Discussion deck - DataNight SG - 28-Apr-2016. BiData Visualised; opportunities pyramid in a data driven organization; challenges and pitfalls in BigData adoption from leadership vision, management direction to technology and infra readiness.

1. The BigData Ready Bank
- Vijay Richard

2. BigData Visualized

3. The Data Driven Organization – Maturity / Opportunities Pyramid

4.  Leadership Vision
 Technology vs. Business Goals
 Interest, charges, commissions, trade finance… vs. monetization
 Is traditional reactive banking enough?
 Churn (npath, CLV)
 Delayed services (machine failures, queues)
 Does validated learning drive process change?
 Operational research – KPI?
 Management Direction
 Top-down vs. bottom-up
 Prerogatives
 Execution capability
Organizational Mindset

5.  Challenges
 Skillset
 Framework investment (restart-ability, operational metrics, validations, automation)
 Data Lake vs. Data Pond vs. Data Sewer
 Infra – readiness
 Integrated Warehouse
 Network bandwidth
 Internal / External real-time integration
 Pitfalls
 Business adoption (relates to business goals)
 Data Security Audits (perimeter, access, isolation, encryption)
Challenges and pitfalls

6.  ~200 internal source systems
 ~2-3 disparate partially data marts
 ~150 entities and ~2500 attributes in the integration layer
 ~5-10 data marts from integration layer serving departments
 SAS, R, QV, Tableau, BO – reporting and predictive modeling tools
 Technology team size ~150-200, Business Analysts ~20-30 (including 5-10
Data scientists)
Typical Banking Data Warehouse

7. Thank You