The document addresses the significant data governance challenges faced by public sector agencies, including issues with data accessibility and trustworthiness. It suggests implementing a comprehensive enterprise data governance program and management platform to tackle these challenges and improve decision-making. The report includes insights on establishing data governance organizations, best practices, and the importance of treating data governance as a critical business function.

1. Is Your Agency Data
Challenged?
Kick start your data governance initiatives with DLT Solutions
July 2015

2. • Federated data organizations in public sector face more
challenges today than ever before. As discovered via
research performed by North Highland Consulting, these
are the top issues you are most likely experiencing:
– Knowing what data is available to support programs and other
business functions
– Data is more difficult to access
– Without insight into the lineage of data, it is risky to use as the
basis for critical decisions
– Analyzing data and extracting insights to influence outcomes is
difficult at best
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3. • The solution to solving these challenges lies in creating a
holistic enterprise data governance program and
enforcing the program with a full-featured enterprise
data management platform.
• Kreig Fields, Principle, Public Sector Data and Analytics,
from North Highland Consulting and Rob Karel, Vice
President, Product Strategy and Product Marketing,
MDM from Informatica will walk through a pragmatic,
“How To” approach, full of useful information on how
you can improve your agency’s data governance
initiatives.
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4. • Learn how to kick start your data governance initiatives
and how an enterprise data management platform can
help you:
– Innovate and expose hidden opportunities
– Break down data access barriers and ensure data is trusted
– Provide actionable information at the speed of business
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5. 5
Data Governance for Govt.
Kreig Fields
Principal, Public Sector Data & Analytics
North Highland

6. 6
Agenda
1. Why data governance for public sector?
2. Establishing a data governance organization
3. Building a data governance solution
4. Do’s and don’ts
5. Open questions

7. 7
Why data governance for public sector?
Data governance enables cohesive, consistent and reliable data across all of these
data perspectives
• Federated governance approach in public sector leads to fragmented data
perspectives
HQ
District1
District2
District3
District4
District5
District6
AcquisitionACQ ACQ ACQ ACQ ACQ ACQ
Proj…Proj Proj Proj Proj Proj Proj

8. 8
Common gaps in enterprise data

9. 9
Challenging but not impossible
• Federal Government has defined a governance approach for aligning data and
applications across the federal government
› Uses Federal Enterprise Architecture (FEA)
Link to Federal Enterprise Architecture information
› The DoD uses Department of Defense
Architecture Framework (DoDAF)
Link to DoDAF information
Proper data governance and tools are essential to managing these
issues!

10. 10
Data Governance
• Decision-making and oversight
• Uses a Committee to make decisions
and to provide strategic direction
Data Stewardship
• Formally making someone accountable
for data integrity
• Manages internal data subject area and it’s impact to the business
• Coordinates with colleagues and recommends operational changes needed to
improve data governance
Data Management
• Day to day execution of the Governance rules
• Responsible for the day to day data management activities
• Receives direction and guidance from data steward
Establishing a data governance organization

11. 11
Systems Model
Projects Model
Biz Function Model
Subject Area Model
Biz Process Model
Sample Data Governance/Stewardship Models

12. 12
Building a data governance solution

13. 13
Start by assessing where you are.
Data
Quality
Data
Integration
Data
Strategy and
Architecture
Master
Data
Management
Metadata
Management
Analytics
Security
and
Privacy
Dashboards
Scorecards
Reporting
Projects, interviews,
and surveys
• Do not let
perfection stand in
the way of
progress
Public Sector

14. 14
• Address data reliability and consistency issues
• Improve business visibility and decision-making
• Streamline time/effort required to share information
• Increase the organization’s understanding of the business in order to promote
transparency and efficiency
• Drive increased accuracy, timeliness and the precision with which teams can
discuss and collaborate on business issues and opportunities
• Communicate and coordinate activities such that rework and duplicative
calculations, analysis, and reporting are either eliminated or recognized as
necessary.
• Facilitate purposeful and coordinated ad hoc analysis
• Shift focus from data entry applications, to exploiting the value of information for
our business and customers.
Data Governance: Do’s

