How To Build A Data Hub For A Bank

Transcription

1 Suncorp CDI-MDM Implementation In The Wake Of A Merger Glenn Mead IT Architect Monica Smith CRM Architect MDM Asia-Pacific Summit Sydney April

2 Suncorp Background Australia s 6 th largest Bank; 3 rd largest General Insurer; significant Wealth Management business Merger & Acquisition activity Promina 2007 GIO 2001 Suncorp / Metway 1997 M&A activities are a constant challenge for integration platforms such as CDI (or Data Warehouse, GL etc) Suncorp & GIO had a long history with in-house CDI systems, but all were end of life / unable to expand for new customer base / requirements 1

3 Suncorp Existing CDI / MDM Capability CDI capability (One brand Suncorp across Banking, Wealth and Insurance) Standardise and match customer records Provide customer services to support Sales and Service process Provide basis for CRM capability GIO (M&A) CDI Capability (Multiple brands Insurance only) Standardise and match customer records Provide customer services to support Sales and Service process Promina (M&A) No CDI capability (Multiple brands across Insurance and Wealth) Customer Analytics (Group) Suncorp/GIO in place, Group view under Development 2

4 CDI Project Background Initial Drivers from Suncorp / Promina Integration The CDI Hub, providing a view across the entire group = data to enable: Analytics & Marketing Understand group customer base, Brand cross-over Limit cannibalisation Single marketing platform Customer Service: Support for existing business processes after system rationalisation Short-term outcomes - aligned to medium/long-term Customer & IT roadmaps. Having these roadmaps in place & socialised was crucial to funding when (M&A / Integration) opportunity arrived. 3

5 Why CDI / MDM? 360 Degree view of customer Group-wide view for analytics / marketing Allow expansion to operational CRM / customer service Multiple brands Provide operational views by brand Understand customer holdings across brand House-holding - Properly understand share of wallet Data Quality Reporting Improvement on Customer Service / Understanding Pin-point problem systems / monitor DQ over time Customer data partitioning / protection, e.g: Portfolio Protection (products sold through brokers / channels) Protection of Joint Venture Customer Data External Prospect Lists Customer relationships A comprehensive uplift from current capability is being put in place. 4

6 CDI Context SOURCE SYSTEMS CUSTOMER (CDI) HUB ANALYTICAL CRM (CFDM) OPERATIONAL CRM Data silos Quality Controller The Brains The Playmaker Siloed storage of customer, product and transactional data. No single view of the customer exists Verifies and manages data quality from all source systems at an enterprise level Helps to understand and anticipate the needs of current and potential customers for tactical and strategic decision making Automated support to front line business processes including customer contact (sales, service and marketing). The joys of having Marketing as your partner / customer => no complex system diagrams allowed! 5

7 Product Selection IBM Full featured CDI Hub with extensive data model / large number of pre-built services Oracle Siebel UCM Extensive financial services data model Best suited to Siebel CRM sites Siperian No Australian presence at time of selection Source: Gartner 2008 Initiate Best of Breed vendor offering fast & advanced matching, quick start-up Australian presence expanding Background is Health Services Single View of patient (hence strong in matching / client protection) 6

8 The Initiate Product Suncorp chose Initiate as a partner with the right engagement model and people for a mutually successful project. Key Focus Areas for Selection: CDI specifically (not wider MDM) Fast implementation Flexible licensing model to suit the Suncorp implementation model Learnings: Implementation required more lines of code than expected would have structured project differently if this was understood. Initiate partner products (Intech, Clover) caused more challenges than expected. 7

9 POC Highlights (Initiate) 2.2m records standardised, matched & loaded < 3 hours Data model / Web services extended CRM-ish front-end connected in < 2 weeks ETL extract developed suiting Marketing requirement Helped clarify roadmap & eased many concerns: Supporting various project timeframes & requirements (Relatively) simple transition from existing systems Learnings from our POC: Initiate model had many advantages over existing CDI systems (brand protection, source data retention, flexible matching algorithm). Many puzzles remained including: what to keep in the Initiate model versus alongside?; will CRM searching requirements be satisfied? Note - Should have done separate POC on Initiate partner technologies. 8

10 Initial Phase 24 sources 20 million customer records 2 existing CDI Hubs to synchronise with Feed to marketing data mart External data (prospect lists) Some learnings on the period between POC and project start: Needed a longer planning / decision making period before initial project phase. Some project technology choices & scope decisions rushed / impacts not fully realised. 9

11 Technology Set Initiate Matching algorithm, Data Model, Framework Intech Standardisation (name, address, , phone) Clover ETL (data transformation) Java (business logic, called by Clover) Red Hat Linux (App Server note CPU intensive activity) Oracle DBMS (note, Initiate makes little use of many Relational DBMS features) Tableaux Auto-Deployment Notes: First Suncorp usage of Linux for major CPU intensive data manipulation Auto Deployment software critical to management of multiple environments with complex software sets 10

12 Challenges Business Engagement Difficult to get business ownership for customer data / required (informed) involvement BUT Crucial to success - Project would have failed due to IT politics without Business Support (and funding) - Customer Roadmap socialisation prior to opportunity made funding / direction decisions far easier - Business representatives on the project will help drive further adoption Tangible business objectives, in terms of data quality metrics, have been crucial to getting business agreement on CDI Hub success Learnings Have plans in place, to enact as opportunities arise Agree target data quality metrics upfront and focus testing on measuring against these metrics. 11

13 Challenges - Data Projects Need to have right resources (e.g. data analysis) Need appropriate testing methodology (data management versus functional testing) Difficult but crucial to get business agreement on requirements : - Data Sourcing / Target Data Model - Data Quality Metrics - Functional requirements What is working for us Split functional development from data quality improvements (matching / standardisation) separate projects with differing resource requirements / timeframes etc). 12

14 Value - Data Quality Reporting by Source Value Provide summary reporting and details of DQ levels / errors for each source. Understand where the real issues are, allow focused resolution Get early warning of DQ degradation Enablers Initiate data model (retention of source data) Data Lineage information One of the highest value differences with our Initiate implementation is provision of regular data quality monitoring by source. Key DQ metrics are reported by source as a whole and by time period (understand DQ trends), and compared with the whole base. 13

15 So - Where Do We Want To Be? SOURCE SYSTEMS CUSTOMER (CDI) HUB ANALYTICAL CRM (CFDM) OPERATIONAL CRM Data silos Quality Controller The Brains The Playmaker Siloed storage of customer, product and transactional data. No single view of the customer exists Verifies and manages data quality from all source systems at an enterprise level Helps to understand and anticipate the needs of current and potential customers for tactical and strategic decision making Provides automated support to front line business processes including customer contact (sales, service and marketing). 14

16 What does this mean? Integration into Customer Analytics Roadmap /Marketing Roadmap Create Services layer to support Operational Systems Integrate to CRM Front-Ends Real-Time feeds to / from some Source Systems Decommissioning: Legacy Hubs Data Warehouse feeds Source system feeds 15

17 Questions? 16

18 End