Pega Customer Decision Hub learning paths
Pega Customer Decision Hub learning can be approached from different angles. You will see how to start and progress across various Pega Academy missions depending on your role and needs. The learning path is specific to the role you choose, while the missions are specific for the two different phases in a 1:1 Customer Engagement project: the Implementation phase and the Business-as-Usual phase.
1:1 Customer Engagement roles
A 1:1 Customer Engagement implementation project consists of two phases: the Implementation phase, which takes place before launch, and the Business-as-Usual (BAU) phase, which usually occurs after the system goes live. Both phases involve specific roles that are responsible for configuring the system.
The Implementation phase includes the System Architect, Decisioning Architect, and Data Scientist roles. In the Business-as-usual phase, the roles involved are Team Lead, Next-Best-Action Analyst, Next-Best-Action Specialist, Next-Best-Action Designer, and the same Data Scientist who participates in the Implementation phase.
Depending on the role that you wish to pursue, you might want to consider different learning paths in Pega Academy. First, you learn about the current Pega Academy Missions that teach individual tasks specific to each phase. Later, you discover the Pega Academy Missions that are most relevant for each role.
In the following figure, click the + icons to learn more about the 1:1 Customer Engagement roles:
Enablement path for implementation roles
In the following figure, click the + icons to learn more about the enablement path for implementation roles:
Enablement path for business-as-usual roles
In the following figure, click the + icons to learn more about the enablement path for business-as-usual roles:
Decision Management Enablement
Pega's decision management capabilities extend beyond the Pega Customer Decision Hub, offering versatile applications across various domains through different Pega products. One notable application is Pega Credit Risk Decisioning, which leverages these capabilities to automate risk assessment and streamline decision-making processes in loan application processing, thereby enhancing the accuracy and efficiency of credit risk evaluations. Additionally, Pega Process AI™ integrates artificial intelligence with decision management features to develop self-optimizing business processes. This integration enables organizations to continuously improve their operations by making data-driven decisions and adapting to changing conditions in real-time, thereby maximizing operational efficiency and effectiveness.
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