This document summarizes Manulife's global data strategy and data operations in Melbourne. It discusses establishing a balanced hub-and-spoke model to provide global consistency, talent, and dynamics. The data offices follow the business roadmap and have engineering, governance, and analytics functions. The enterprise data lake setup includes three physical instances across regions with identical technology stacks for operations, preview, validation, and DR. It ingests and stores various data sources and enables advanced analysis, digital connection of systems, and automated reporting use cases across regions.

1. Dataworks Melbourne
Manulife Journey

2. Our Strategy
1

3. OUR GLOBAL
DATA
STRATEGY
1. Invest in Data Talent
2. Establish “Fit for Purpose” Data Governance
3. Build Open Enterprise Data Architecture
4. Establish Modern Data Infrastructure
5. Foster Data & Analytic Innovation

4. OUR TECH STRATEGY

5. Our Organization
2

6. The “balanced” hub
and spoke model has enabled:
Ø Global consistency
Ø Bigger pool of talent
Ø Healthy dynamic

7. # 1
Data Offices are part of CIO org, and functionally
align to CAO
# 2
Data Office roadmap follows
business roadmap
# 3
Data Offices have 3 functions:
Engineering, Data Governance and
Analytics Enablement

8. How do we
deliver?
3

9. 9
Support
DESIGN
DEVELOP
OPERATE
ENGINEERING
& Platform
DATA
Enablement
ANALYTICS
Enablement
Tech Ops Data Ops Analytics Op
Support
Tech
Engineering
Solution / Data
Architects
Data Lifecycle
Management
Eagle Team
Member
Tools
Implementation
Analytics Design
& Implementation
Enablement &
Governance
Design procedural &
technical solutions to
address business
needs
Implement designs
across technology,
process and
operational domains
to solve business
issues
Ensure adherence to
BAU process and
procedures. Maintain
technology to operate
as designed,
addressing issues as
they arise
Test Management
BA / DA management

10. Eagle Team
Squad 1 Squad 2 Squad 3
Platform and Data Operations (Supporting Service Now ticket using Kanban Model)
Squad 4 Squad x
Platform BU1 BU 2 BU 3 BU X

11. Our Enterprise
Data Lake
4

12. Our Set Up
Ø One Global Design:
SAME TECH STACK
Ø 3 physical instances:
US, CA and Asia (HK)
Ø 4 Clusters each instance:
Operation, Preview, Validation & DR
Ø All on Azure Production

13. Data
Sources
Ingestion
Services
Core Data Lake
Consuming
Services
Target Apps
Batch
Ingestion
Real-Time
Ingestion
RDBMS
(e.g. CAS / AWD)
Storing Processing Publishing
Unstructured
Data
Structured
Data
Semi-
structured Data
Streaming
Data
Operational
Serviced
Analytical
Services
Real-Time
Services
Data
Exploration
SQL
Transformations
Non-SGL
Transformations
Pass-through
Processing
Data Search
Platform Capabilities
Metadata Security Scalabilities Resilience User Access
Real-Time
Consumption
Data Query
CRM (e.g. CC2.0)
Hadoop Data Platform
Data Analytics
Microservices
APIs
Social media
data
Our Conceptual Infrastructure

14. Use case sharing
5

15. Advanced Analysis
• Make data available to data
analysts and scientists
• Key deliverables may include:
• Customer experience – process
analysis and improvement
• Sales – promotions, campaigns &
lead generation
• Protection – fraud detection,
investigations
Digital Connection
• Connect data from multiple
systems
• Make that data available to
support strategic technology:
• Sales Force (Customer
Relationship Management)
• Policy printing
• Customer communications
Automated Reporting
• Display information and InSight to
internal and external users
• Also known as:
• Business Intelligence (BI)
• Management information (MI)
• Dashboards
• Reports
VN – Orphan Reassignment , Retention Analysis
JP- Customer and Agent Segmentation, Cross-Sell &
Up-Sell analytics
VN - Agency Dashboards
HK – Ops & CRM Dashboards
Regional - KPI Dashboard, NPS Dashboard
JP – AML Management Reporting
Data Flow supporting ePOS (VN), eClaim (HK & VN),
Contact Center 2.0 (JP, HK), Submitted PC (HK)
Our Use Cases

16. Kevin Love
Manulife Hong Kong
Information Technology Strategy
Digital Connection

17. Master
Dataset
Overview
Agency
Banc.Claims
New
Business
http://10.231.6.5/reports/powerbi/Vietnam/Advanced%20Analytics/VN%20dashboard/Navigation
Presentation

18. Geo Coding Map
VN Branch Transformation
• Geocoding customer and HCMC branch based on address.
• Geo-cluster customers to branches based on radius distance
using Nearest Neighbors model to create clusters.
• Profiling customer clusters based on socio-demographic
information.
• Profiling customer clusters based on customer transactions
with branches.
• Give recommendation on new branch opening.
Technology Stack:
Profile
Advanced Analytics

19. Sample
Reference
Architecture
Sharing
6

20. Digital
Connection
Use
Case

21. Thailand - Real time Architecture
{
name: "Midhuna",
age: 23,
place: "New York",
hobbies: ["Singing", "Reading Books"]
spouse: {
name: "Akash",
age: 25
}
}
Product Data Lake
Policy Transaction
Policy Contract
Accounting (GL
Entries)
REAL TIME
USE CASE

22. Consumption Pattern – Machine Learning with Jupyter & Zeppelin
Machine
Learning
Use
Case

23. Digital
Analytics
Use
Case

24. Thank you