An Operational Data Layer is Critical for Transformative Banking Applications
Download as PPTX, PDF1 like1,356 views
The document discusses the importance of an operational data layer for transformative banking applications, highlighting the need for digital transformation to enhance customer experience in the rapidly evolving financial industry. It presents key requirements, features, and security measures required for a successful implementation, citing Macquarie's success in utilizing an operational data layer to achieve real-time customer visibility and enhance their consumer banking app. The document underscores the significance of integrating deep analytics, scalability, and security into modern banking architectures.
![Bank App
3rd Party
Customer
New
Application
Web
Applications
Open API
Reuse it inside the business
© DataStax, All Rights Reserved.
Scale On Demand
100% Always On Integrated Search
Isolated Datasets
24/7/365 Expectations
Scalable Low Latency
Embedded Search
Secure Data Exposure
Operational
Data Layer
Embedded
Search
New
Application
Dataset per
Service
Employee
App
Peer Org
[Micro-]
Services
Internal APIEmployee](https://image.slidesharecdn.com/20180118bankingodlwebinaren-180125162343/85/An-Operational-Data-Layer-is-Critical-for-Transformative-Banking-Applications-16-320.jpg)
![The full-scale architecture with analytics
24/7/365 expectations
Mega writes / s
Contextual/Personal
Real-time/Responsive
Batch
Analytics
Operational
Data Layer
Fast-path
Analytics
Multi-model
Scalable write
100% Always On Combine Session and History
Online Stream Analytics
New
Application
[Micro-]
Services
Event API Embedded
Search
New
Application
Web
Applications
© DataStax, All Rights Reserved.](https://image.slidesharecdn.com/20180118bankingodlwebinaren-180125162343/85/An-Operational-Data-Layer-is-Critical-for-Transformative-Banking-Applications-17-320.jpg)
An Operational Data Layer is Critical for Transformative Banking Applications
- 1. An Operational Data Layer is Critical for Transformative Banking Applications Richard Henderson Solution Engineer DataStax
- 2. Todays Agenda • Introduction • Who DataStax are • Why transformation is needed • Customer success stories • The key requirements • The common features that meet those requirements • Where the solution lives in your architecture • How it should be implemented • Why it also works internally • Live Q & A
- 3. Some of our customers
- 4. In a rapidly evolving world… accelerates expectations © 2017 DataStax, All Rights Reserved. Company Confidential
- 5. You must respond User Expectations And Behaviours Regulatory change Non-traditional Players (Fintechs, Challenger Banks)
- 6. The Opportunity is now! Requirements: • Present the data we already have to customer applications. • Combine deep analytics with session data (“fast analytics”) to provide intelligent predictions to applications. • Present the data we already have to internal applications. • Safely and efficiently expose the data we already have to 3rd parties. • Have unified data for a customer across products.
- 7. Macquarie THE CHALLENGE: Drive digital transformation initiatives to enhance customer experience. 7 • Transformed from no retail presence to a digital consumer banking leader in less than 2 years. • Macquarie used DSE as the core of a operational data-layer to enhance rather than replace. • Consolidated data from many existing disparate systems delivers 360o, real-time customer visibility. • Their world-class consumer banking app utilizes real-time analytics and full text search
- 8. ING Focuses on Customer Experience and Micro-Services • Focusing on customer experience ING has moved to a touch-point architecture based increasingly on micro- services • Need for availability, consistency, and scalability • Lots of small use cases, DevOps teams, no ephemeral storage • 12 clusters (4/5 environments) • Cassandra eases availability challenges by being active-active and having an always-on architecture. THE CHALLENGE: Availability. © DataStax, All Rights Reserved.
- 9. Banking Transformation with an Operational Data Layer
- 10. Requirement Zero: Be Demonstrably Secure
- 11. DSE advanced security features © DataStax, All Rights Reserved. ● At-rest Transparent Data Encryption ○ Local Key ○ External Key Manager via KMIP ○ Configuration Value Encryption ○ System Info Encryption ● Authentication ○ Internal or Password Authentication ○ Kerberos Authentication ○ LDAP Authentication ○ Unified Authenticator ○ Proxy Authentication ● Authorization ○ Role Based Access Control ○ Internal Role Management ○ External Role Management ○ Row Level Access Control ● In-flight Encryption ○ Inter-node SSL ○ client-to-node SSL ● Auditing ● OpsCenter Security
- 12. CONTEXTUAL Transformative banking applications have these qualities ALWAYS-ON DISTRIBUTED SCALABLEREAL-TIME © 2017 DataStax, All Rights Reserved. Company Confidential
- 13. Where the operational data layer lives © DataStax, All Rights Reserved. New Application Bank App 3rd Party Web Applications Open API 24/7/365 Expectations Scalable Low Latency Embedded Search Secure Data Exposure ??
- 14. Don’t just add an API gateway and a search engine © DataStax, All Rights Reserved. High Cost Scale Up Front Batch/Mostly Available Separate Search More Complexity Directly Exposed Data Application Integration Service New Application Bank App 3rd Party Web Applications Open API 24/7/365 Expectations Scalable Low Latency Embedded Search Secure Data Exposure
- 15. Add a simple integrated operational data layer © DataStax, All Rights Reserved. Operational Data Layer Scale On Demand 100% Always On Integrated Search Embedded Search Isolated Datasets 24/7/365 Expectations Scalable Low Latency Embedded Search Secure Data Exposure New Application Dataset per Customer Bank App 3rd Party Web Applications Open APICustomer
- 16. Bank App 3rd Party Customer New Application Web Applications Open API Reuse it inside the business © DataStax, All Rights Reserved. Scale On Demand 100% Always On Integrated Search Isolated Datasets 24/7/365 Expectations Scalable Low Latency Embedded Search Secure Data Exposure Operational Data Layer Embedded Search New Application Dataset per Service Employee App Peer Org [Micro-] Services Internal APIEmployee
- 17. The full-scale architecture with analytics 24/7/365 expectations Mega writes / s Contextual/Personal Real-time/Responsive Batch Analytics Operational Data Layer Fast-path Analytics Multi-model Scalable write 100% Always On Combine Session and History Online Stream Analytics New Application [Micro-] Services Event API Embedded Search New Application Web Applications © DataStax, All Rights Reserved.
- 18. Unify your data © DataStax, All Rights Reserved. Mortgage Bank Account House Insurance Life Cover
- 19. DataStax Enterprise © DataStax, All Rights Reserved. Linear Scalability Geographically Distributed Continuously Available Instantaneously Responsive Integrated Search Integrated Operational Analytics Always On, Multi-model, Mixed Workload, Scalable Data Layer