Briefing: Statewide Data Architecture and Exchange

2010 LeaderPath for IT Managers

STATE OF WASHINGTON

Version 1 -- Final

Problem Statement2

Goals2

Research2

Principles3

Master Data Management (MDM)3

Customer Data Integration (CDI)4

Data Governance4

Rejected Approaches5

Recommendations6

Assumptions7

Unknowns7

Methods and Techniques7

Process(s)8

Lessons Learned8

Results8

Appendices10

Case Study: Integration Competency Center (ICC)11

Case Study: DOT Data Governance Council13

Case Study: Identity Management16

Analysis Exercise: Balanced Score Card for Statewide Data Sharing initiative17

Roadmap to achieving a Statewide Data Architecture and Exchange18

References19

Problem Statement

The data architecture [in] state government is not standardized, consistent, or aligned with business needs. The state spends a lot of effort in managing data, but pays a premium for this due to the fact that data formats, definitions, and rules for usage are different between data exchange partners.

The customers of state services are impacted and inconvenienced when the state maintains several different methods to identify them, determine who they are and what their business, social and economic needs are.

The state's IT project centric model does not provide a vehicle for creating state architecture.

What strategies should the state adopt to create and utilize data architecture policies, principles, models, and standards for business decision making [?]

Goals

Identify principles, strategies, and models to:

1. Standardize state data architecture in consistent ways to align with state business needs.

2. Reduce data management costs through standardization of data formats, definitions, and rules between state agencies.

3. Make state services convenient to use through common methods to validate access and determine needs

4. Develop a data governance function within state government.

Research

The research this team performed and the personal interviews conducted, provided no indication of a fully implemented public sector data architecture model.

The LeaderPath Team conducted personal interviews with the following stakeholders:

Melisa Cook is an industry thought-leader in enterprise architecture and information technology governance and decision-making frameworks for large, complex, and distributed organizations. Melisa spent 20 years with the Hewlett-Packard Company in systems engineering and management in the formative years of the distributed computing industry after graduating with a Computer Science Degree in 1979 from the University of Washington. She has senior level management experience developing successful enterprise architecture and other executive-level decision-making frameworks used in the Board Rooms at Fortune 500 corporations. She is familiar with a broad variety of governance concepts from Zachman, TOGAF, FEAP, NASCIO and from strategic technology suppliers including HP, IBM, Microsoft, SAP and Oracle. She regularly writes opinions for ComputerWorld and is the author of the book "Building Enterprise Information Architectures" published by Prentice-Hall. Melissa's publications and writings are referenced around the world in over 50 languages.

Melisa Cook suggests the best piece of advice is to put an enterprise data standards process in place and set some data standards. Nobody has been able to do this very well.

State Representative Ross Hunter is serving his 4th term in the Washington State House of Representatives, representing the 48th district parts of Bellevue, Redmond, Kirkland, Issaquah, and all of the Points Communities. He is chairman of the House Finance committee, a member of the House Ways & Means committee and the Appropriations committee on Education Financing. He is a past chair of the Joint Legislative Audit and Review Committee, responsible for performance auditing of state agencies. Rep. Hunter currently serves on the state Information Services Board (ISB). Prior to serving in the legislature Rep. Hunter spent 17 years at a small eastside software company (Microsoft), rising to become a general manager. He has a BS in computer science from Yale University.

Representative Hunter would like to see that investment costs for data architecture are readily available for decision making to ensure efficient use of state dollars.

State Chief Information Officer Tony Tortorice has been serving as the states Chief Information Officer since July 2009. He was the CIO of the Los Angeles Unified School District, the nation's second-largest school district, was an IT executive for the Los Angeles Community College District, the largest system of community colleges in the U.S., and was a partner at PricewaterhouseCoopers.

CIO Tortorice would like the ability to see statewide information technology investments leverage enterprise solutions.

