Published Release Notes

Rule creation and simulations of changes in a change request in the Decision Manager portal

Two major enhancements to revision management give Decision Manager portal users more control over change requests. Revision managers and strategy designers can now create rule instances as part of a regular change request. They also can run data flows to do simulations of changes that are introduced by the change request.

For more information, see Rule creation and simulations of changes in a change request in the Decision Manager portal.

Language detection option in Text Analyzer

On the Text Analyzer form, you can identify the language of incoming records based on their language metadata tag by selecting the Language detected by publisher option. This option applies only to Text Analyzer rules that process Twitter records. By combining this option with the advanced language filtering criteria of Twitter data sets, you can filter out unnecessary data and analyze only the Twitter records that are relevant to your business objective.

For more information, see Configuring advanced filter conditions for Twitter data and Configuring language settings.

Facebook metadata types available for analysis

The number of Facebook metadata types available for analysis in your application through Facebook data sets has been extended. After you provide a valid page token that corresponds to your Facebook page, you can analyze the following metadata types that relate to that page: public posts, tagged posts, promoted posts, direct messages, and comments on posts. By analyzing these metadata types in your application, you can enhance your understanding of your customers' needs and respond to issues faster.

For more information, see Creating a Facebook data set record.

Options for filtering data in Twitter data sets

Twitter data sets have more options for filtering tweets. On the Advanced options tab of the Edit Data Set form, you can configure your Twitter data set to not analyze tweets if they contain specific keywords or if the tweets are made by specific authors. You can also select the languages to analyze based on the language metadata tag of the incoming tweets (tweets in other languages are ignored). By configuring the filtering criteria, you can ignore data that is not relevant to your text analysis and retrieve only the records that you need.

For more information, see Configuring advanced filter conditions for Twitter data.

Reconfiguration of the Adaptive Decision Manager service after upgrade to Pega 7.2.2

After you upgrade the Pega 7 Platform to version 7.2.2 from a version prior to 7.2,1, you need to configure the Adaptive Decision Manager service. Beginning with Pega 7.2.1, the Adaptive Decision Management (ADM) service is native to the Pega 7 Platform and is supported by the Decision data node infrastructure.

For more information, see Services landing page.

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).

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.

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).

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.