May 2, 2023

Leveraging Data Assets for NYS Through Data-Centric Thinking

Raising the Data Literacy of New York State

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Data Governance

Is the exercise of authority and control (planning, monitoring, and

enforcement) over the management of data assets. Guides how all

other data management functions are performed. Is high-level

executive data stewardship.

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Data Governance Framework

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Data Lifecycle Model

Plan & Task Acquire & Assess Authorize & Process Discover & Share Analyze & Exploit Retain & Retire

Data Governance

FeedbackGuidance

FeedbackGuidance

FeedbackGuidance

FeedbackGuidance

FeedbackGuidance

FeedbackGuidance

Conceive and plan the creation of data, including capture

method and storage options.

Receive data, in accordance with

documented policies, from data providers.

Transfer data to an archive, repository,

data center with appropriate

permissions.

Publish and share data using tools and services so that

people can find data and understand the

content.

Description of processing steps for

converting an observation into a

derived data product or report.

Determine whether or not organization

wants to maintain data or dispose of it

according to procedures.

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Data Governance Components

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Present State versus Desired State

ITBusiness DATABusinessIT

Program-Based Work

Program-Based Work

Project-Based Work

Project-Based Work

Present State Desired State

DATA

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Data-Centric Approach

Data and IT governance are synchronized relative to specific IT projects to help ensure

compliance and data reuse.

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Seven Deadly Data Sins

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Solutions Already Exist

NYS can tailor frameworks and help

ensure that its can leverage data to its

fullest.

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Complementary Models & Standards

Project Management InstituteProject Management Body of Knowledge (PMBOK)

CMMI InstituteCapability Maturity Model (CMM)

There is already a precedent for using well-known and trusted standard that the state can tailor.

Project Management

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Complementary Models & Standards

Project Management InstituteProject Management Body of Knowledge (PMBOK)

Data Management AssociationData Management Body of Knowledge (DMBOK)

CMMI InstituteCapability Maturity Model (CMM)

CMMI InstituteData Management Maturity Model (DMM)

There is already a precedent for using well-known and trusted standard that the state can tailor.

Project Management Data Management

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Data Management Maturity ModelData

Management Strategy

Data Governance Data Quality

Platform & Architecture

Data Operations

Implementation OversightCommunication Coordination

MetadataOversight

Business ITAlignment

InfrastructureOversight

Business ProcessData Requirements

Quality RulesQuality Criteria

DataInfrastructure

Data Profiling ResultsShared Services

Architecture

Official Data

StakeholderAlignment

Supporting Services

Data Management

Strategy

Data Management GoalsCorporate CultureData Management FundingData Requirements Lifecycle

DataGovernance

Governance ManagementBusiness GlossaryMetadata Management

DataQuality

Data Quality FrameworkData Quality Assurance

DataOperations

Standards and ProceduresData Sourcing

Platform & Architecture

Architectural FrameworkPlatforms & Integration

Supporting Processes

Measurement & AnalysisProcess ManagementProcess Quality AssuranceRisk ManagementConfiguration Management

Component Process Areas

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Data Management Maturity LevelsData

Management Goals

Governance Model

Corporate Culture

Standards & Procedures

Data Requirements

Lifecycle

Implementation OversightCommunication Coordination

MetadataOversight

Business ITAlignment

InfrastructureOversight

Business ProcessData Requirements

Quality RulesQuality Criteria

DataInfrastructure

Data Profiling ResultsShared Services

Architecture

Official Data

StakeholderAlignment

Data Management

Funding

L1

L4

L3

L4

L5

L2

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Example: Data Governance Empowers Data Sharing

Data management helps ensure that provisioning and access control decisions are made in an automated, auditable, and accountable

way.

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Conclusions

• NYS needs many different capabilities to leverage data and analytics.

• Information technology is necessary but insufficient.

• NYS needs technical and nontechnical solutions to address all issues.

• NYS needs to have comprehensive data governance framework.– Using a mature DMM objectifies the problem.– Provides a roadmap for the future.

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“Technology gives us power, but it does not and cannot tell us how to

use that power. Thanks to technology, we can instantly

communicate across the world but it still doesn’t help us know what to say.”

-Jonathan Sacks-

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Contact Information

Todd Harbour

Chief Data Officer (CDO), New York State

518-473-0780 (Phone)

todd.harbour@its.ny.gov

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Questions

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Data Management Maturity Levels

Data Requirements

Lifecycle

Data Management Funding

Corporate Culture

Data Management Goals

Standards and Procedures

Level 1: Performed Level 2: Managed Level 3: Defined Level 4: Measured Level 5: Optimized

Data requirements are gathered and evaluated against deliverables on a project basis. Data sets

and attributes are defined, aligned and prioritized against project objectives and core

business functions.

