Published Release Notes

Predictive models can drive predictions

You can use predictive models as the basis of your predictions. As a data scientist, you can now use Machine Learning Operations (MLOps) to replace models in your system. You can replace a model in a prediction with a PMML, H2O MOJO, or Pega OXL predictive model, as well as a scorecard or field, and then approve the update for deployment to a production environment. You can respond to a Prediction Studio notification that an active model does not generate enough lift and decide to replace the low-performing model with a high-accuracy model. You can also update a prediction on a regular basis, for example, whenever you develop a new churn model in an external environment.

For more information, see Replace models in predictions and migrate changes to production with MLOps.

Enhancing the performance of your Next-Best-Action strategy with globally optimized strategies

Starting in version 8.6, Pega Platform™ combines the versatility of Next-Best-Action Designer with strategy performance enhancements provided by using globally optimized strategies (GOS). Decrease the run time and memory usage of executing the Next-Best-Action strategy in batch or real-time scenarios by using the globally optimized strategies generated by Next Best Action Designer.

GOS is supported by Pega Platform's standard business change management process. GOS rules are automatically included in relevant revision packages.

For more information, see Enhance the performance of your Next-Best-Action strategy with globally optimized strategies (8.6).

Improved processing of correspondence details

When you send correspondence, the system now does not update the pxSendDateTime property on the pxCorrSummary work page that belongs to pyWorkPage. In previous versions of Pega Platform™, after sending correspondence, the update of the pxSendDateTime property could cause errors when working with an application at run time. To prevent such issues, Pega Platform now includes a pyEnableCorrSummary when rule that, by default, is set to false. If your business scenario requires updating the pxCorrSummary work page, set the when rule value to true.

For more information, see:

• Notifying participants about events
• Approval by email
• Sending automatic emails from cases

Support for picklists with parameterized data pages in App Studio in Cosmos React UI

You can now use data pages with parameters to populate a property of the picklist type with filtered results in App Studio. For example, in a survey case type, you can use a parameterized data page to configure cascading drop-down controls in which the values in a secondary drop-down list are based on the value that the user selects in the primary drop-down list. With dynamically-sourced picklists, you get greater flexibility in configuring picklists, and users see more accurate values.

For more information, see link Dropdown control Properties — General tab.

Questionnaires available in App Studio

App Studio now supports authoring questionnaires, which in previous releases of Pega Platform™ were known as surveys. You can create questionnaires, add question pages, and populate the pages with questions of different types so that you can collect the exact data that your business processes require. For more efficient data management and reuse, data objects from a data model now store answers to questions, instead of autogenerated properties. For example, when a customer provides a date of birth in a questionnaire, and your application stores the date as a data object, you can conveniently reuse that data object in related cases. For greater flexibility, both standard Pega Platform and Cosmos React applications support questionnaires, however, some question types are unavailable in Cosmos React.

For more information, see:

• Create questionnaires in a low-code way in App Studio (8.6)
• Creating a questionnaire

Remote case types in App Studio

App Studio now supports remote case types for applications that you build on Cosmos React. With remote case types, you can create a case type inside one application, and then work on cases based on this case type in another, remote application. As a result, users can perform work from multiple applications on one screen without the need to switch between applications, which results in increased efficiency and faster case resolution.

For more information, see:

• Increase efficiency by sharing remote case types between applications (8.6)
• Remote case types  

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