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.
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