Published Release Notes

Easier customer record management in Customer Profile Designer (early preview)

The new Customer Profile Designer module of Pega Customer Decision Hub™ makes it possible for marketing analysts and strategy designers to define the associated data for each customer context directly in the Pega Customer Decision Hub portal. It is also possible to define more complex associated data structures that use a custom data flow, or define associated data of different types, such as RDBMS and Cassandra, for the same customer context.

Customer Profile Designer is available in Pega Customer Decision Hub 8.6 version as an early preview version. The functionality will be further expanded in future releases.

For more information, see Manage customer records in Customer Profile Designer (8.6).

Support for calling a data transform from a case life cycle in App Studio and initializing data at case creation

You can now call a data transform from a case life cycle in a low-code way in App Studio. For example, when a customer wants to order the same product again, your application can reuse the shipping information from their previous order. Additionally, by configuring data initialization for a case type, you can prepopulate fields at case creation. For example, an application for a delivery service can apply the customer's preferred delivery options when a new case starts. These enhancements save time for developers because the system can initialize data and prepopulate fields in a case by reusing existing logic, and for customers, who do not need to manually enter their details every time.

For more information, see Calling a data transform from a case and Initializing data at case creation.

Text predictions simplify the configuration of text analytics for conversational channels

Enable text analytics for your conversational channels, such as email and chatbot, by configuring text predictions that manage the text models for your channels. This new type of prediction in Prediction Studio consolidates the AI for analyzing the messages in your conversational channels in one place and replaces the text analyzer rule in Dev Studio.

Through text predictions, you can efficiently configure the outcomes that you want to predict by analyzing the text in your channels:

• Topics (ticket booking, subscription cancellation, support request)
• Sentiments (positive, neutral, negative)
• Entities (people, organizations, airport codes)
• Languages

You can train and build the models that predict these outcomes through an intuitive process, and then monitor the outcomes through user-friendly charts.

For more information, see Predict customer needs and behaviors by using text predictions in your conversational channels.

Upgrade impact

Channels that you configured with text analyzers in the previous version of your system continue to work in the same manner after the upgrade to the current version. When you edit and save the configuration of an existing channel, the text analyzer rule is automatically upgraded to a text prediction. The associated text prediction is now an object where you can manage and monitor the text analytics for your channel. When you create a new channel in the upgraded system, the system automatically creates a text prediction for that channel.

What steps are required to update the application to be compatible with this change?

1. Enable the asynchronous model building and reporting in text predictions through job schedulers that use the System Runtime Context (SRC) by adding your application to the SRC.
   For more information, see Automating the runtime context management of background processes.
2. Enable model building in text predictions by configuring background processing nodes.
   For more information, see Assigning decision management node types to Pega Platform nodes.

Data propagation for questionnaires in case types

Pega Platform™ now provides a tool to prepopulate answers in a questionnaire that runs in a case by propagating data from a parent case. At run time, specified questions in a questionnaire that users complete as part of case processing, can display answers by using information that users already provided in the parent case. As a result, you speed up the development of your application and help users process cases and complete questionnaires faster.

For more information, see:

• Deliver user-friendly questionnaires by prepopulating answers (8.6)
• Running a questionnaire in a case

Kafka data set enhancements

The Kafka data set is a high-throughput and low-latency platform for handling real-time data feeds that you can use as input for Pega Platform event strategies.

For better integration of Pega Platform with externally hosted Kafka, the following enhancements are implemented:

• Support for Kafka message keys and headers - extended values data format (JSON Data Transform, Apache Avro)
• Custom value processing
• Configuring topic names by using Application Properties
• Data-Admin-Kafka enhancements - supporting a wide range of connection properties

For more information, see Improve your Kafka data set with new enhancements.

Case outcome predictions in case types

Pega Platform™ introduces the use of machine learning and AI in Case Designer. You can now use predictions in case types, both in App Studio and Dev Studio. By using predictions, your application can predict various outcomes, such as the possibility of reaching a positive or negative case resolution, so that you can prioritize work, route cases according to risk, or optimize the case flow in other ways. You can use predictions when you create conditions in the condition builder, for example, when you define a business logic for routing assignments. 

For more information, see:

• Prioritize and route case work by predicting case outcomes 
• Predicting case outcome
• Creating predictions for case management

Virtual questions deprecated in 8.6

Following the improvements in Pega Intelligent Virtual Assistant (IVA), the creation of virtual questions is now deprecated and planned for removal. To avoid additional effort during updates to future releases, do not use deprecated features. For optimal application performance and efficient development of conversation processes, you now collect information from users by using the Ask a question smart shape.

For more information, see Adding a case type conversation process for a conversational channel and Asking a question in a case.

External data flow rules are removed

In previous versions of Pega Platform™, you could configure data flows to run in an external Hadoop environment. The external data flows functionality was deprecated and hidden from view in Pega Platform 8.5. The functionality has been now removed and is no longer available in Pega Platform 8.6.

For more information, see External data flow rules are deprecated.