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Data Management Efforts

Bureau of Land Management (BLM) Christine Hawkinson, Bureau Data Administrator. Data Management Efforts. BLM Data roles. Bureau Data Administrator Responsible for data management policy and guidance Data Management (Division of Resource Services) Operations

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Data Management Efforts

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  1. Bureau of Land Management (BLM) Christine Hawkinson, Bureau Data Administrator Data Management Efforts

  2. BLM Data roles • Bureau Data Administrator • Responsible for data management policy and guidance • Data Management (Division of Resource Services) Operations • Development of national data standards • Development and implementation of national geospatial datasets • Division of Information Resource Management -Operations • Database (application) development • Implementation of applications • Geospatial Program • Geospatial Infrastructure and Issues • Data Stewards – national, state and local • State and Field Offices • Data Collection

  3. Data life cycle

  4. Data Development – High Level

  5. Data standard & standardized dataset • Data Standard • identify logical / business data requirements • document what information is needed to meet those business requirements to provide for high-quality data. • Defined before data is collected. • Not specific to geospatial data • Standardized Dataset • Anational schema with a shared set of attributes and domain values, capable of being seamlessly compiled to a National dataset. The implementation of a data standard also results in a standardized dataset. • Database / Geodatabase • A defined, automated, shared and centrally managed collection of data. A data base is a collection of interrelated data stored in a structured manner.

  6. Data Advisory Committee (DAC) DAWG: Data Advisory Working Group

  7. Propose data development project • Deliverables: • Data Development Proposal (template) • Development Schedule – through Defined Dataset • Purpose: • Defines initial scope • Communicates intent of work • Locate Subject Matter Expertsfor Development

  8. Enterprise data framework

  9. Data Standard Development • Deliverables (templates): • Data Standard Report • Domain Document • Quality Plan • Metadata Description • Implementation Guidelines (opt) • Purpose: • Describes business data, rules and domains for bureau • Provides common schema • Provides information on how quality will be assessed (insures will meet minimum quality requirements)

  10. standardized dataset development • Deliverables (templates): • Standardized Dataset Report • Domain Document • Quality Plan and Checklist • Metadata Description • Purpose: • Provides common schema and domain values for national compilation of diverse local datasets • Provides plan for insuring quality and completeness of dataset

  11. Implemention plan • Dataset Deliverables: • Implementation Plan • Quality Checklist • Metadata Description (final) • Data Certification • National Certified Dataset • Database Deliverables: • Database design • Data Management Plan (including quality) • Data Migration Plan (optional)

  12. Change management • Deliverables: • Updates to existing standard (optional) • Updates to schema • Retired standard

  13. National Dataset implementation • States provide state-level datasets • Quality checked for minimum required criteria • Quality reports • Specific domains, attributes, topology • Trusted Data Aggregation: States responsible for error correction • Metadata reviewed for completeness • Meets required minimum • Replicated into national dataset • No data updates at aggregated national level • Timing of updates from states depends on data steward requirements • Dataset is certified by data steward • Can be published once certified (internal/external)

  14. Completed Datasets Processes for aggregation vary Examples: • Basic National datasets • Administrative Boundaries • Land Use Planning Areas • Allotments and Pastures • Summarized National datasets • Land Health Reporting • Visual Resource Inventory

  15. Example: Land treatments • Several redundant systems • Crosses programs • Same types of treatments/data which are completed for various reasons • Looking at developing a treatment module that can be called by different systems • Same data standard; same data elements • Store once, use and update many times

  16. DATA Management functions DAMA DMBOK,2009

  17. Questions?

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