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The big data ready bank

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.

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The big data ready bank

  1. 1. The BigData Ready Bank - Vijay Richard
  2. 2. BigData Visualized
  3. 3. The Data Driven Organization – Maturity / Opportunities Pyramid
  4. 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. 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. 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. 7. Thank You

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  • LeonardVijayRichard

    Apr. 28, 2016

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.

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