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Big Data Case study - caixa bank

How CaixaBank uses big Data in order to Anticipate the needs of its customers

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Big Data Case study - caixa bank

  1. 1. Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Big Data in Banking – How CaixaBank Uses Big Data in Order to Anticipate the Needs of its Customers Seoul 17 Sep 2015 Chungsik Yun Oracle Consulting Technical Manager Chungsik.yun@oracle.com
  2. 2. Copyright © 2014, Oracle and/or its affiliates. All rights reserved. | Program Agenda Financial industry in major transition European leader How can I launch my journey 1 2 3 2
  3. 3. Copyright © 2014, Oracle and/or its affiliates. All rights reserved. | Banks are fundamentally changing the way they serve Customers Historically, banking systems have been product and account centric Product Out But the demand on them is to be truly customer centric … Customer In Digital Engagement Digital Experience Checking Product Definition Accounting Eligibility Channels Master Mortgage Product Definition Accounting Eligibility Channels Master Credit Card Product Definition Accounting Eligibility Channels Master 4
  4. 4. Copyright © 2014, Oracle and/or its affiliates. All rights reserved. | Data is at the heart of “Customer In” - Leaders convert Data into Value “One of the strategic objectives is that CaixaBank becomes a European leader in the use of Big Data and generates value from analyzing its customer data. In order to do that, CaixaBank has partnered with Oracle to develop a new technology platform that can help improve the business and enable the bank ‘to anticipate the needs of customers with a 360 view of the customer’” Juan Maria Nin, CEO CaixaBank Expansión (Spain), 26 Mar 2014, translated from Spanish App Store Adaptive STP App Capture Document submission e-signature Customer 360 Fine grained segmentation Mobile Payments Contextual Selling Real-time Bundles Dynamic Pricing PFM Tools Product Comparison Omni channel Self service Automated workflows LowerCost Foundational Transformational UpliftRevenue 5
  5. 5. Copyright © 2014, Oracle and/or its affiliates. All rights reserved. | Customer background One of the leading banks in the Spanish market 7
  6. 6. Copyright © 2014, Oracle and/or its affiliates. All rights reserved. | Customer background One of the leading banks in the Spanish market 8
  7. 7. Copyright © 2014, Oracle and/or its affiliates. All rights reserved. | 9 Current Situation As-Is Architecture & current limitations Assessment for Continuous Innovation Capabilities • On the 21st Century things can not be done in the way was designed in the previous Century. Current limitation sensed by observed Business needs:  Agility, flexibility and capability for transformation  Business users acquiring emerging roles/skills and able to take advantages by information analysis  Information Discovery – “Data Democratization” on/trough Internal data External data Leveraging latest technologies available (Big Data, Advanced Analytics…) • Business and Competitiveness on risk if the Information Architecture is not flexible enough to embrace the change of paradigm Agility affected by complexity on ELT, Lack of agility due to complexity • Data hijacked by OLTP, Silos and Complexity Over decades, the Informational Systems Architecture have been evolved in a way that the data goes from Transactional and Operational Systems to the Informational and Analytical Data Marts through complex and thus, expensive processes, resulting • Limitations/dependencies on to current IT infrastructure Difficult to access to unstructured formats, limited scalability, complexity on providing SLAs…
  8. 8. Copyright © 2014, Oracle and/or its affiliates. All rights reserved. | “4 main goals: Consolidate 17 data marts into ONE. Improve relationships with customers by offering better products. Improve employee efficiency. Centralize regulatory information.” Luis Esteban, Chief Data Officer, CaixaBank Motivation 20+ years DWH in Mainframe 10
  9. 9. Copyright © 2014, Oracle and/or its affiliates. All rights reserved. | Phased approach 2. Apps/Cases built on the Data Pool 1. Build Data Pool + Data Factory Engine for All Data 11
  10. 10. Copyright © 2014, Oracle and/or its affiliates. All rights reserved. | CaixaBank started with building the foundation for future business driven use cases - the Data Pool Deposits Pricing Source - CaixaBank ATMs Customized Menus Online Risks Scoring Online Marketing Automation Sentiment Analysis 12 Business Use Case Examples
  11. 11. Copyright © 2014, Oracle and/or its affiliates. All rights reserved. | 13 These Use Cases are dependent on multiple Data Sources that will feed into the Data Pool Source - CaixaBank
  12. 12. Copyright © 2014, Oracle and/or its affiliates. All rights reserved. | CaixaBank Logical Architecture 14
  13. 13. Copyright © 2014, Oracle and/or its affiliates. All rights reserved. | Data Pool - Platform Architecture DC2DC1 IB IB Backup Snapshot TSM 10GbE VTL VTL Oracle DataGuard FC IB IB IB IB ZFS Replication BDR Replication ZS-3 Backup Oracle RMAN TSM FC 10GbE Backup Snapshot Data Pool Data Pool ZS-3 Backup Oracle RMAN SANSAN 15
  14. 14. Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | IM Architecture – Products Mapping 16 Actionable Events Event Engine Data Reservoir Data Factory Enterprise Information Store Reporting Discovery Lab Actionable Information Actionable Insights Data Streams Execution Innovation Discovery Output Events & Data Structured Enterprise Data Other Data Oracle Information Management Reference Architecture Oracle Event Processing Oracle Golden Gate Apache Flume Oracle Data Integrator / Oracle Enterprise Metadata Manager Oracle Real-time Decision Cloudera Hadoop Oracle NoSQL Database Oracle R Distribution Oracle Database Oracle Advanced Analytics Oracle R Enterprise Oracle Big Data Connectors Oracle Business Intelligence Enterprise Edition Big Data Discovery Oracle R Data Factory Engine
