Ready Data Part 1 – The Key to Rapid Analytics - Harbour
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Transcript of Ready Data Part 1 – The Key to Rapid Analytics - Harbour
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)
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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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