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

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Government Technology NY IT LF presentation - Ready Data Part 1 – The Key to Rapid Analytics - by Todd Harbour

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

  1. 1. March 21, 2017 Leveraging Data Assets for NYS Through Data-Centric Thinking Raising the Data Literacy of New York State
  2. 2. March 21, 2017 2
  3. 3. March 21, 2017 3 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.
  4. 4. March 21, 2017 4 Data Governance Framework
  5. 5. March 21, 2017 5 Data Lifecycle Model Plan & Task Acquire & Assess Authorize & Process Discover & Share Analyze & Exploit Retain & Retire Data Governance Feedback Guidance Feedback Guidance Feedback Guidance Feedback Guidance Feedback Guidance Feedback Guidance 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.
  6. 6. March 21, 2017 6 Data Governance Components
  7. 7. March 21, 2017 7 Present State versus Desired State ITBusiness DATABusinessIT Program-Based Work Program-Based Work Project-Based Work Project-Based Work Present State Desired State DATA
  8. 8. March 21, 2017 8 Data-Centric Approach Data and IT governance are synchronized relative to specific IT projects to help ensure compliance and data reuse.
  9. 9. March 21, 2017 9 Seven Deadly Data Sins
  10. 10. March 21, 2017 10 Solutions Already Exist NYS can tailor frameworks and help ensure that its can leverage data to its fullest.
  11. 11. March 21, 2017 11 Complementary Models & Standards Project Management Institute Project Management Body of Knowledge (PMBOK) CMMI Institute Capability Maturity Model (CMM) There is already a precedent for using well-known and trusted standard that the state can tailor. Project Management
  12. 12. March 21, 2017 12 Complementary Models & Standards Project Management Institute Project Management Body of Knowledge (PMBOK) Data Management Association Data Management Body of Knowledge (DMBOK) CMMI Institute Capability Maturity Model (CMM) CMMI Institute Data 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
  13. 13. March 21, 2017 13 Data Management Maturity Model Data Management Strategy Data Governance Data Quality Platform & Architecture Data Operations Implementation Oversight Communication Coordination Metadata Oversight Business IT Alignment Infrastructure Oversight Business Process Data Requirements Quality Rules Quality Criteria Data Infrastructure Data Profiling Results Shared Services Architecture Official Data Stakeholder Alignment Supporting Services Data Management Strategy Data Management Goals Corporate Culture Data Management Funding Data Requirements LifecycleData Governance Governance Management Business Glossary Metadata Management Data Quality Data Quality Framework Data Quality Assurance Data Operations Standards and Procedures Data Sourcing Platform & Architecture Architectural Framework Platforms & Integration Supporting Processes Measurement & Analysis Process Management Process Quality Assurance Risk Management Configuration Management Component Process Areas
  14. 14. March 21, 2017 14 Data Management Maturity Levels Data Management Goals Governance Model Corporate Culture Standards & Procedures Data Requirements Lifecycle Implementation Oversight Communication Coordination Metadata Oversight Business IT Alignment Infrastructure Oversight Business Process Data Requirements Quality Rules Quality Criteria Data Infrastructure Data Profiling Results Shared Services Architecture Official Data Stakeholder Alignment Data Management Funding L1 L4 L3 L4 L5 L2
  15. 15. March 21, 2017 15 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.
  16. 16. March 21, 2017 16 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.
  17. 17. March 21, 2017 17 “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-
  18. 18. March 21, 2017 18 Contact Information Todd Harbour Chief Data Officer (CDO), New York State 518-473-0780 (Phone) todd.harbour@its.ny.gov
  19. 19. March 21, 2017 19 Questions
  20. 20. March 21, 2017 20 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.
  21. 21. March 21, 2017 21

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