15. 15
• Introduce cumbersome and unwieldy processes
• Create redundant processes
• Focus on just the technology, but on how business can achieve the maximum
benefits of technology.
Data Governance: Don’ts

16. Thank You

17. Accelerating and Enabling a Sustainable
Data Governance Program in Government
Rob Karel, Informatica VP Product Strategy and
Marketing, Information Quality Solutions

18. Agencies Face Data Challenges
20
Valuable information exists,
but trapped in legacy
systems or not digitized
New sources and huge
volumes with up to 80%
unstructured
Long lifespan of data, due to
retention regulations, adds storage
stress…
…But short life of usable data due to
data degradation
No data map to classify types
and importance of data
Data governance, including data policies, needed
Data stewardship and master data
management non-existent
Proliferation of
duplicate and
uncleansed data

19. With Many Unanswered Questions
21
 What is the quality of existing and new data?
 How do we define quality anyway?
 Do we capture data appropriately?
 How much data must we store, and in what format?
 How long must we keep the data?
 Who owns the data?
 How do we secure and address privacy of personal data?
 Where’s this information coming from? Should I trust it?
 Who needs what data, and why?
 How do we balance real-time vs. right-time data delivery?
 Does data change frequently?
And that’s just the tip of the iceberg

20. Data Governance is a Business Function
Data governance should be
managed as a business function, no different
than Finance or Human Resources

21. Data Governance Maturity Stages
Fragmented Holistic
IT-drivenBusiness-driven
0: Unaware
• No activity
1: Initial
• Ad hoc
2: Repeatable
• Pilot
3: Defined
• Project
4: Managed
• Program
5: Optimized
• Function
Fragmented Holistic
IT-drivenBusiness-driven
IT
efficiency
and
compliance
Cost control,
business
efficiencies &
risk reduction
Greater
efficiency,
compliance
and support
mission-critical
objectives
Innovation,
automation,
economy of
scale and
agility

22. Data Governance Maturity Benchmarks (as of 7/15/2015)
Public sector maturity
below x-industry
average

23. Data Governance is not – and should Never have
been – About the Data…
…the vision must
be to improve the
business processes,
decisions and
interactions trusted,
secure data enables!

24. The Ten Facets of Data Governance
People Vision and Business Case Tools and
Architecture
Dependent
Processes
Measurement
Org
Alignment
Change
ManagementPolicies
Defined
Processes
Program
Management

25. Data Governance Roles and Responsibilities
Steering Committee
Business and IT Stewards
Data Governance
Leader/Driver
Executive Sponsor(s)
• Facilitation
• Communication
• Measurement
• Escalation
• Business case
Drive X-functional:
• Prioritization
• Resource allocation
• Approvals
• Broader funding
• Enforce collaboration
• Vision
• Evangelism
• Funding
• Remove barriers
• Analysis
• Definition
• Business/IT liaisons
• Education
• Ensure compliance
• Mitigation

26. Flavors of Data Governance Measurement
 Operational
monitoring
 Service Level
Agreements (SLAs)
 Program
effectiveness
 Business Value/ROI
Data Governance Leader,
LOB and Data Stewards
Executive Sponsors and
Steering Committee

27. Sample Key Performance Indicators
KPI Name KPI Type KPI Description
Level of DG program
influence
Program
effectiveness
# of lines of business, functional areas, system areas, project teams and other
parts of org that have committed stewardship resources or sponsorship
DG interactions Program
effectiveness
Capture all types of value-added internal interactions such as training, consulting
and project implementation support
Issue resolution Program
effectiveness
Categorize and track status of all issues that come in to the data governance
function
External validation Program
effectiveness
Industry awards, benchmarking against peers, thought leadership via speaking
tours
Data quality metrics Operational Monitoring of data accuracy, completeness, integrity, uniqueness, consistency,
standardization, and other baseline DQ metrics
Policy compliance Operational Audits ensuring compliance with privacy, security, retention and other regulatory
policies.
Recovery time SLA A contracted agreement with the business on how long before a data exception will be mitigated
Data latency SLA A contracted agreement with the business on how quickly a data update or insight will be
delivered to a dependent process or decision-maker
Compliance Biz value Reducing penalties by ensuring regulatory compliance; reducing enterprise risk
(e.g., contractual, legal, financial, brand)
Cost savings Biz value Lowering costs (e.g., business, labor, software, hardware)
Spend optimization Biz value Optimizing spending (e.g., procurement, supply chain, services, labor)
Efficiency improvements Biz value Improving operational efficiencies (e.g., employee, partner, contractor);.
Revenue growth Biz value Increasing top-line revenue growth;
Customer satisfaction Biz value Optimizing customer experience and satisfaction

