end users when retrieving approval information.
End user time savings for group information (minutes): Time saved by end users when retrieving group information.
End user time savings for file search (minutes): Time saved by end users when searching for files.
End user time savings for action ticket deflections (minutes): Time saved by end users when a ticket-eligible action is completed directly by the AI Assistant.
End user time savings for ticket interactions (minutes): Time saved by end users when interacting with tickets.
End user time savings for approvals (minutes): Time saved by end users when performing approvals.
End user time savings for email writing (minutes): Time saved by end users when writing emails with the AI Assistant.
End user time savings for text translation (minutes): Time saved by end users when translating text with the AI Assistant.

Agent Tickets Handled Annually

The number of tickets handled by a single service desk agent per year. Used to translate time savings into agent-capacity terms in value reporting.