✨ Operational Data Store (Layer):

• Same as Data Fabric, Operational Data Hub
• Layer between existing data sources and consumers
• Don't need to replace legacy systems
• Combine data from multiple systems into a single hub

🔍 Collect data sets from different systems

• Provide a complete picture of data

🔥 Ad-hoc analytical tools

• Real-time analytics
• Up-to-the-minute view of the whole business
• Without interfering with operational workloads

💡 Example:

• Day-to-day responsibilities
• Required Service Level Agreements (SLAs)

🏗️ Foundation for re-architecting

• Iterative approach to digital transformation
• Parallel to legacy and new systems
• Legacy system continue to work without interruption

🚀 Legacy Modernization:

• Build new business functions faster
• Scale to millions of users
• Data consumers access Operational Data Layer (ODL)

💎 Benefit:

• Access entire data set
• Customer single view
• Artificial intelligence processes

💻 Data as a Service:

• Operational Data Layer gathers all important data in one place
• Applications and analytics get the full picture of enterprise data

☁️ Cloud of Operational Data Layer:

• Deployed on the same cloud provider
• Same regions as its consuming systems
• Gradual, non-disruptive approach to cloud migration

🔍 Challenge:

• 70 different systems in 15 different screens

🔑 Solution:

• Single view
• Use only one screen to access all the information
• Real-time representation
• Customer 360
• Single views of products, financial assets, entities relevant for business
• Data from multiple sources

🚀 Faster customer call times

• Analyze customer data for cross-sell and upsell opportunities

📋 Use Case:

• Customer service representatives
• Fraud and risk systems
• Sales and marketing staff
• Quantitative analysts
• User online account

💪 Mainframe Offload:

• Single point of failure
• Taken offline for maintenance
• Easier to serve mainframe data to new digital channels without straining legacy systems

🤔 Challenge:

• Requires a complete view of enterprise data
• Warehouse or a Hadoop-based analytics
• Can't meet today's demand for real-time analytics
• Loaded in daily or weekly batches
• Long-running queries taking hours

🔍 Solution:

• For real-time decisions
• Up-to-the-minute state of data
• Low latency analytics queries
• Ad-hoc questions
• Example:
  • Recommendations for customers
  • Personalizing content based on user info
  • Machine learning on enterprise data to extract new insights
  • Improve operational efficiency

🚀 Modernization:

• Exposing existing data to new applications
• Don't have potential impact to legacy systems

💼 Data Steward:

• Get data from application database then store to operational data layer
• Use ETL (Extract, transform, load), CDC (change data capture)
• Data must fulfill requirements
• Frequency of data transfer
• Without affecting current producing & consuming applications

🤖 Reference:

https://www.mongodb.com/resources/basics/implementing-an-operational-data-layer
Implementing an Operational Data Layer

https://www.mongodb.com/resources/solutions/use-cases/mainframe-modernization-reference-architecture
Mainframe Modernization Reference Architecture

Editor

Danny Chan, specialty of FSI and Serverless

Kenny Chan, specialty of FSI and Machine Learning