Funding for data management is part of the IT budget process viewed as a cost item and

consolidated with other IT expenditures. TCO estimates and funding for DM initiatives aligned

with immediate project-based business objectives.

Inconsistent stakeholder alignment on data management objectives. Unclear distinctions

exist between ‘data’ and ‘information.’ Communication is informal and ‘grapevine’

based.

Goals, objectives, scope and priorities informally established for specific projects. Data domains determined based on project needs. Resource

competition occurs.

Data attributes defined and cross-referenced to business requirements and applications. DM operations processes, transformations and workflows documented. Data definitions implemented in data model and aligned

semantically.

Formal business case with TCO cost components are consistently defined and allocated to both

business areas and operational functions. Tangible benefits from the investment in data

management are quantified.

Mechanism exists for data management strategy alignment. Communication about DM

governance occurs at business unit level. Roles and structures for DM are defined and in process

of being implemented.

Shared organizational objectives are established for projects. Priorities managed across program

or business area and linked to business objectives. Staff accountability defined and

documented.

Consensus definition from all involved stakeholders on core data attributes and systems

of record. The canonical data model and semantics repository are used as the foundation for managing organizational data requirements.

Standard business case methodology including TCO structure and allocation models are fully

defined and implemented. All aspects of funding for both ‘building’ and ‘running’ data

management capabilities are aligned with organizational governance.

Data management program is resourced to ensure sustainability. Executive management is

fully engaged in setting DM objectives. Mandates issued to ensure adoption.

Communication is aligned with governance.

Goals and priorities are synchronized at the organizational level, aligned with business objectives and approved by organizational

governance. Data management activities linked to ROI analysis.

Data requirements verified for every initiative. Quality of canonical data model and semantics

repository based on standard metrics. Operational workflows measured for

effectiveness as the organization evolves.

Funding model, TCO structure and ROI methodology are standardized and audited

against organizational objectives. Allocation and chargeback methodology is implemented based

on traceable client usage of data resources.

Data is understood as an asset and quantified using standard metrics. Performance

benchmarks and data goals are aligned with business strategy. Communication is monitored

for effectiveness.

DM programs aligned with regulatory and business objectives. Accountability monitored

for compliance and value. Formal quantification of outcomes are fundamental in running the

data management program.

Continuous improvement is formally implemented to ensure the selection,

prioritization and verification of data assets. Data lifecycle metrics are continually refined and

used as a critical measure by senior management.

Funding model is flexible and encourages ‘data driven’ innovation based on the evolving goals

and priorities of the organization. Predictive models are used to ensure that sustainable

funding is in place for the data management program.

Data management competency is formally recognized. Collective ownership of data as an

operational asset is in place and understood as a component of competitive advantage.

Communication strategy encourages data innovation.

Data management goals are continually evaluated and aligned with organizational

objectives based on formal business process analysis. Stakeholders are coordinated and

proactively engaged.

Governance Model

Governance is event driven. Data management ownership, stewardship and accountability are

project based and often informal. Data management policies and metrics are defined

but inconsistently implemented

Governance and accountability structures exist at business unit level. Executive sponsor exists.

Roles and responsibilities are formalized, aligned and communicated in accordance with key

milestones.

Formal governance structures exist with clear roles, responsibilities and lines of authority.

Formal policies and procedures are documented and adopted. Shared language about DQ

adopted. Standard metrics are used to measure performance. CDO function implemented.

Governance structure is continually monitored using standard metrics. Performance goals and

resource requirements are based on data management objectives. Business, IT and

operations aligned. Governance funded as non-discretionary.

Data governance enhancements based on proactive input from stakeholders. CDO has final decision-making authority. Status of DM control is a standard item for executive management. Predictive models used to manage data assets

and allocate resources.

Value of standards and procedures are recognized and planned for major initiatives.

Business processes, capabilities and authoritative data sources identified for critical

data sets. Data control process is often IT focused.

Uniform selection criteria established for authoritative sources. Formal standards and

procedures are implemented. Shared attribute mapping and common ontology established. Shared data elements are traced across data

stores.

Standards and procedures are established, operationalized and formally documented.

Business definitions and EW ontology used for all attributes based on ‘single term/single definition’ principle. All authoritative data

sources identified.

Standards, policies and procedures are actively monitored for compliance and updated as

requirements evolve. EW ontology is maintained in a centralized metadata repository and all data

definition adjustments are synchronized.

Policies, standards, processes and governance are formally reviewed and enhanced on a

repeatable basis using analytical metrics and formal feedback mechanisms. Industry standard

ontology supported and embedded into all systems and processes.

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