Data Management Domain-Technology Reference Model Version 2.0

3.1 Introduction and Background

Data is the representation of facts, concepts or instructions in a formalized manner suitable for communication, interpretation or processing. When data is combined appropriately, information is derived. Much like the natural resources it manages, Interior’s data and information are valuable assets that must managed. The full value of data and information resources is realized when Interior is able to appropriately share that data and information internally, as well as with external partners.

The focus of the Interior Enterprise Architecture is on providing guidance for information technology (IT) issues and initiatives that are Interior-wide or multi-bureau in scope. The Data Management architecture defines the mechanisms and standards for collecting, documenting, accessing, managing, maintaining the integrity of and securing Interior’s electronic data assets.

If used correctly, the Interior Enterprise Architecture will act as a catalyst for those looking to capitalize on its contents and better understand the full meaning of its guidance. This understanding will permit IT personnel to better engage the non-IT organization in discussions around tradeoffs and priorities within the proper governance structure (e.g., Management Initiatives Team (MIT), Information Technology Management Council (ITMC)). The Interior Enterprise Architecture is not intended to be the “last word” (e.g., some automated checklist for product selection). It is intended to be one of the “first words” to assure that Interior’s mission priorities and its IT priorities remain closely aligned.

There are many instances within Interior of data sharing and reuse. Conversely, there are also many examples of where data is not reused and shared enterprise-wide but collected and duplicated in innumerable databases throughout Interior or even within a single Bureau (e.g., names, addresses, and social security numbers may be stored and maintained in every application system that needs that particular data). It is difficult to determine which database stores the most current or correct information. Storing and maintaining multiple copies of the same data throughout the enterprise is time consuming and expensive.

Because Interior is incorporating the OMB’s Federal Enterprise Architecture (FEA) models, the technical guidance provided by the subject area experts within a domain spans both the Service Component Reference Model (SRM) as well as the Technical Reference Model (TRM). For the Data Management domain, the SRM elements are as follows:

Service Domain(s): The Back Office Services Domain defines the set of capabilities that support the management of enterprise planning and transactional-based functions.

Service Type(s): Data Management - defines the set of capabilities that support the usage, processing and general administration of unstructured information.

Development and Integration - defines the set of capabilities that support the communication between hardware/software applications and the activities associated with deployment of software applications.

Component(s): Data Classification – defines the set of capabilities that allow the classification of data.

Meta Data Management – defines the set of capabilities that support the maintenance and administration of data that describes data.

Data Cleansing – defines the set of capabilities that support the removal of incorrect or unnecessary characters and data from a data source.

Data Exchange – defines the set of capabilities that support the interchange of information between multiple systems or applications.

Data Recovery – defines the set of capabilities that support the restoration and stabilization of data sets to a consistent, desired state.

Extraction and Transformation – defines the set of capabilities that support the manipulation and change of data.

Loading and Archiving – defines the set of capabilities that support the population of a data source with external data.

Data Mart – defines the set of capabilities that support a subset of a data warehouse for a single department or function within an organization.

Data Warehouse – defines the set of capabilities that support the archiving and storage of large volumes of data.

Data Integration - defines the set of capabilities that support the organization of data from separate data sources into a single source using middleware or application integration as well as the modification of system data models to capture new information within a single system.

These SRM service elements are likewise supported by Interior’s IT (technical) infrastructure (e.g., servers, networks). Within this infrastructure are individual TRM components for which this domain team is providing guidance. The graphic below outlines those TRM elements for this domain that support the service needs of the SRM.

Additionally, it’s doubtful that a single domain chapter from the TRM can be used to address a substantive issue. More realistically, a few architecture domains may need to be reviewed when addressing an important IT decision. For example, if Interior was considering the creation of a new Interior-wide Web application that could be used both by the general public and Interior personnel, then the TRM chapters like Data Management Technologies, Information Security, Distributed Systems Management and Application Development might all need to be reviewed.

3.2 Architectural Principles

The principles listed below provide guidance for the design and selection of technology components that will support the data management needs of Interior-wide IT initiatives.

