A Data Governance Wake-Up Call - DAMA NY · Enable accountability Overcome silos Prove value...
Transcript of A Data Governance Wake-Up Call - DAMA NY · Enable accountability Overcome silos Prove value...
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A Data Governance Wake-Up CallC O M P L I A N C E Y E S , B U T S O M U C H M O R E
D G I Q J U N E 2 0 1 8
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Data is the “New Oil”
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Improved
Decision-Making
Increased
Customer
Satisfaction
Compliance,
Privacy & Security
Operational
Efficiency
Revenue
Growth
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It Requires an Enterprise Data Governance Experience
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Regulatory
Peace Of MindVisibility
Across Domains
Integrated
Ecosystem
Any Data
Anywhere
Collaboration
& Organizational
Empowerment
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Data Governance: The Driving Principle
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UNDERSTANDDISCOVER
SOCIALIZE
Data
Sets
Data
Issues
Impact &
Lineage
GOVERN
Policies and
Rules
DGOM
Collaboration
Data
Dictionary
Data
Quality
Data
Usage
Reference
Data
Business
Glossary
D ATA V I S I B I L I T Y, C O N T R O L & C O L L A B O R AT I O N U N L O C K B U S I N E S S VA L U E
What do we have?What does it mean?
Where did it come from?
Is it secure?What rules or
restrictions apply?
How accurate is it?
Who is accountable?
Who is using it?
How is it used?How can
I access it?Where is it?
DATA IN CONTEXT
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The Facebook
“Data Trauma”
Syndrome
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And it drives
Verizon’s
purchase
price down
$1 billion
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The New Data Trinity
What data do we have and where is it?
What private data do we store and how is it used?
Who has access and permissions to the data?
Privacy
Security
Governance
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Organizations Are Placing Big Bets on Self-Service Analytics, AI and Machine Learning – “Danger Will Robinson”
These disciplines rely on enterprise data to enable the desired outcomes.
All are susceptible to providing bad results if the inputs are bad to begin with.
Organizations need to mitigate the risk that results from:
✓ Poor quality data
✓ Mis-understood data
✓ Incomplete data
✓ Misused data
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New Data Governance: Greasing the Wheels!!!Risk Avoidance and Opportunity Enablement
Offense
• Enable Data Driven Agility
• Optimize Data Driven Insights
• Assure Data Driven Transformation
Defense
• Enable Data Compliance
• Optimize Data Operations
• Assure Data Security
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“Analytics is changing how
organizations make decisions
and take actions. Data by itself
has limited value but when
managed as a strategic asset,
data can change how
organizations compete and win.”
Chris Mazzei, Global Chief
Analytics Officer, EY
V I S I B I L I T Y A N D C O N T R O L U N L O C K S
B U S I N E S S VA L U E O F D ATA
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Key Findings
DG ISN’T JUST FOR
COMPLIANCE ANYMORE
60% say regulatory compliance is the biggest
driver, but it’s not the only one. 49% see it as a
way to improve customer satisfaction, and
45% see it supporting better decision-making.
Reputation management (30%), analytics
(27%) and Big Data (22%) are also key
drivers.
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Key Findings
ROADBLOCKS
58% of respondents report the biggest
obstacle is the cost of data governance,
followed by understanding the right
approach (42%), executive support
(42%); organizational support (39%),
effective tools (36%) and articulating
business justification (27%).
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It’s Here!!
Are You Ready
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Compliance isn’t just about GDPR
Sarbanes-Oxley
Dodd-Frank
HIPPA
Basel I, II, III….
Office of Foreign Assets Control (OFAC)
….
In many cases, different regulations mandate conflicting requirements!!
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This Photo by Unknown Author is licensed under CC BY-NC-ND
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APPLICATIONS
&
INFRASTRUCTURE
SECURITY
METRICS
GOALS &
STRATEGIES
CAPABILITIES
&
PROCESSES
GROUPS
& ROLES
Framework for Data Context, Control & Collaboration
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Assess the current state
Plan the way forward
Balance control & agility
Enable accountability
Overcome silos
Prove value
Iterate success
Measure Progress
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The DG Team Needs to Expand beyond Owners, Stewards, Custodians
Enterprise Architects Visibility into enterprise goals &
strategy as well as the IT landscape
and roadmap
Data Architects Visibility into the data resources
available, where they reside and
the key use cases
Process Architects Visibility into the business
operations, organizational
structure and mechanisms
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Data Modeling
Enterprise Data Governance
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The Role of EA Modeling With Data Governance
• Goals
• Motivations
• Metrics
Align DG with business plans and strategy
• Desired outcomes
• Related tasks / Roadmap / Project Management
• Stakeholders, organizations and communities
Architect DG into the enterprise
• Applications
• Infrastructure
• Business Capabilities
Extend the visibility of your DG program
beyond the data sources
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Architecting data governance into the enterprise
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The Role of BP Modeling With Data Governance
• Business processes
• Roles
• Value Chains
Align DG with details of the
business operations
• Issues Management
• Detail process flows
• Roles & Responsibilities
Define DG processes and
operating model
• Landscapes
• Processes
• Organizations
Extend the visibility of your DG program
beyond the data sources
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Mapping out the business for data governance
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The Role of Data Modeling With Data Governance
• Requirements
• Reusable Standards
• Lifecycle
Govern Data Definition and Deployment
• Business Terms
• Data Elements
• Business Rules
Accelerate The Harvesting of DG
Bodies of Knowledge
• Use Cases
• Designs/Schema
• Context
Extend the visibility of your DG program
beyond the data sources
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The fabric that stitches data governance together
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Identification you want to identify those assets that are critical for
customers to anonymize
Analyze you want to see the impact of identified assets on
processes, systems, locations and technology
Classificationyou want to classify data assets with personable identifiable
information (PII) and show processes and applications
Adherence you want ensure that the data can be removed and put
remediation plans in place for compliance
Reporting you want to create supporting documentation of
compliance and management of GDPR
Discover you want to find out where the data assets are in the
organization
Harvest you want to understand the data assets as models or
structured lists
Governyou want to have a clear understanding of your requirements
for GDPR, resources in place to deliver them and for the
organization to understand their roles in GDPR
High level GDPR operating framework
} Conduct an
audit
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Setting up the GDPR operating frameworkA. Identify
business value
1. Identify value drivers
2. Show compliance drivers
3. Define KPIs
B. Setup object types
1. Define overall meta-model for all object types and
relationships
2. Modify Business and IT object types
in EA agile / DG
3. Create views
4. Setup scorecards for
metrics
5. Define operational checklist
C. Define stakeholders
1. Create communities
2. Assign users and roles
3. Assign users to tools
D. Create workflows
1. Define work packages
2. Create status attributes
3. Create work item (task) owners
(stewards)
4. Set up Kanban boards
E. Setup work products
1. Identify outputs for different
stakeholders
2. Identify charts for decision making
3. Verify the best communication
medium for the org
4. Show how to capture GDPR
remediation and adherence processes
F. Setup dashboards |
reports
1. operational progress
2. health of model
3. lawful basis
G. Document operating
model
1. Document operating
procedures
2. Document meta-model, object and
attribute usage
3. Explain Scorecards
4. Map work products to GDPR compliance criteria
H. Educate stakeholders
1. Roles and responsibilities
2. Core views
3. Tool training
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Data Governance Is Evolving: The New Normal
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What Data Do We Have?
How Does It Serve the Business? Who, What, Why?
How Is Data Linked to Our Go-Forward Strategy?
What Should We Do First?
How Do We Deploy for Success?
What Does Success Look Like
How Do We Make It Sustainable
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Q & A