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Using Smart Technologies to Modernize and Transform the Customer Experience in Banking.

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Data is a valuable asset to your business – driving competitive advantage and transforming the customer experience. However, most organizations are unable to leverage it.

Discover a case study on Using Smart Technologies to Modernize and Transform the Customer Experience in Banking. Norman Wren, former Director of Technology and Operations at Santander, and Dave Jones, Vice President of Product & Industry Marketing at Nuxeo, will show how to defuse data issues by using smart technologies like AI, micro-services, and modern content services.

In our latest Finovate webinar, Wren and Jones will discuss real world examples of practical business applications and solutions that can help you:

• Leverage existing data systems
• Drive value from unstructured data
• Have flexible, yet secure, and auditable data
• Remove obsolescence, reduce costs, and maintain compliance

Learn how to modernize the customer experience in banking with smart technology and turn your data into a valuable asset.

Publicada em: Tecnologia
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Using Smart Technologies to Modernize and Transform the Customer Experience in Banking.

  1. 1. The Ticking Time Bomb of Data David Jones & Norman Wren
  2. 2. Introductions Digital Transformation – A Reality Check The Ticking Time Bomb of Data Q&A 1 2 3 4 Agenda
  3. 3. Introductions 3 1 David Jones  VP Product Marketing  Nuxeo 2 Norman Wren  Former Technical and Operations Director  Santander
  4. 4. Introductions 4 David Jones  VP of Product Marketing  Nuxeo  @InstinctiveDave 2 Norman Wren  Former Technical and Operations Director  Santander Digital Transformation A Reality Check
  5. 5. Digital Transformation in Financial Services Massive Spend Average $42M in 2018 Rising to $45M in 2019 Purpose 66% Customer facing innovations Success? 88% - project delayed, reduced scope, or cancelled 26% - Digital Transformation = Insurmountable Task Statistics courtesy of Couchbase
  6. 6. “Everyone who hears these words of mine, and doesn't do them will be like a foolish man, who built his house on the sand. The rain came down, the floods came, and the winds blew, and beat on that house; and it fell—and great was its fall.” — Matthew 7:24–27
  7. 7. Building on Sand? Legacy Systems Disconnected Systems Search not find Customer Experience Mobile & Web Apps Performance & Scalability
  8. 8. The Ticking time bomb of Data Making Sense of Legacy Data Norman Wren
  9. 9. Why is this important? Customer Expectation: Customer in control Unlimited data access Always on 24 x 7 Real time Added value services Regulation : Customer rights Data portability Open access to third parties Remediation of historic practices Internal Driver: Exploit data assets
  10. 10. Legacy Data Challenges • Fragmented data ; not real time; internal view; unstructured data; access limited • Multiple formats; limited documentation; Knowledge gap • Integrity within applications - not across • Degradation over time • Security and data leakage • Obsolescence • Compliance with Regulation • Cost of Change; Time to market • Consolidation of data stores. • Architecture and technology compatibility • BAU Running costs • No scalability • Access limitations • Poor schema design Access Data Quality and Integrity Risk Cost Performance
  11. 11. Value: 3 Basic Requirements  Find Data  Document attributes and meaning  Understand usage and context  Define Architecture  Set usage, access and security rules  Build governance and ownership  Organise around common Business Purposes  Make accessible through common access layer  Use Meta data to organise and add value  Build new Capabilities – Data Driven Understand the Data Manage the Data Exploit the Data 3 1 2
  12. 12. Considerations Archaeology: Find and document data and how used Architecture: Define data architecture and principles Data ECO system: Distributed data Define data usage Update; query; analytics Common Layer: To bridge technologies Scaleability Cloud processing; distributed data Ownership Governance and accountability
  13. 13. Key Points Data as an Asset needs: • Clear Ownership and Accountability • Knowledge and documentation Meta data • Clear Understanding of usage and value • Data architects and engineers • Architectural readiness • Capability • Data Strategy
  14. 14. Stick or Twist?  Obsolescence  Security  Maintenance  Cost  Risk  Cost  Risk  Data Quality  Integrity  Business case  Less Risk  Unlock assets  Bridge old and new  Create data eco system for the future Do Nothing Big Bang Migration Co-existence/ Common Layer
  15. 15. Summary Data knowledge and documentation fundamental • Hard work and time consuming Clear target architecture addressing data • technology stack and data usage Common layer to separate out data from business processing Avoid migration if possible • Avoid pitfall of access in situ Define common Business purposes Build road map • Balance value, cost and risk Invest in capability
  16. 16. Questions?
  17. 17. Thank you! www.Nuxeo.com