Principle 1: Data Sharing
	Data and information must be managed to facilitate data sharing across Interior, with our partners and the public. Rationale: Reduces duplication of effort. Achieves economies of scale, especially through cooperative data collection efforts. Leads to increased data quality. Conforms with the Government Paperwork Elimination Act, Clinger-Cohen Act, Paperwork Reduction Act, Electronic Freedom of Information Act Amendments of 1996 and section 508 of the Rehabilitation Act. Enhances reusability of data and information. Implications: Data and information resources will need to be defined in bureau and department information architectures. Need to agree upon data exchange mechanisms and protocols. Data that is common among many business applications will be sourced and updated from a single authoritative source. Need to establish common core data standards, including data definitions. Need to agree upon and establish a common data standards process. Need a consistent data management process. Need well-documented and defined metadata. Sharing and access needs to be timely. Additional effort may be required in the presentation of data to meet accessibility requirements. Data should be made available in a variety of formats suitable for the user. The value of information is increased when not held in isolated pockets. 12. Need to balance the desire to share data with sensitivity, privacy and confidentiality restrictions. 13. Need to take electronic records management requirements into consideration.

Principle 2: Data Collection and Reuse
	In considering data requirements, we should look to reuse existing data before we buy. If no data exists within Interior, consider acquisition of data from external sources before collecting/creating new data. Rationale: Saves money. Leads to increased data quality and integrity. Saves time. Supports the promotion of standards. Supports the Federal Activities Inventory Reform Act, Paperwork Reduction Act and Clinger-Cohen Act. Implications: Need a clearinghouse of metadata for existing data. If you are going to acquire data, consider facilitating its use by all of Interior. Potential data sources’ data quality must be validated before acquisition or collection of data. We are at the “supplier’s” mercy for future cost, quality, availability, service and metadata. Good data requirements are needed to evaluate potential data sources. Need a standard process for acquiring data, when formal agreements are required. Data that is common among many business applications will be sourced and updated from a single authoritative source. When acquiring data from private vendors, licensing restrictions should be considered.

Principle 3: Data Security
	Data needs to be secured according to its sensitivity. Rationale: Complies with the Computer Security Act, the Privacy Act, the Government Information Security Reform Act, Office of Management and Budget (OMB) Circular A-130 Appendix 3, Electronic Freedom of Information Act Amendments of 1996, Computer Matching and Privacy Protection Act and Section 515 of the Treasury and Consolidated Agency Appropriation Act. Enhances public trust. Helps safeguard confidential and proprietary information. Enhances the proper stewardship over information. Enhances the integrity of the information. Helps to ensure legal and proper use of information. Implications: Need to establish data sensitivity and privacy classifications and a review process. Need to conduct periodic (re)assessments of data classifications. May require additional resources (e.g., personnel, hardware and software). Will lead to the use of authentication technologies; for example, digital signatures or passwords. May need to edit sensitive data that is released to the public. Employees and contractors will require training regarding use of sensitive data. Data stewards will require training on this “new” stewardship responsibility.

Principle 4: Data Contingency Planning
	Contingency planning processes need to be in place to ensure data availability. Rationale: Supports Interior Continuity of Operations (COO) plans. Ensures continued operations. Protects Interior data. Complies with Federal Preparedness Circular 65, FEDERAL EXECUTIVE BRANCH CONTINUITY OF OPERATIONS (COOP). · Allows Interior to continue its mission and meet legal requirements. Implications: Need to establish data recovery priorities. Resources must be provided for data recovery testing. Alternative off-site data archives need to be in place and synchronized. Need periodic reassessment of bureau/Interior COO plans to ensure data availability is addressed.

Principle 5: Data Lifecycle
	Information is valued as an Interior asset; therefore, Interior data needs to be managed throughout its lifecycle. Rationale: Data has its own lifecycle related to the lifecycle of the mission, not the information system. Facilitates data reuse and locating data at each stage of its lifecycle (including historical). Managed data improves the ability to accelerate sound decision-making. Meets the legal requirements of Paperwork Reduction Act, Government Paperwork Elimination Act, Federal Records Act, Clinger-Cohen Act, the OMB Information Initiative on the National Spatial Data Infrastructure and OMB Circular A-130 regarding data quality (i.e., utility, objectivity and integrity). Increases the usefulness and value of data. Promotes the wise use of Interior data assets. Implications: Interior needs to dedicate resources to data management in addition to relying on the DBA function. Need ongoing management support and oversight throughout the data lifecycle: data stewards, data managers and data administrators. Management of data needs to be tied to workflow of the business process. Data management (including its on going storage and archiving) is a mission cost that transcends an individual project. Data quality is everyone’s responsibility. Integrate data resource planning with business and information technology planning. Need a consistent data management process.

