Statewide
Data Architecture
and
Exchange
Problem
 Data
not standardized, consistent, or
aligned with business needs
 Citizens/Customers of state services
are impacted and inconvenienced
 We have: an IT project centric model
 We lack: understanding of the
magnitude of the problem or the quality
of the data
2
Goals
 Identify
principles, strategies & models to:
Standardize state data architecture
Reduce data management costs
Make state services convenient to use
Develop a state data governance
3
Research
 Personal
interviews
Melissa Cook – MA Cook Corporation
Ross Hunter –Representative, 48th District
Tony Tortorice – State Chief Information Officer
David Zager – State Enterprise Architect
4
Research (cont.)
 Federal
data standards
 Other state’s data architecture models
Michigan
California
Connecticut
Missouri
 WA
state agency documentation (ISB,
EAC, DOT)
 Informal interviews with data experts
5
Principles
 Keys
to achieving an effective data
architecture strategy
Master Data Management
Customer Data Integration
Data governance
6
Principles (cont.)
 Master
Data Management
Framework of processes & technologies
Authoritative, reliable, sustainable,
accurate, & secure
Single version of the truth
Data cleaned, rationalized, validated, and
integrated into a statewide “system of
record”
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Principles (cont.)
 Why
Master Data Management?
Consistent data management
Customer-centric business model
Single master data set
Complete picture
Reduces costs and errors
Higher trust value
8
Principles (cont.)
 Customer
Data Integration
Data consolidation technique
Various sources
Single source of truth
Customer data integrity
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Principles (cont.)
 Why
Customer Data Integration?
Creates value for the customer
Eliminates duplicative efforts
Customers own, update, & manage
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Principles (cont.)
 MDM
& CDI Implementation Challenges
Organizational and political obstacles
Fear others may use data inappropriately
Creation of a master data facility
Hoarding and proliferation of duplicate data
Data standards governed by other agencies
Determining the data source to make master
Semantic inconsistency in data attributes
Different business rules to transform data
Agreeing on who maintains the data
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Principles (cont.)
 Data
Governance
A quality control discipline for assessing,
managing, using, improving, monitoring,
maintaining, and protecting organizational
information
Supports…
Decision making
Assigning accountability
Identifying business rules
Defining processes
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Principles (cont.)
 Data
Governance (cont.)
Helps…
Meet regulatory compliance mandates
Increase revenue opportunities
Decrease expenditures
Build better citizen, customer, & partner
relationships
13
Principles (cont.)
 Governance
Challenges
Business objectives conflict or interfere
Resources are limited or are in competition
Political agendas
Territorial issues
Unrealistic expectations
Lack of organizational support & acceptance
Reluctance to evaluate & redesign current
business processes
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Approaches to Data Architecture
 Super
Hero
Single individual who ultimately fails
 All
or Nothing
 To achieve success…”boil the ocean”
 Top-Down
Most common, executive order, a state
priority, years before a return is recognized
 Iterative
Smaller data sets, measureable results
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Recommendations
 Iterative
Approach to Data Architecture
 Master Data Management (MDM)
 Customer Data Integration (CDI)
 Data Governance
16
Recommendations (cont.)
 Why
this strategy?
Authorizing environment
Economic conditions
Low state maturity level
Growth from existing roots
Principles and methods of others
Opportunities for external funding
Citizen-centric
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Structure of Proposed Data Governance Committee
Senior
Executive
Sponsor
Data
Governance
Committee
(DGC)
Appointed by the Governor
Data
Creator
Data
Steward
·
·
·
·
·
·
·
·
·
·
Members of the Committee
Student Achievement
Postsecondary Learning
Health
Vulnerable Children & Adults
Economic Vitality
Mobility
Public Safety
Natural Resources
Culture & Recreation
State Government
State of
Washington ISB
Data
User
Data Creator, Data Steward and Data
User represent state agencies.
Provides oversight of
the DGC
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Recommendations (cont.)
 Data
Standards Implementation &
Governance (cont.)
Committee & Experts expectations
“System of record” for a subject domain
Complete data set to grow business
Improve citizen-government interactions
10-year planning window
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Recommendations (cont.)
 Data
Standards Implementation &
Governance (cont.)
First steps already underway
Data standards: Commodity codes, Email
Voluntary efforts: ESD - DOL
Next Steps
Inventory of all statewide systems, baseline
3 to 4 initial data elements
Data dictionary
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Summary
 Know
your limits
 Learn from their successes or challenges
 Don’t reinvent the wheel
 It’s a long road…don’t rush it
 Understand the business problem
 Show value to the citizen
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Questions?

Will Saunders, DIS
 [email protected]

Susie LaPalm, DOP
 [email protected]

Randy Baker, WSDOT
 [email protected]

Rathnavel “Vel” Rajagopal, ESD
 [email protected]

Doug Beam, OFM
 [email protected]
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