State Enterprise Architect David Zager joined the Department of Information Services in February 2010 and is staff to the Enterprise Architecture Committee of the ISB. For 15 years, he designed, coded, and implemented messaging middleware applications that enabled information sharing across institutional and technological boundaries. He was the Sr. Director, Head of Business Process Reengineering at AT&T Wireless, Director of Emerging Technologies & Enterprise Architect at Merrill Lynch, Vice President & Chief Scientist at Avesta Technologies, Vice President, Infrastructure Engineering at Morgan Stanley & Co., and Assistant Professor at SUNY at Stony Brook.

EA Zager states the biggest disconnect is to clearly identify business problems before developing solutions.

Additionally, the LeaderPath team explored federal data standards and data architecture models from other state governments such as Michigan, California, Connecticut, and Missouri. Reviewed Washington state agency documentation and conducted informal interviews with data experts in Washington state government. For a more detailed listing for key reference documents, see the references section.

Principles

Our analysis suggests that the principles of master data management, customer data integration, and data governance are key to achieving an effective strategy for data architecture.

Master Data Management (MDM)

MDM is a framework of processes and technologies aimed at creating and maintaining an authoritative, reliable, sustainable, accurate, and secure data environment that represents a single version of truth, accepted systems of record used both intra- and inter agencies across a diverse set of application systems, lines of business, and user communities.

Master data for the state can be defined as the set of data that has been cleaned, rationalized, validated, and integrated into a statewide system of record for core business activities (note the term core). For example, employers and the citizens data domains could be core, while services rendered by each agency could be feeding off the core data domains. Services cannot be a part of the master data domain since it is agency specific.

Why do we need MDM?

1. To manage certain data domains in a consistent integrated fashion

2. Move from an account-centric business model to more of a customer-centric business model

3. A single master data set allows the state to reduce costs by sun-setting and discontinuing old application systems that create and use various local versions of the data

4. Provides a complete picture of the customer allowing an agency to provide personalized services

5. Reduces cost and errors, resulting in a higher trust value in transactions with data assets

Customer Data Integration (CDI)

CDI is a technique by which data pertaining to a customer is consolidated from various sources resulting in a single source of truth and enabling retention of customer data integrity across the board.

Why do we need CDI?

1. Creates value for the customer

2. Eliminates duplication of effort

3. Allows customers to own, update, and manage their data.

Challenges to Implementing Statewide Data Architecture

1. Organizational and political obstacles to cleaning existing data stores (e.g., an individual business units desire to hold on to its version of data because it is deemed unique to the business units goals)

2. Fear that other agencies may use and maintain the data inappropriately to change its meaning

3. Technical challenges with the creation of a master data facility

4. Hoarding and proliferation of duplicate data driven by siloed organizational structures

5. Common data standards governed explicitly by other organizations

6. Determining which data source will be used as the master data.

7. Semantic inconsistency in data attributes across agencies, caused by diversity of roles of those conducting business with or receiving services from the state

8. Different business rules to transform data across agencies

9. Agreement on who maintains the data.

Data Governance

Data governance is fundamentally a quality control discipline for assessing, managing, using, improving, monitoring, maintaining, and protecting organizational information. It supports making decisions, assigning accountability, identifying business rules and defining processes related to information itself. These practices establish repeatable, measurable business processes and manageable policies for improving data quality; they are essential for managing data as an asset. Data governance helps meet regulatory compliance mandates while increasing revenue opportunities, decreasing expenditures, and building better citizen, customer, and partner relationships through a higher quality of information.

What are the challenges with data governance when there are clear, measureable, and attainable benefits?

Even though data governance is a worthwhile practice, the Legislature as well as other state agencies may believe that initiatives providing a faster return on investment take precedence over a long-term investment. Organizations may require external assistance to help determine the best ways to achieve success and access the value of data for their operations and performance. In some cases, there may be legislative motivators; even with these motivators, data governance is, at best, inconsistent. However, legislative motivators have created a tipping point requiring organizations to protect certain types of data. In addition to these considerations, the following generic challenges have affected the majority of data architecture initiatives identified by the team.

