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Customer Insights Cache

Data lies at the heart of 1:1 customer engagement, and Pega Customer Decision Hub™ requires relevant and up-to-date data to make high-quality decisions for customers.

What is the Customer Insights Cache?

Customer data required by Pega Customer Decision Hub typically resides in various sources, such as data warehouses, data lakes, or 3rd party applications. Customer Decision Hub requires only a portion of this data.

To expedite the process of identifying the key decisioning properties (the fields from the data sources), Pega has constructed industry-specific data models for customer-centric decisioning. These data models are based on best practices synthesized from implementations at innovative organizations worldwide, following an outcome-driven design process with real-time performance top of mind. They are intended to be used as a starting data model and extended as necessary.

The process by which data is imported into the 1:1 data model, the way it is used by the Pega Customer Decision Hub Application, and how the data is stored, altogether is called the Customer Insights Cache (CIC).

Data is migrated to the Customer Insights cache


How is the Customer Insights Cache used?

The 1:1 data model is a flat structured record of the customer data in a format optimal for performance. Other flattened entities, for example, account data, transactional data, action insights, or behavioral data enrich the customer data and are associated with it through the Customer Profile Designer (CPD). This extended data model acts as a cache and is not the system of record.

A high level overview of different data sources


Data from the systems of record must be imported into the 1:1 data model using real-time and batch import data jobs in Pega Customer Decision Hub and must be periodically refreshed based on business needs and requirements.

In addition to the data that is imported from external sources, data is also generated within Customer Decision Hub. The Interaction History is the internal data layer that stores information about the interactions with customers, for example, how a customer engaged with the company, which actions they showed interest in, how they responded to a service message, and so on. Customer Decision Hub uses Interaction History Summaries to aggregate the interaction history data into meaningful summaries and then utilizes the data as predictors in adaptive models and contact policies. Interaction History Summaries aggregate interaction data in real-time, and new aggregates are configured using Customer Profile Designer.

You can also define additional summaries in Customer Profile Designer on any streamed data source to improve the quality of decisions.

The Customer Insights Cache is not a source of reporting, but rather, a source of information to help make accurate decisions. It should contain only the data that is actively used in engagement policies, predictive models, and for personalization. Also, as a best practice, avoid creating nested structures when extending the 1:1 data model. This can affect system performance negatively and complicate the creation of new eligibility conditions or using the properties as predictors in adaptive models.
To speed up the setup of your Customer Insights Cache, use Pega-provided 1:1 industry data model components when applicable. You can use these accelerators during the initial setup of Customer Decision Hub in the application setup wizard, or you can download them from Pega Marketplace and then manually apply them.

Note: Currently, Pega offers a data model template for the following industries:
1:1 Insurance Data Model
1:1 Communications Data Model
1:1 Healthcare Data Model
1:1 Financial Services Data Model

During the initial phase of your implementation project, you perform a data mapping workshop.

For each industry, the data model documentation is available in XLSX format, and it includes crucial information on each entity and attribute. The workshop helps you to visualize the structure of the industry data model and to map it to your existing data model. Typically, you perform a gap analysis after the data mapping workshop to extend the data model with new attributes and entities that are specific to your requirements.

After you complete the data mapping workshop, run the setup wizard, and then apply the appropriate industry data model that is specific to your use case. Completing the data mapping workshop early in the process gives you a head start in development and helps data architects work on database-related tasks right away.

After you configure Customer Decision Hub, create new and extend existing entities by using the output of the gap analysis.

To extend an existing entity stored in the database:

  • Expose the property in the database table,
  • Create the property in Pega,
  • Map the property in the class definition.

To create a new entity in the database:

  • Create a new table in the database,
  • Use the utilities that come with Pega PlatformTM to generate all the artifacts.
  • Create a data set to access the data in the database table.

If the attribute or entity is sourced in a Cassandra dataset, you do not need to expose the property or do additional mapping in the class rule. For more information about configuring the customer insights cache, see Setting up the Customer Profile.

Use Customer Profile Designer to build the relationship among the new entities, configure your customer insights cache, and then ingest data from the data warehouse or data lake into Customer Decision Hub. It is also possible to manually import data using a CSV file.

Development is a working process. As new business requirements arise, so does the need for new attributes and entities. The project team builds these new requirements in the development environment and then migrates them to higher environments through an enterprise change pipeline in a continuous development cycle.


Tip: To practice what you have learned in this topic, consider taking the Adding a new property to an existing entity challenge

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