How To Build A Data Hub For A Bank

Suncorp CDI-MDM

Implementation In The

Wake Of A Merger

Glenn Mead IT Architect Monica Smith CRM Architect

Australia’s 6th _{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

Existing CDI / MDM Capability

Suncorp

• 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)

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.

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.

SOURCE

SYSTEMS CUSTOMER (CDI) HUB CRM (CFDM)ANALYTICAL 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

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).

CDI Context

The joys of having Marketing as your partner / customer => no complex system diagrams allowed!

Product Selection

Source: Gartner 2008

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

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

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.

• 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

POC Highlights (Initiate)

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?

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.

Technology Set

• Initiate Matching algorithm, Data Model, Framework • Intech Standardisation (name, address, email, 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

• 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.

• 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).

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.

So - Where Do We Want To Be?

SOURCE

SYSTEMS CUSTOMER (CDI) HUB CRM (CFDM)ANALYTICAL 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

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,

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