28. Data Governance Process Stages
Discover
• Data discovery
• Data profiling
• Data inventories
• Process inventories
• CRUD analysis
• Capabilities assessment
Define
• Business glossary creation
• Data classifications
• Data relationships
• Reference data
• Business rules
• Data governance policies
• Other dependent policies
• Key Performance IndicatorsMeasure
and Monitor
• Proactive monitoring
• Operational dashboards
• Reactive operational DQ audits
• Dashboard monitoring/audits
• Data lineage analysis
• Program performance
• Business value/ROI
Apply
• Automated rules
• Manual rules
• End to end workflows
• Business/IT collaboration
Apply
Data
Governance
Apply
Measure
and
Monitor
Define
Discover
IT Business
Collaborate

29. Informatica Platform Built to Support Holistic
Data Governance
Apply
Data
Governance
Apply
Measure
and
Monitor
Define
Discover
IT Business
Discover Define
Measure
and Monitor
Apply
Collaborate
Business
Process
Management

30. Architectural Scope of Data Governance
Enterprise Data Warehouse
BI/Analytics
Performance Management
MobileShared
Capabilities
• Metadata/lineage
• Business glossary
• BPM/Workflow
• Connectivity
• Services
• Collaboration
• Monitoring
• Policy management
• Stewardship
• Discovery
• Security
• Canonical Model
Cloud
Social
Hadoop
Big Data
Enterprise
Integration
DQ, Profiling
CEP and
Business
Rules
MDM/
Reference
Data Mgmt
Business,
Data and
Process
Modeling
Information
Security/
ILM
Data
Virtualization
Legacy
Web
Enterprise apps
On premises (machine) Big Data
3rd party/Market data

31. Identify candidate business opportunities
1. What are the top business imperatives as
defined by your most senior leadership?
2. What organizational business processes,
decisions and stakeholder (e.g., citizen,
partner, employee) interactions are most
important in support of these top
imperatives?
3. What data and applications are used to
support those processes, decisions and
interactions?
Data
Scope thousands of “relevant” data items
to dozens or hundreds of “critical”

32. Use Discovery Processes to prioritize roadmap
4. What upstream people, systems, and
processes create, capture, and update that
data?
5. What is the business end user’s level of
confidence in the security and
trustworthiness of that data?
Repeat process and reassess priorities
ongoing (quarterly or bi-annually at minimum)
Data

33. 1
2
3
4
5
6
7
8
9
10
0.00
1.00
2.00
3.00
4.00
5.00
6.00
0.001.002.003.004.005.006.00
BusinessValue
High <--- Investment & Effort ---> Low
Business Opportunity Name
Consider Prioritize
ExperimentIgnore
# Business Opportunity Name
1 Reduce eDiscovery risk
2 Improve customer satisfaction scores
3
Implement shared services /COE for data
management
4 Improve financial reporting
5 Secure sensitive data
6 Optimize supply chain
7
Reduce costs and inefficiencies through
modernization of enterprise applications
8
Reduce waste, fraud & abuse
9
Reduce costs through data Center
Consolidation
10
Introduce new service channels e.g.
mobile
Consider Standardizing Process For Business
Opportunity Prioritization

34. Free Tools Available on GovernYourData.com

35. Contact Us
DLT Solutions | Infrastructure Performance Management
• Visit us on our website:
– http://www.dlt.com/brands/informatica
• Reach out to us via email:
– ipm@dlt.com
• Find us on social media:
7/20/2015 DLT Solutions LLC - Proprietary & Confidential 37

36. Learn More
• For more information, click below to download the full
on-demand webinar:
7/20/2015 DLT Solutions LLC - Proprietary & Confidential 38