Principle 6: Data Stewardship
	Data and information must be managed and maintained as a stewardship responsibility to support the mission of the department. Rationale: Data is a resource critical to the mission of Interior. Like natural and cultural resources, data needs stewards who are responsible for its valuation, preservation, security, access, quality, and utilization. Data stewardship promotes common business rules, facilitates information sharing and improves data integrity. · Data stewardship promotes the establishment of authoritative sources. · Complies with requirements of Section 515 of the Treasury and Consolidated Agency Appropriation Act. Implications: Need to develop a data stewardship program that will transcend many organizational boundaries. Need to define data stewardship responsibilities that span the entire data lifecycle. Need to identify and train subject matter experts as data stewards. Need to include various levels of stewardship while leveraging and adhering to Federal data programs and standards (e.g., Federal Geographic Data Committee (FGDC), National Institute of Standards and Technology). Data stewards need to ensure that metadata is captured and managed.
Principle 7: Data Standards
	Interior will strive to create, acquire, and share data that adheres to data standards defined internally, with consideration to existing national standards. Rationale: Facilitates exchange of data. Facilitates the migration of data. Using existing national standards reduces burden on Interior and complies with OMB Circular A-119, Federal Participation in the Development and Use of Voluntary Consensus Standards and in Conformity Assessment Activities. Makes data collection more consistent and increases data quality, compatibility, integrity. Implications: Need to develop a data standards program that will transcend many organizational boundaries. Adopting standards may require changes to existing data or planning for migration to the new standard. Need to raise awareness of applicable standards. Interior needs to increase participation in standards organizations to make it more likely to get industry adoption of standards that serve Interior’s needs. Need the ability to review the data standards and determine appropriateness. Need to establish common data standards, including data definitions. Long-term commitment of resources will be needed to establish and maintain data standards. Data stewards will identify and manage data standards model to be effective.

Principle 8: Mainstream Technologies
	Data management will use industry-proven and mainstream technologies. Rationale: Increases the probability that solutions remain viable through the system lifecycle. Promotes robust product support. Enables greater use of commercial-off-the-shelf solutions. Enables faster deployment of systems. Complies with OMB Circular A-130 “Management of Information Resources”, which requires the application of up-to-date information technology. Implications: Need a coordinated mechanism for sharing information on industry-proven, mainstream technologies. Requires the maintenance of data management technology information in the Technical Reference Model/Standards Profile. Need criteria and an on-going process to assess vendors and products.

3.3 Technology Components

The Data Management components in this domain include:

· Database – Refers to a collection of information organized in such a way that a computer program can quickly select desired pieces of data.

· Modeling – The process of representing entities, data, business logic, and capabilities for aiding in software engineering.

· Utilities – Refers to software tools that address various miscellaneous processes for technology applications and users.

· Other Applications – Refers to software applications that do not fit in any of the other aforementioned software categories.

· Static Display - Static Display consists of the software protocols that are used to create a pre-defined, unchanging graphical interface between the user and the software.

· Data Exchange – Data Exchange is concerned with the sending of data over a communications network and the definition of data communicated from one application to another.

· Database Connectivity - Defines the protocol or method in which an application connects to a data store or data base.

· Reporting and Analysis - Consist of the tools, languages and protocols used to extract data from a data store and process it into useful information.

· Data Format / Classification – Defines the structure of a file.

· Data Types / Validation – Refers to specifications used in identifying and affirming common structures and processing rules.

· Data Transformation - Data Transformation consists of the protocols and languages that change the presentation of data within a graphical user interface or application.

· Service Discovery - Defines the method in which applications, systems or web services are registered and discovered.

The classifications for any products or standards within this domain are:

Life Cycle Definition/

Classifications Meaning

Preferred Product/standard of choice; support available; recommended.

Contained Develop solutions using these standards or products only if there are no suitable alternatives categorized as preferred; if a preferred product is available that will meet the requirements, plans should be developed to move from contained to preferred as soon as practical.

Obsolete Being phased out; (e.g., vendor support ending); plans should be developed to rapidly phase out and replace (often to avoid substantial risks.)

Research Product/standard to be used in conjunction with technology research efforts only (e.g., testing, pilots).

Rejected Product/standard has been evaluated and found not to meet technical architecture needs.

3.3.1 Database

Refers to a collection of information organized in such a way that a computer program can quickly select desired pieces of data. Databases organize data and information into physical structures, which are then accessed and updated through the services of a database management system. Databases may be relational, hierarchical, flat files or any other formal collection of data.