1. Business objectives conflict or interfere

2. Resources are limited or are in competition

3. Political agendas

4. Territorial issues

5. Risk of innovation

6. Unrealistic expectations

7. Lack of organizational support and acceptance

8. Reluctance to evaluate and redesign current business processes

Organizations have attempted to implement data governance with a variety of approaches. An effective data governance program must include the people, processes, and policies necessary to create a single, consistent view of an enterprises data.

Rejected Approaches

The team examined but rejected several data architecture approaches, including:

Super Hero Approach

Some organizations have tried assigning the program to a single individual who ultimately fails because the job is too big and broad for one person. Still other organizations have established a coalition of interested parties, which has resulted in limited success because this group typically does not have the budget authority needed for a proper data governance program, nor the influence to shift priorities.

All or Nothing Approach

Some organizations believe that to achieve data architecture success, they must take a boil the ocean approach in attempting to resolve all data governance issues in single effort, which often becomes overwhelming and requires a time investment of several years, with costs escalating before any true return is realized.

Top-Down Approach

The most common approach to statewide data architecture and exchange in other states begins with developing a vision at the statewide level. The support for this effort must come from the very highest levels (Governor, Legislature) and be supported by incorporating it into the priorities of government. Many states have attempted to move in this direction, and without support at the very highest level, the efforts are not successful. Data architecture and exchange must be a priority for the state in order to make sustainable forward movement in this area. States that are seeing positive impacts in this area are driven by a directive or executive order to put the cultural shift into action. The approach takes significant time and cannot be accomplished overnight. Many of the states showing progress have been working on this effort for several years. This approach requires the coordinated efforts of information technology, business management, finance, and other functional units.

Examples of states that have moved in this direction and have years of experience under their belt that can be used as resources are California, Missouri, Connecticut, and Michigan. All have used a phased in approach and their efforts started at the top.

While most experts acknowledge the importance and need for enterprise data architecture in state government support from the Governor and Legislators, capacity to undertake this effort at a statewide level, and funding to pay for a major data architecture initiative are essential to be successful.

Recommendations

The recommended strategy for Washington state government is to take an iterative approach to data architecture with the objective of achieving data sharing using the principles of Master Data Management (MDM) and Customer Data Integration (CDI).

The decisive factors contributing to this recommendation arise in the following areas:

1. Authorizing environment, which is likely to be changed substantially and permanently by the shared services initiative currently under way

2. Economic conditions, which are historically poor for the public sector and may well not improve for 2-3 years

3. Maturity level of the state in this area, which we assess as low, despite previous steps toward improved data management and business planning

4. Growth from existing roots, which we feel will occur at a limited level even without a major initiative

5. Adopting and promoting principles and methods from private industry and the federal government

6. Seeking opportunities for external seed funding where and when possible

7. Staying centered on the citizen to enhance the sustainability of funding and to show visible results

In view of apparent limitations on our capacity and maturity in state government, we should first leverage the efforts already in progress, bring them to completion, and demonstrate results before undertaking a more global approach.

We conclude that an iterative approach of achievable scope augmented by external opportunities and promotion or recognition within the organization is all that the state can reasonable accomplish in the short to medium term. In other states, these efforts are the result of years of forward progress combined with governance and significant investment, both financial and political. External opportunities could include existing federal standards (such a personnel standards, recovery act reporting); grant opportunities, agency collaborative relationships and unusual financial pressures.

The state should choose a smaller data set, and focus efforts on understanding and improving it. Next determine what worked and apply lessons learned in addressing another data set. We believe that this will enable organizations to demonstrate immediate, measureable results within a shorter period, while growing the infrastructure to help manage data quality in the future. An iterative approach also allows organizations to monitor revenues, expenditures, and relationships while making mid-course corrections and incremental improvements that will ultimately have a favorable outcome.

