This is the slide deck presented at the Customer Privacy and Data Protection India Summit 2019 held in Mumbai, India. The specific topics touched upon are the guiding principles, Aligning with Data Architecture, Data Quality & Compliance.

1. Developing & Deploying an Effective
Enterprise-wide Data Governance Framework
CUSTOMER PRIVACY &
DATA PROTECTION INDIA SUMMIT 2019
1
Kannan Subbiah
Sr. VP & Chief Technology Officer
MF Utilities India Pvt Ltd

2. Topics
discussed
2
Data Governance
Framework -
Overview
Aligning data
governance with
enterprise data
architecture
Implementing
robust data quality
controls
Driving data
compliance across
the organization

3. The Why,
What, How
3
Data everywhere
it is excessively expensive to
manage, and
you cannot find it, make sense
of it, or agree on its meaning.
It is Not Data
Management
DG organization is not there to
do information management.
It is there to guide and
monitor.
The Mark of Success
the organization treating its
information as it treats its
factories, supply chains,
vendors, and customers
One clear goal is - to
disappear.
to fade into the business fabric
It becomes part of the fabric
of business, like financial
controls.

4. 4
CorporateDrivers Customer
Focus
Mergers &
Acquisitions
At-Risk
Projects
Decision
Making
Operational
Efficiencies
Regulatory
Compliance
Data Governance
Organization Principles Policies Functions Metrics
Functions Technology & ToolsOrganization
Data Management
Data Security
Data Quality
Data Warehouse
Business Intelligence
Steering Committee
Governing Council
Data Stewards
Data Architects
Data Monitoring
Data Collection
Data Integration
MDM Tools
BI & Analytics
Data Protection
Enterprise Data Architecture

5. People,
Process &
Technology
5
• Data Owners, Stewards
• Data Creators & Consumers
• Data Flow, Controls
People
• Standards
• Data Quality, Metrics
• Controls
Processes &
Policies
• Meta Data / Data Quality
• Reference, Master Data
• Classification, Control
Tools &
Technology
• Scope & Mandate
• Centralized / Federated
• Funding / Budgeting
Execution
Models

6. ALIGNING DATA GOVERNANCE WITH ENTERPRISE DATA
ARCHITECTURE
6

7. Goals of EDA
7
Understand
Current State
Reduce
Redundancy &
Fragmentation
Eliminate
Inefficiencies
in Data Flow
Integrated view
of Data
Optimize
Technologies
involved
Improve Data
Quality
Improve Data
Security

8. 8
The Synergies
Data Architecture Data Governance
Critical Data
Defines Data
Helps building Business & IT Consensus
Identifies Data & business impact
Establishes Accountability
Data ImprovementIdentify areas of improvement Prioritize implementation
Data StrategyMaps out Blueprint of Data Flow Links to Business Goals
Architecture DeliverablesAids Governance on Focus, Priorities … Validates & help evolve
OthersAids Communication across functions Helps build a business case
Acts as a Business Sponsor

9. 9
The Relationship
Data Architecture Data Governance
Strategies, Standards
and Architectures
Monitor Strategies and
Standards
Design the
Architecture
Common Data
Requirements
Better Quality Data
Resolve Data Issues

10. Common
Alignment
issues
10
Failure to align
with other
governance
initiatives
Random point
fixes - creates
silos
Lack of cross-
functional
standards

11. ROBUST DATA QUALITY CONTROLS
11

12. Data Quality – Why?
12
Misguided
business
decisions
Legal and
monetary
penalties
Financial
inaccuracies
and mistakes
Negative
company
image
Missed
opportunities
Loss of
customers

13. Data Quality
Vs Data
Control
13
Data Quality
Data quality is an Outcome
Measure and Fix Data Quality
Issues
Data Control
Data control is about ensuring
Quality
Validate at source and prevent

14. Sources of Bad Data
14
Different types of
systems in use
Transfer of data
between different
(often incompatible)
systems
Accidental/intention
al removal of data
Improper data
governance
Lack of responsibility
and authority for
managing data
Lack of awareness
of value of
information
Lack of integration
between IT and
business processes
Lack of training and
motivation

15. Dimensions
of Data
Quality
15
Intrinsic
Accuracy
Provenance
Semantics
Structure
Contextual
Timeliness
Currency
Completeness
Consistency
Aggregation
Transformation
Conformance
Fitness
Compliance

16. Data Lifecycle - Controls
16
Creation
• Input
Validation
• Meta Data
conformance
• Referential
integrity
Ingestion
• Contextual
Quality
• Integrity
Checks
Storage
• Data
Changes
• De-
duplication
Consumption
• Access
Controls
Disposition
• Integrity
Check
Audit

17. Data Quality –
Continuous
Process
18
Define Thresholds
Different Thresholds
Data Quality Rules
Assess Quality
Data Profiling – Run the
Rules
Resolve
Manage as Defects
Track it to closure
Monitor & Control
Continuous Process
Responsibility with Data
Owners
Revise
Revise Thresholds –
Changing Needs

18. Data
Quality
Dashboard
19

19. Data Quality – Focus Areas
20
The Team
Fix Ownership
and
responsibility
Encourage
collaboration
across data
domains
Tools - Data
Profiling
Use Meta Data
Use Statistical
Tools / Models
Rules &
Metrics
Quality Rules
aligned to
Strategy & Goal
Define data
quality measures
and thresholds
Reporting
Data Quality
Dashboard
Exception
Reports
Issue
Tracking
Track & Resolve
as Defects
Address Root
Cause

20. DRIVING DATA COMPLIANCE ACROSS THE ORGANIZATION
21

21. A Business
Opportunity
22
Improved Data
Quality
More accurate
analysis
Facilitated /
Federated Data
Services
Process
Efficiency
Enhanced
Consumer Trust

22. People,
Process &
Technology
23
People
Role, Title,
Responsibility
Positive Team
Dynamics
Culture
Process
Effective Workflows
Effective Control
Review and
Monitoring
Technology
Enterprise wide
Integration
Contribute towards
Data Quality

23. Get it Right
24
Must be Forward
Looking – Align
with Strategy
Avoid Boiling the
Ocean
Designed and
Managed by
Business – Not IT
Buy-in from Senior
Management
Keep it Simple,
Implementable
Value Delivery

24. Status quo –
Not an option
25
Regulators expect
More
Internal pressure –
Do more with less
Cultural pressure –
hard and fast rules
Risk fatigue Technology – double
edged sword

25. Thank You
26
Follow me on Twitter: @kannagoldsun
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-- Peter Drucker