Decision Platform – Reference Architecture

Vision for Decision Platform

AS-IS State of People, Process, Technology and Data Aspects:

Silo department and team structures
Monolithic applications and designs
Segregation of duties across different teams
Focus on operations vs. delivering experience
Too much emphasis on achieving short term gains vs. following long term vision and strategy
Data is inherent to specific systems that are aligned to a product stream/ department
Decision and Actions focus is limited with narrow focus

TO-BE State of People, Process, Technology and Data Aspects:

Market differentiation achieved through internal collaboration and overcoming silo department and team structures
Overcome data silos by investing in creating data pipelines that is shared across applications and teams
Cross cutting concerns across teams with focus to deliver customer experience
Focus on delivering experience while automating operational repetitive work
KPI led by delivering experience to end consumers including internal and external to organization
Data democracy with technology governance
Decision and Actions is consolidated with broad focus on delivering experience

Key Considerations for Reference Architecture

Simplify decisions and rule complexity by segregating rule data vs rule modeling
Focus on 100% automation of decision authoring, validation and analytics
Provide scalability for run execution platform by adopting cloud and containerization
Redefine and integrate business processes with decisions and data analysis
Availability of back-end master data and associated refresh mechanisms on change
Analytics should be the glue between authoring and runtime; Same team should be enabled with data and insights in real-time
Minimize out of the system coordination by teams by auto processing and categorization of actions and their review