Data Standards Implementation and Governance

A cross-departmental data governance committee (see appendix) is recommended to align business priorities, people, processes, and technology in order to set policies and procedures for a shared data strategy administration. Without a governing body in place, statewide data sharing will be difficult to sustain. This committee would decide the parameters for how data is consolidated, who owns the data, update rules for common data, and provide specifications for auditing requirements. In addition, the committee would be responsible for deciding the best way to resolve the data conflicts that are bound to occur with more complex data management rules.

The members of this committee should be experts in the states business and represent the ten Priorities of Government:

1. Student Achievement

2. Postsecondary Learning

3. Health

4. Vulnerable Children & Adults

5. Economic Vitality

6. Mobility

7. Public Safety

8. Natural Resources

9. Culture & Recreation

10. State Government

For an iterative approach to be successful there must be a strong team in place with all the right roles. The roots of a sustainable iterative approach to data architecture could begin with appointment of a senior executive to sponsor the initiative and to chair the data governance committee. The senior executive would report directly to the Governor and must have the ability to commit some budget, alter priorities and strategies, and eliminate alleviate issues. The sponsor and data governance committee would then establish and draw upon a group of subject matter experts and data owners in a data governance committee.

The data governance committee and its associated subject matter experts and owners should be charged with the following over a 10-year period:

1. Creating and maintaining an accurate and timely authoritative system of record for a given subject domain

2. Creating an accurate, timely, and complete set of data needed to manage and grow the business

3. Improve the efficiency of citizen-government interactions

Initial steps toward these goals should include:

1. Complete an inventory of all state information systems to establish a baseline and understanding of the states data assets

2. Identify three or four common data elements within those information systems (name, address block, phone number, demographics)

3. Develop a data dictionary to document the data elements, structures, flows, stores, processes, and external entities

In addition to the work listed above, the data governance committee and its associated subject matter experts and owners should evaluate and build upon the work the Enterprise Architecture Committee has already completed relevant to data architecture (e.g. the data standards initiative).

Assumptions

Our strategy assumes the following:

1. Washingtons economic situation is not likely to improve within the next biennium.

2. State governments historic challenges with regard to citizen trust and service will remain a theme during the strategic planning window envisioned by this effort.

3. IT Executives and Agency Directors support the principles underpinning this recommended strategy.

Unknowns

1. Availability of funding to support discreet or comprehensive data architecture efforts such as citizen identity management.

2. The true nature and extent of business problems connected with shortcomings in our data architecture efforts to date (we dont know what we dont know).

Methods and Techniques

1. Balanced Score Card (BSC)

2. Interviews of key state personnel

3. Organizational Chart for Data Council

4. Flow Chart of the data standards process

5. Research of relevant materials

6. Brainstorming for ideas on how to get data standards implemented

7. Analysis of the research gathered

8. Strategy sessions and discussions

9. Recommendations based on debate of the research

Process(s)

The team used research and interviews to form opinions and a working hypothesis about data standards. The more research that we did the larger the problem became. We called this the analysis stage and it was the most challenging part of the exercise. As a team, we were challenged to form and debate comprehensive opinions until the last month of our project. This was the point at which our recommendations and strategy started to take shape and actually began to make sense. An important driving force behind this transition was the impending end of the course. Having formed, debated and synthesized our recommendation we proceeded to capture our ideas and create a record of our research for future reference. Finally we consulted with other groups, prepared and our presentation.