Standards:

Use of British Inter-governmental Group on Geographic Information is classified as Research.

Use of Library of Congress Metadata Encoding & Transmission Standard (METS) is classified as Research.

Use of Common OMG Common Warehouse Metamodel (CMM) Standard is classified as Research.

Use of OMG Common Warehouse Metadata Interchange (CMMI) Standard is classified as Research.

Products:

Use of Oracle (Version > 8.0 ) is classified as Preferred[sda1] .

Use of Microsoft Access (Version > 97) is classified as Preferred.

Use of Microsoft SQL Server (Version > 7.0) is classified as Preferred.

Use of Ingres II is classified as Contained.

Use of Sybase IQ is classified as Contained.

Use of Lotus Domino is classified as Contained.

Use of Personal Oracle Lite is classified as Contained.

Use of R-Base is classified as Contained.

Use of My SQL is classified as Contained.

Use of ESRI Info 8 is classified as Contained.

Use of IBM DB2 is classified as Contained.

Use of IBM UDB is classified as Contained.

Use of Microsoft Foxpro is classified as Contained.

Use of IBM Informix is classified as Contained.

Use of Progress DLC91C is classified as Contained.

Use of PostgreSQL is classified as Contained.

Use of ADABAS is classified as Contained.

Use of Model 204 is classified as Contained.

Use of SAS System 2000 is classified as Contained.

Use of Raining Data PIK is classified as Contained.

Use of FileMaker Pro is classified as Contained.

Use of Corel Parodox is classified as Contained.

Use of dBASE is classified as Contained.

Use of ESRI Arcstorm is classified as Obsolete.

Use of Lotus Approach is classified as Obsolete.

Use of Aspen is classified as Obsolete.

Use of IBM VSAM is classified as Obsolete.

Use of HP Image/SQL is classified as Obsolete.

3.3.2 Modeling

The process of representing entities, data, business logic, and capabilities for aiding in software engineering is referred to as Modeling.

Standards:

Use of Object Management Group (OMG) UML (Version > 1.5) is classified as Research.

Products:

Use of Oracle Designer (Version > 2000) is classified as Preferred.

Use of Popkin System Architect (Version > 9.0) is classified as Preferred.

Use of CA ERWIN (Version > 4.0) is classified as Preferred.

Use of Microsoft Visio is classified as Contained.

Use of CA RPTwin is classified as Contained.

Use of Powerbuilder Power Designer is classified as Contained.

Use of Platinum Model Mart is classified as Contained.

Use of BMC Mainview Predict is classified as Contained.

Use of BMC Mainview Applications Analysis is classified as Contained.

Use of Booze-Allen EAMS is classified as Contained.

Use of Intelliview Visible Analyst is classified as Contained.

Use of Teamwork is classified as Obsolete.

Use of IBM Rational Rose is classified as Research.

3.3.3 Utilities

Generally, Utilities are software tools that address various miscellaneous processes for technology applications and users. More specifically, the utilities identified in this document provide the means to manage data.

Use of Veritas Net Backup (Version >3.4) is classified as Preferred.

Use of Time Navigator is classified as Contained.

Use of Legatto Backup Exec is classified as Contained.

Use of ESRI Arcserve is classified as Contained.

Use of Budtools is classified as Contained.

Use of Quest TOAD is classified as Contained.

Use of Shavelik HF Net Check is classified as Contained.

Use of Bindview Net Inventory is classified as Contained.

Use of HP Adager is classified as Obsolete.

Use of HP VE Soft is classified as Obsolete.

Use of Altova XML Spy is classified as Research.

Use of Syncsort is classified as Research.

Use of Redhat Cygwin is classified as Research.

3.3.4 Other Applications

Other Applications refers to software products that do not fit in any of the other aforementioned software categories but also are used in conjunction with data management processes. Applications in this category perform a wide range of data management functions and should not be compared to each other for classification.

Use of Intergraph Spatial Metadata Management System (Version > 3.2) is classified as Preferred.

Use of Oracle Warehouse Builder Metadata Manager is classified as Preferred.

Use of Microsoft Visual Intercept is classified as Preferred.

Use of Insightful S-Plus is classified as Contained.

Use of TKG2 is classified as Contained.

Use of Litteral DBBrowse is classified as Contained.

Use of Earthware is classified as Research.

Use of CA Advantage Repository is classified as Research.

Use of Blue Angel Enterprise is classified as Research.

