A Logical Model is the way the data in Salted CX are organized, making them easy to use for a wide range of analytical needs. The Logical Model is designed to be easy to understand for people without deep technical knowledge. Understanding the logical model is helpful for creating custom metrics, building reports, and designing dashboards.

The data available in the Logical Model varies depending on the connected data sources and their configuration. The Logical Model can work perfectly fine if the data are not complete. A subset of metrics will be available.

Data Sets

Data sets represent different kinds of items stored in the logical model. Salted CX uses a limited set of data sets that enable users to quickly navigate in it.

Each data set has a set of attributes, facts, and references to related data sets.

Attributes

An Attribute is a property of a Data Set that can be used for segmentation and filtering of data visible in the reports and dashboards.

Attribute and label values are limited to 100 characters. If an attribute or label value is longer than 100 characters, the value is trimmed. Whenever a value is trimmed, the event is reported to the Technical Log as a warning. We recommend monitoring trimmed values, as they may lead to skewed reporting. If multiple values share the same first 100 characters and differ only after those characters, segmenting by such attributes will merge metrics from these two distinct values into a single segment.

Enumerations

Enumerations are special types of attributes that can contain only values allowed by Salted CX. Unsupported values cause the data to fail to load using our Ingest API.

Entities

Entities are very simple Data Sets. Unlike Data Sets, they have only one attribute with two labels and no facts. Every entity has the same set of one attribute and two labels for that attribute.

Entities enable you to have a unique representation of important items in the contact center, with a human-friendly visualization. Entities also enable you to easily rename the items without breaking relationships between important data points.

PID

Each item in a dataset and every entity has a unique Permanent Identifier (PID). PID is a special type of attribute. PID is always a UUID that is either generated randomly or deterministically based on identifiers in the connected platform. Once a PID is generated for an item, it cannot be changed in the future.

Labels

Labels are alternative visualizations for attributes. While you might use a hard-to-read value that you are certain is unique as an attribute value, the unique value may be very inconvenient for end users to read.

There might be two items with the same label but different underlying attribute values. If such a label is used in insights, you will see two different segments with the same name and different metric values.

Similarly to attributes, the label values are limited to 100 characters. If a label value exceeds 100 characters, it is trimmed. Whenever a value is trimmed, the event is reported to the Technical Log as a warning. Since labels are not directly used for segmentation and filtering, the trimmed labels have a lesser impact on data correctness. However, if there are two labels that have the same first 100 characters, the insights may be misleading - for example, you can see twice the same label value or accidentally filter for a different value.

Facts

A Fact is a numeric value in the Logical Model. Facts can be used for arithmetic operations, aggregated, and ultimately form the foundation for creating metrics.

Metrics cannot be used for filtering in insights and dashboards. However, you can use fact values in metric filtering conditions.

When you load custom data and provide a non-numeric value where a fact is expected, the value is ignored and the event is reported in the Technical Log as a warning. The number in quotes is also not considered a fact.

Date, Time, and Duration

All dates and times in the logical model have one-minute granularity. There are multiple dates and times in the Logical Model.

All durations in the logical model are in seconds.

Technical items in data sets

Some data sets have technical values. Technical values have an attribute Type set to value Technical. You can use this attribute to filter the technical items in the data sets.

Technical items are stored by default in the Agent, Customer, and Engagement data sets.

Data not in logical model

The logical model does not contain the following data:

• Conversation content, such as transcripts or voice recordings. It may, however, contain metadata extracted from the content, such as discussed topics, sentiment, and other features extracted from the content. The content is stored separately and displayed only when users drill down to the customer journey and have permission to view the conversation content.

• Customer's personally identifiable data. Customer's personally identifiable data is removed and replaced by identifiers from the customer profile. We do not expose personally identifiable information in analytics or in the customer journey. To display personal identifiable information, the user must have a dedicated permission and click on a specific masked value to view it.

Technically, you can provide pieces of conversation content and personally identifiable data in attributes and labels when loading custom data to Salted CX. However, we strongly discourage you from doing that as it bypasses compliance enforcement in Salted CX.