Lessons Learned

1. Know your limits

a. We started out wanting to undertake an analysis of key data assets and fields and draft a recommendation on which fields to standardize and how. Working with a simple example citizen addresses we quickly came to appreciate the gravity of several key ambiguities and complexities. For example, data classification by different groups, even within the same agency, are incredibly challenging to achieve with any kind of consistency so that the data is reliable and can be used in a statewide environment.

b. Data standardization in government or private industry is complex with many players involved. The reality of politics and rapid change in leadership within government hierarchies limits the amount of time and resources that are available to be committed for the duration of a large project. Any statewide large scale enterprise wide project is certainly doomed to failure because of the economy, a lack of long term resources and requires a time commitment of eight to ten years to complete the project.

c. The success of a data standards project is directly linked to its size and scope. It will take many iterative projects over a period of 10 years to complete the change. The credibility of the standards and any momentum be gained by an iterative approach is fragile and must be sustained through each connecting project.

2. Commit to identifying and understanding the business problems before coming up with solutions.

3. IT Initiatives must show value to the citizen in order to command legislative and executive support (Marshburn and Hunter)

4. Dont reinvent the wheel, look at, and learn from what other states and entities have done.

5. Its a long road that needs to be planned out, dont rush your initiative.

6. Most states showing forward movement have used a top-down approach to their statewide initiative.

7. Incorporate work already done to date. Refine those efforts and use them to build the foundation for a sustainable state wide approach.

Results

What was achieved?

Data standardization is possible but only in smaller iterative projects. Long term planning for standardization will be dependent upon focused political interest and long term legislative and governor driven initiatives. However, iterative projects can show the way for future large enterprise data standardization projects. An all-or-nothing strategy will still not be successful. Medium scale projects for key data stores that are common between multiple agencies will be more successful. They will be cost effective and meet citizen expectations for consolidating data sources.

What was its value with respect to the problem statement?

Although this problem was very large and extremely complicated, and there have been many failed attempts at resolving it, there are still opportunities for reaching our goals if we approach it with the right attitude and energy. Scaled projects can be successful if managed correctly. At first the project seemed so large that we thought there was really no way to overcome it. But after we got moving and gathered a large amount of research we began to realize there might be a path that could work if we took it in smaller portions. People do want standardization for data and there are agencies who have adopted standards internally. Employment Security Department is adopting a data standard from another agency. It is possible, with the right effort, to gain support and push forward with this initiative.

Appendices

Case Study: Integration Competency Center (ICC)

The purpose of an ICC is first and foremost to support and facilitate Service Oriented Architecture, using the following basic model:

1. Contributing agency develops a "service" (e.g. Pay by Check)

2. It gets stored in an ICC repository

3. Can be offered for re-use

4. Service can be called from other applications

OFM's ESB connects to DIS ESB, and perhaps to others as well.

DIS's ICC is a broker it gets agencies to list services (or data) they are publishing and can explain/broker to other potential consumers.

Services are defined and offered in tiers:

1. Enterprise-wide service, used by more than one agency

1. Internal but common across a single agency

1. Internal to workgroup

As yet there are few other examples of ICC's in state government:

OFM is working on standing one up, principally to deliver mainframe to smaller agencies

DSHS ISSD is a consumer of these functions through Famlink/ProviderOne

Jindex is almost ready to offer an interface to DSHS on background checks

ESD Next Generation Tax initiative

DOH/DOL collaborate on vital statistics

Issues:

Do other state agencies have the infrastructure to connect and use a real ICC?

How to pay for or charge for the costs?

Current ICC Projects

Central Print

Part of Provider One. Works with vendor CNSI to get letter templates to the state printer.

Case Study: DOT Data Governance Council

Background

Around 1984 a group of programmers, database administrators and end user customers (later to be called Data Stewards) got together in a group called the Data Committee. This committee discussed the issues surrounding data programming and database use and assigned specific categories for data types and came up with an abbreviation naming standard. The pieces that describe a data element are the Entity-Description-Class Word. An example of this for naming would be Biennial Monthly Amount and would be abbreviated to the element of BIMTAMT.

This naming standard held consistent until the 1990s when client server architecture came into existence. This radically changed the way programmers and database engineers used naming to describe elements. Field size went from 32 characters to 255 characters which allowed much longer names and descriptions. Also, the front end naming presentation of elements could now be different than the backend database naming structure. This tended to make these fields more user friendly and also had the side effect of not needing the Data Steward to be involved in field naming as much.