  15. 15. Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Oracle Confidential – Internal/Restricted/Highly Restricted 17 Main requirements Solution Initially, 900 different file structures to be ingested. Nowadays 2.000 and 3.000 in the future and they are not known at the beginning ODI code generator based on descriptions and common patterns Deploying new sources has to follow a procedure Files are first ingested in a test environment, checked and then the automatic ingestion is promoted to production Loading dependencies based on data loaded and finishing of the previous load A custom scheduler for controlling loadings and dependencies “Datascientists” need an area to “play” with the data The discovery lab has been created and tools for managing data & metadata between areas Access to data has to be protected & audited A custom solution based on Oracle products for giving access & auditing Monitoring and reporting on the loadings is needed All actions generate traces that can be reported. Monitoring modules are implemented. Why DFE?
  16. 16. Copyright © 2014, Oracle and/or its affiliates. All rights reserved. | Data Factory Engine Methodology and Governance • Security (BDA!) •Oracle Big Data SQL 18
  17. 17. Copyright © 2014, Oracle and/or its affiliates. All rights reserved. | Module description 19 • Automation of the landing of the information in the Data Reservoir, making simpler adding new sourcesIngestion • Layered structure and keeping in sync the different layers based on the dependenciesLogical Structure • Management of the samples for testing in previous environments + Automation of promotion of codeEnvironment lifecycle • Speeding the development of projects by providing code generators & knowledge modulesCode Generation • Scheduling the loadings by dependencies and resources availableScheduler • Managing the access to the information stored. Object, row & column filters based on metadataAccess Control • Functional monitoring & reporting based on metrics like amount of ingested information, delays on loadings, etcMonitoring & Reporting • Recording the operations executed by usersAudit • Tools for supporting the modeling of the structured information and also the metadata associatedModelling support • Rules and guidelines for developing projectsGuidelines & Best practices • Data lineage & impact analysis of changesData Management • Metadata management & project configuration data maintenceApplication Governance
  18. 18. Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Data Factory Engine - Ingestion overview Oracle Internal 21 Stage ConsumerEnterprise HDFS / NoSQL Oracle DB Data Pool strongly typed data strongly typed format conversion (GBs/TBs) HDFS data mapping weakly typed data Oracle Data Integrator Metadata Data Factory Engine
  19. 19. Copyright © 2014, Oracle and/or its affiliates. All rights reserved. | DATA POOL Costs reduction with controlled TCO Improvement in Time to Market & Time to Value Flexibility and Agility Advanced Analytics (Interactive, Discovery, etc) Any type data management High Performance with Homogeneous & Scalable platform End to End support to Oracle Solution Data Factory Engine Benefits and Summary 23
  20. 20. Copyright © 2014, Oracle and/or its affiliates. All rights reserved. | Oracle Confidential – Internal Caixa’s Use Cases - Roadmap Data and Systems Consolidation Credit Risk Calculation Resource Mgmt at Branches Churn Detection Regulatory compliance Best Offering at Branch Desk Analysis of trading chats Web abandon. detection Fraud Detection Analytical Processing RT Processing Data Governance Sandboxing and Rapid Devepment Discovery Data Aging Location based offering Mainframe Offloading 24
  21. 21. Copyright © 2014, Oracle and/or its affiliates. All rights reserved. | Benefits Línea Abierta (main web site) • 3M login/day to Online banking • Real-time messages (commercial & non-) • Data Pool & Oracle RTD  peak capacity 1600 req/s • Business impact: 39% click-thru increase for new campaigns Premia-T • Real-time proactive SMS triggered by credit-card payments • Geolocation • 1.5M payments a day • Oracle RTD & OEP 26
  22. 22. Copyright © 2014, Oracle and/or its affiliates. All rights reserved. | Program Agenda Financial industry in major transition European leader How can I launch my journey 1 2 3 27
  23. 23. Copyright © 2014, Oracle and/or its affiliates. All rights reserved. | Data Factory Engine Innovation Workshops Discovery Lab Data Reservoir DW Offload Information Management Deep Dive Fast Data Big Data & AnalyticsRapid Start Packs 28 How To Get Started - with Oracle Consulting Transform the business Lay the foundation Pilot BIG DATA ANALYTICS BIG DATA APPLICATIONS BIG DATA MANAGEMENT BIG DATA INTEGRATION CREATE VALUE FROM DATA 28
  24. 24. Copyright © 2014, Oracle and/or its affiliates. All rights reserved. | 29 Oracle Big Data Consulting Framework 2.0 29 Technology Rapid Start Packs Acquire Organize Analyse / Decide - NoSQL - Real Time Decision - Big Data SQL - Big Data Connectors - Advanced Analytics - Endeca Information Discovery Architecture ininInnovation Workshops Big Data & Analytics Information Management Deep Dive in Roadmap & Blueprint Solutions Discovery Lab Data Factory Engine Apps Store for Oracle BDA DW Offload Data ReservoirFast Data Big Data Competency Centers Big Data Workshops
  25. 25. Copyright © 2014, Oracle and/or its affiliates. All rights reserved. | Business led Innovation Workshops Divergent and Convergent Thinking. Iterative process. “ …finally Business Value through an innovative approach… „ Big Data & Information Management MasterClass Big Data Architecture Solutions & Leading Practices. “ …Vendor agnostic. Set the foundations of your Big Data Architecture… „ 30 Analytical Capability Your Business Use Cases. Swiftly Discovered. “ …empower Data Scientists and Analysts in your Discovery Lab… „ Roadmap & Blueprint Design your Big Data Solution. “ …deep dive Big Data Eng Systems and Technologies… „ Big Data Workshop
  26. 26. Copyright © 2014, Oracle and/or its affiliates. All rights reserved. | Oracle Confidential – Internal/Restricted/Highly Restricted 31

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