A solid data management foundation to support big data analytics and more importantly a data-driven culture is necessary for today’s organizations. A mature Data Management Program can reduce operational costs and enable rapid business growth and development. Data Management program must evolve to monetize data assets, deliver breakthrough innovation and help drive business strategies in new markets.

1. Enterprise Data Management Framework
John Bao Vuu
Director of Data Management
www.enterpriseim.com
www.linkedin.com/in/johnvuu

2. Director of DM
Consultant | Advisor
John Vuu SPECIALTIES
✓ EIM Strategy & Solutions
✓ Data Governance / DQ
✓ Business Analytics
✓ Data Warehouse
INDUSTRIES
✓ Banking
✓ Insurance
✓ Ecommerce
✓ Healthcare
• 18 years experience in Data Management
• Founder of 2 technology companies
• Former Accenture BI Consultant
• DM Director at EIM Partners
• BA degree in Finance – Western WWU, Washington, USA
• BS degree Information Systems – WWU, Washington, USA
About the Speaker

3. B. Data Analytics
IoT, ML, AI
BI
Reporting
Business
Analytics
Cloud
Applications
DWH Cust. DB Dept. DM
Data-Driven
Analytics-Driven
DM Foundation
Data Management Maturity:
Underlying drivers of business
performance
DI Maturity Level
(Development Methodology)
Agile
Lean
Fit-for-Purpose
- - -
Data-driven decision making:
Data manageability, accuracy
and consistency Data Management systems supporting data-driven decision making across business functions
Aligning Business and IT; establishing cohesive & consistent DM practice across the organization
Leveraging data as an asset to explore new and innovative directions for FE Credit
Data Management Framework
A mature Data Management Program can reduce operational costs, improve project time-to-value and ultimately enable
rapid business growth and development. Data Management program must evolve to monetize data assets, deliver
breakthrough innovation and help drive business strategies in new markets.
MDMCIF
Data Lake /
B. Data Platform
DM
Strategy
Data
Governance
Data
Quality
Data
Architecture
Data
Operation
Operational/Strategic analytics:
Discover opportunities,
manage risk, reduce fraud..

4. DMM: Data Management Maturity Model
Data Mngmnt
Strategy
Data
Quality
Data
Operations
Platform &
Architecture
Data
Governance
ImplementationOversight:
Communication Coordination
OfficialData
Metadata
Oversight
Business IT
Alignment
Infrastructure
Oversight Data Profiling
SharedServices
Architecture
Data
Infrastructure
Business Process
Data Requirements
QualityRules
QualityCriteria
DATA MANAGEMENT
STRATEGY
DATA
GOVERNANCE
DATA
QUALITY
DATA
OPERATIONS
PLATFORM &
ARCHITECTURE
DM Strategy
Communications
DM Function
Business Case
Funding
Governance Management
Business Glossary
Metadata Management
DQ Strategy
Data Profiling
DQ Assessment
Data Cleansing
Data Requirements Definition
Data Lifecycle Management
Provider Management
Architecture Approach
Architecture Standards
DM Platform
Data Integration
Historical Data / Archiving

5. Data Integration Maturity Levels
Competency
Center
Program
Management
Enterprise Projects
Ad hoc, Functional Development
Integration Platform
(Efficiency, Performance)
Data Quality Tool
(Improved Data Quality)
Data Integration Tool
(Standardization)
Hard Coding
(Quick & Dirty)
Data Integration Maturity Level
4 STAGES of Data Integration Maturity
1. Project: disciplines that optimize integration solutions
around time and scope boundaries related to a single
initiative
2. Program: disciplines that optimize integration of specific
cross-functional business collaborations, usually through
a related collection of projects
3. Sustaining / Governance: disciplines that optimize
information access and controls at the enterprise level
and view integration as an ongoing activity independent
of projects
4. Lean: disciplines that optimize the entire information
delivery value chain through continuous improvement
driven by all participants in the process
Most organizations are project-focused as opposed to being program-focused – attention around data integration and supporting
standards are typically are not well governed. Additionally, there is a lack of standardization and enforcement of DI / DQ tools and
practices. Many DI driven projects are still applying hard coding data transformations without a standard ETL tool, this practice often
leads to cascading maintenance & scalability issues for the organization.

6. DM Program Management & Oversight
Data
Quality
MDM
Data
Analytics
Data
Governance
Management
&Oversight
Oversight, Governance, Support, Management,
Initiatives
&Projects
Business
Operations
Decision
Support
Business
Analytics
Fintech
Business
Units
Stakeholders, Sponsors, Project Managers, End-Users
SALES FINANCE BD RISK MRKTNG
Program
Operationalization
DM Roadmap
Future Vision
• Business / IT
Drivers
• Current / Future
State
• DM Maturity
Assessment
• Prioritization /
Selection
• Timeline /
Milestones
• KIP / Metrics /
Dashboard
• Oversight /
Management
• Process
Framework
• Toolsets / Best
Practices
• KPI / Metrics /
Dashboard
• Agile / Lean
Methodologies
• Policies /
Standards

7. DM Program Management Activities
✓ Establish / Improve key DM
Programs: Data Governance,
Data Quality, Data Analytics,
MDM
✓ Program Oversight, Governance
and Support
✓ Establishment of DM Processes
& Best Practices as Enterprise
Standards
✓ Introduce underlying toolsets
for Improved Data
Management
✓ Program Intermediary Support
for Business and IT
✓ Enterprise Data Management
Domain Expertise
Program Management
✓ DM SWOT Assessment:
Strengths, Weaknesses,
Opportunities, Threats
✓ Define Current & Future State
Strategy: Establish Fit-for-
Purpose Roadmap
✓ Data Management Maturity
Measurement & Action Plan
✓ Establishment of Frameworks
for; Data Integration, Data
Quality, Data Governance,
MDM
✓ Assessment of current data
assets and associated
opportunities
✓ Establish KPIs / Metrics for
improvement
DM Strategy
✓ Operationalize Agile, Lean, Fit-
for-Purpose Methodologies to
DM Initiatives
✓ Establish Common Data Policies
and Standards
✓ Operationalization of Data
Governance Framework
✓ Operationalization of Data
Quality Framework & Data
Remediation Strategy
✓ Establishment of Metadata
Management Strategy:
Business Rules, Data Flow, Data
Lineage, etc.
✓ DQ Assessment and Dashboard:
DQ Accuracy Measurement
DM Operating Model
✓ Establishment of Common Data
Integration Architecture Across
Projects
✓ Data Acquisition / Integration
Process Framework
✓ Establishment of Data Storage /
Data Distribution Policies
✓ Establishment of Data Access &
Presentation Policies
✓ Establishment of Metadata
Management & End-user
Access
✓ Enterprise Data Modeling
Standards / Conventions:
Business Glossary, Data
Dictionary, etc.
Data Architecture

8. BUSINESS : www.enterpriseim.com
LINKEDIN : www.linkedin.com/in/johnvuu
BLOG : www.johnvuu.com
MOBILE: +84 090.264.0230
Thank You!