Data Council Charter (Excerpt)

PURPOSE:

The purpose of the Data Council is to place accountability for data resources in the hands of departmental employees with a stake in its quality and protection. The Data Council will provide a forum for the Data Stewards, Data Creators, Data Users, and Data Management Services to work cooperatively in the best interests of the department. The Data Council will also serve as a resource to the Customer Advisory Group (CAG).

GOAL:

The goal of the Data Council is to provide leadership and guidance in order to:

Maximize the consistency of data definitions and values throughout the department;

Minimize the cost of collecting and maintaining accurate data within the department;

Encourage sharing of accurate and timely data throughout the department; and

Promote data as a departmental asset.

COUNCIL ORGANIZATION:

The Data Council will serve as a sub-committee of the CAG. As a sub-committee of the CAG, all meetings are open to attendance and participation by any WSDOT employee.

MEMBERSHIP:

The Data Council will be composed of Data Stewards, Data Creators and Data Users from throughout the agency and the Data Management Services Manager from the Office of Information Technology (OIT).

Each Region, Service Center and Division will have an opportunity to designate representation from their organization.

Data Council members will serve staggered two-year terms on a rotational schedule. When new members are needed, the Data Council may recommend potential candidates for membership on the Council to the Executive(s) of organizations needing new representation. The final choice of representation remains with the Executive(s) of those affected organizations. It is acceptable to include an exiting member in the candidate list. The Data Council will build the first rotation schedule after July of 1998.

At the end of the first year, the Data Council will evaluate all aspects with respect to its membership and make recommendations to the CAG about the best structure for the Council.

The Data Council uses the following terminology.

DEPARTMENTAL DATA:

Any data which is required for the organization (WSDOT) and is used in a decision making process.

Departmental data may be called corporate data or organizational data in publications relating to this topic which have been published in various trade journals or other sources relating to the general field of data management.

DATA STEWARD:

An employee responsible for the quality of some segment of departmental data.

Data Stewards are expected to manage data for the welfare of the department. A Data Steward is accountable for planning data collection and use, definition of data elements, quality control and authorizing access to a set of department data.

A primary function of a Data Steward is to seek efficiency in the management and use of departmental data.

DATA CREATOR:

An employee responsible for creating or updating departmental data.

DATA USER:

An employee responsible for the proper use of departmental data.

OIT DATA MANAGEMENT SERVICES MANAGER:

The OIT Data Management Services Manager is a technical person with management authority and a stake in the quality of departmental data.

The OIT Data Management Services Manager manages departmental data for the welfare of the department. The OIT Data Management Services Manager is responsible and accountable for the daily operation of the departments Data Base Management Systems. This includes the planning for, implementation of, and security of database files contained within each Data Base Management System, which contain departmental data.

DATA COUNCIL ACTIVITIES:

The Data Council will provide leadership and guidance in creating and promoting data as a departmental asset by:

1) Recommending corporate data policies, standards, guidelines, and implementation strategies to the CAG and the department as a whole.

2) Review Corporate data activities and standards including but not limited to:

Data repository development and use

Data ownership

Data access policies

Data accuracy

3) Promoting principles of data resource management and identifying relevant training and education.

4) Providing a forum for the discussion of data issues.

CONFLICT RESOLUTION:

The following steps will be taken to resolve a disagreement within the Data Council.

1. An Issue Statement will be prepared by the appropriate member(s) of the Council. (See Attachment A.)

2. The Council will meet in an attempt to resolve the issue.

3. If the issue cannot be resolved within the Council, this issue will be presented to the CAG for resolution.

Case Study: Identity Management

Currently in the early stages of negotiation is a multi-state digital identity pilot program, drawing on Washingtons existing collaborative relationship with the Federal Government on the enhanced drivers license.

