Decision Architecture

Announcing the start of a new series of posts relating to making data science actionable. I’ll to go deeper into how data goes from a repository of “stuff” into a system and process that uses the best of what technology is available to make an action happen. This group of related posts will be tagged #DecisionArchitecture.

We will explore practical applications of data and analytics and share how all the pieces fit together. It will start with people and with organizational strategy and culture. Topics will also touch on processes which direct the products, services, and technology deployed around taking action from data. Sometimes posts will be technical and sometimes anecdotal, but they should all tie to the theme of “Making Useful Data Science Happen.”

Topics to cover (an evolving table of contents):

1. Define Decision Architecture
   • Framework for making Data Science useful
   • Rules-based and rule-free decisions
2. Case studies
   • Examples of well-designed decision architectures
   • Real-time fraud detection
   • Credit decision models
   • Advertising examples
   • Examples of decision architecture failures
3. Components
   • Databases
     • Event-driven architecture
     • High speed / in-memory databases
     • Slow speed / “big data” storage
   • Technologies
     • Hardware
     • Modular design
     • APIs
     • Containerization
     • Lambda / Kappa Architecture
   • Tools & Vendors
4. Processes
   • Data governance
   • Model management
   • Product management
5. People
   • Key skills
   • Roles and organizational structure
6. History
   • Early decision models
   • Innovations
   • Key people and organizations
   • Thoughts on the future

This series will be a place to share notes and collect content around these related topics. Please share your thoughts and comments to help make #DecisionArchitecture useful, interesting, and entertaining!