In the current vision of this effort, each participating state would commit at least one issuer of identity documents and / or one government service (a relying party); at least one non-governmental service (relying party) would be required and recruited, as well as a small group of citizens.

The project would endeavor to demonstrate the value of inter-jurisdictional collaboration in this area, develop best practices for future efforts in the public sector, and provide citizens better access to higher-value self-service online transactions for government services.

Key principles for implementation include:

Citizen Value

Cost Reduction

Security and Privacy

Re-Usable Identity

Key motivating factors have been identified:

Budget pressure motivates us to reduce cost of service delivery

Reduced resources, constant demand

Online services offer a key opportunity for cost reduction

43% of office transactions do not actually require an office visit

Office transaction costs $7.19

Online transaction costs $0.63

Today:

Citizens must enroll in and maintain multiple digital identities for access to services

Level of identity assurance is therefore low

and online services are limited to low-value transactions

Analysis Exercise: Balanced Score Card for Statewide Data Sharing initiative

Strategic Theme:

Statewide Data Sharing

Objective

Measurement

Target

Initiative

Owner

Supplier

Financial

1. Lower data maintenance costs

2. Lower data storage costs

Storage cost reduced.

< 50 % of the existing storage and maintenance costs.

Storage cost tracking

DIS/Agencies

Not Applicable

Customer

1. Single version of data

2. More control over data

1. Customer service times.

2. Customer satisfaction

Single point of customer data entry

Customer relations and satisfaction.

Data Governance Committee

Not Applicable

Internal

1. Customer data integration (CDI).

2. Master data management (MDM).

3. Reduce redundancy of data.

4. Reduce altering of the meaning of data.

1. Single view of customer data.

2. Identified owners of data.

3. Reduced maintenance on data.

4. Increased data integrity.

Single identity for the core data elements identified across the state.

Single point of origin for customer data without altering the meaning of data.

1. Data Governance committee.

2. Data stewards.

Not Applicable

Learning

Education of state agencies the concept of MDM and CDI.

% of state agencies sharing core data.

50 % of MDM & CDI achieved in 3 years;

100% in 5 years.

CDI and MDM training for data architects.

CIOs of each agency.

Training vendors.

Roadmap to achieving a Statewide Data Architecture and Exchange

Legislative mandate to establish data governance body

Governors executive order for adherence to the policies established by the governance body

Governance body comprising of data architects from all agencies is formed

Strategies to identify customers/ entities who either serve or get a service from the state

Core data entities such as citizens, employers, service providers identified

Identify agencies who use the identified core data entities

Identify Data Owners and Data Stewards for the core data entities

Data owners and stewards establish policies on data structures and rules on sharing data

Governance body establishes technology framework to share data

Strategy to modify existing applications to adhere to data sharing standards is established and agreed upon.

Is application new

For each Agency

Application that uses shared data

Follow Data Governing committee & ISB guidelines

YES

NO

References

Topic

Short Name

Citation

Link

Shared Services

Michigan's Successful Experience With Centralizing

Government IT

Gartner, Industry Research, Publication Date: 12 January 2006 ID Number: G00136603

Proprietary

Data Standards

Interview: Chris Kemp, Manager, Data Management Services, WSDOT

WSDOT Data Standards.doc

http://groups.google.com/group/leaderpathgroup3/files?hl=en

Data Standards

Interview: Tim Crabb; Infrastructure Services Manager, WSDOT

WSDOT Data Standards.doc

http://groups.google.com/group/leaderpathgroup3/files?hl=en

Data Standards

Interview: Noel Morgan, IT Enterprise Implementation Manager, WSDOT

WSDOT Data Standards.doc

Successful and Unsuccessful Data Standards Strategies in Private Business and Government Entities.

http://groups.google.com/group/leaderpathgroup3/files?hl=en

Data Standards

Interview: Gordon Kennedy, IT Information Resource Manager, WSDOT

WSDOT Data Standards.doc

http://groups.google.com/group/leaderpathgroup3/files?hl=en

Data Project Management

U.K. Dept of Health: Prescription for Disaster

Baseline Magazine, November 13, 2006.

http://www.baselinemag.com/c/a/Projects-Management/UK-Dept-of-Health-Prescription-for-Disaster/

Risk

Chapter 1. The risks of IT innovation in government

Book: Making Smart IT Choices: Understanding Value and Risk in Government IT Investments

http://www.ctg.albany.edu/publications/guides/smartit2?chapter=3

Data Standards by DIS

State Enterprise Data Standards Initiative Charter

Data Standards Charter v2.0 ISB.doc scanned in copy.

This is a copy of the original charter used by the ISB Data Standards Committee chaired by Paul Warren Douglas

Dis.wa.gov or isb.wa.gov

History of WSDOT Data Standards

WSDOT Data Standards.doc

WSDOT Data Standards.doc

http://groups.google.com/group/leaderpathgroup3/files?hl=en

Data Standards Charter WSDOT

Data Council Charter WSDOT

Data Council Charter-Revised.doc

http://groups.google.com/group/leaderpathgroup3/files?hl=en

Data Strategy

State of California Data Strategy Report

State of Californias Data Strategy Report

http://www.ocio.ca.gov/Government/Publications/pdf/Statewide_Data_Strategy_Report_Final_v1_0_08062009.pdf

IT Strategic Plan

State of Californias IT Strategic Plan

State of Californias IT Strategic Plan

http://www.itsp.ca.gov/

Data Standards

Federal Data Standards

Federal data standards related to Payroll, HR & Training

http://www.opm.gov/egov/documents/architecture/index.asp

EA Strategies

Conneticut EA Strategies

State of Conneticuts Enterprise Architecture Strategies; & Conceptual Architecture Principles

http://search.usa.gov/search?affiliate=CT.gov&v%3Aproject=firstgov&CATEGORIES_ID=0&query=Enterprise+Architecture+Strategies

IT Consolidation

Missouri IT Consolidation

State of Missouris Auditors Yellow Sheet on IT Consolidation Report

http://www.auditor.mo.gov/press/2009-112.htm

EA Strategic Approach

State of Michigan EA

State of Michigans EA Strategic Approach

www.michigan.gov/documents/dit/2007_EA_Strategic_Approach_206296_7.pdf

Strategic Plan

State of Michigan IT Strategic Plan

State of Michigans Strategic Plan 2008-2012

Used references Appendix A & N

http://www.michigan.gov/itstrategicplan/0,1607,7-222-50226---,00.html

Enterprise Architecture

State of Michigan Enterprise Architecture

State of Michigans Enterprise Architecture Strategic Approach

http://www.michigan.gov/dit/0,1607,7-139-30637_47639---,00.html

Integration Competency Center

Designing an ICC

How to Design and Implement an

Integration Competency Center (ICC)

http://vip.informatica.com/content/Downloads?docid=6525&lsc=1-1342938704

Identity Mgt.

User-Centric Identities

Gartner Research, Publication Date: 26 May 2010 ID Number: G00200800 User-Centric Identities Need Identity Proofing and Other Controls for Sensitive Data Access

Proprietary

Briefing: Statewide Data Architecture and Exchange

Page | 3

State of

Washington ISB

Data

Governance

Committee

(DGC)

Senior

Executive

Sponsor

Data

Creator

Data

Steward

Data

User

Provides oversight of

the DGC

Appointed by the Giovernor

Data Creator, Data Steward and Data

User represent state agencies

Structure of Proposed Data Governance Committee

Members of the Committee

Student Achievement

Postsecondary Learning

Health

Vulnerable Children & Adults

Economic Vitality

Mobility

Public Safety

Natural Resources

Culture & Recreation

State Government

N

S

E

W