Use of Compusalt Meta Manager is classified as Research.

Use of FGDC Toolsuite is classified as Research.

Use of Innovative Interfaces is classified as Research.

Use of Native DBMS Meta Data Management Tools is classified as Research.

Use of Versata is classified as Research.

Use of Microsoft Repository / Rationale is classified as Research.

3.3.5 Static Display

Static Display consists of the software protocols that are used to create a pre-defined, unchanging graphical interface between the user and the software.

Use of HTML is classified as Preferred.

Use of XHTML is classified as Preferred.

Use of pdf is classified as Preferred.

Use of doc is classified as Preferred.

Use of ASCII Text is classified as Preferred.

Use of Unicode Text is classified as Preferred.

3.3.6 Data Exchange

Data Exchange is the format by which not-graphical data is exchanged over a communications network and the definition of data communicated from one application to another.

Use of W3C SOAP is classified as Preferred.

Use of ANSI X12 EDI is classified as Preferred.

Use of OMG XMI is classified as Research.

Use of OASIS ebXML is classified as Research.

3.3.7 Database Connectivity

Database Connectivity defines the protocol or method in which an application connects to a data store or data base.

Standards:

Use of JDBC (Version > 2.0) is classified as Preferred.

Use of ODBC is classified as Preferred.

Use of Net 8 is classified as Preferred.

Use of SDE Connects is classified as Contained.

Use of OLE-DB is classified as Contained.

Use of DAO is classified as Contained.

Use of ADO and ADO.Net is classified as Research.

Products:

Use of SAG Entire Connection is classified as Contained.

Use of CA Ingres Net is classified as Contained.

Use of Oracle Gateway is classified as Contained.

Use of Minisoft Terminal Services is classified as Obsolete.

Use of SQL Net is classified as Obsolete.

3.3.8 Reporting and Analysis

Reporting and Analysis consist of the tools, languages and protocols used to extract data from a data store and process it into useful information.

Standards:

Use of JOLAP is classified as Preferred.

Use of OLAP is classified as Preferred.

Products:

Use of Seagate Crystal Reports is classified as Preferred.

Use of Net IQ Webtrends is classified as Preferred.

Use of Oracle Reports, SQL Plus is classified as Preferred.

Use of Brio Suite is classified as Preferred.

Use of Cognos Suite is classified as Preferred.

Use of Actuate is classified as Preferred.

Use of Access (Version > 97) is classified as Preferred.

Use of Sybase Interactive Query is classified as Contained.

Use of SAG Super Natural is classified as Contained.

Use of Mobius Infopac is classified as Contained.

Use of PC Focus is classified as Obsolete.

Use of Visual Mining Netcharts Server is classified as Research.

Use of Netkey Basic Creator for Touch Screens is classified as Research.

3.3.9 Data Format / Classification

Data Format/Classification defines the structure of a file. There are well over 500 data formats and classifications in existence. For the purpose of this document, only preferred formats and classifications are listed.

Standards:

Use of Geography Markup Language (GML) is classified as Preferred.

Use of ASCII Text is classified as Preferred.

Use of Unicode is classified as Preferred.

Use of HTML is classified as Preferred.

Use of XHTML is classified as Preferred.

Use of PDF is classified as Preferred.

Use of XML is classified as Preferred.

Use of Oracle Import/Export is classified as Preferred.

Use of Microsoft SQL Import/Export is classified as Preferred.

Use of HDF image format is classified as Preferred.

Use of GeoTiff image format is classified as Preferred.

Use of PGM image format is classified as Preferred.

Use of HVHR image format is classified as Preferred.

Use of JPG image format is classified as Preferred.

Use of GIF image format is classified as Preferred.

Use of TIFF image format is classified as Preferred.

Use of PNG image format is classified as Preferred.

Use of SVG image format is classified as Preferred.

Use of BMP image format is classified as Preferred.

Use of Comma Separated Values (CSV) text format is classified as Preferred.

Products:

Use of ESRI ARCexport file (Version > 7.0) is classified as Preferred.

Use of ESRI ARC XML is classified as Preferred.

Use of ESRI Shape (Version > 8.0) is classified as Preferred.

3.3.10 Data Types / Validation

Data Types / Validation refers to specifications used in identifying and affirming common structures and processing rules.

Standards:

Use of W3C Data Type Definition (DTD) is classified as Preferred.

Use of W3C XML Schema is classified as Preferred.

Use of Simple API for XML (SAX) is classified as Preferred.

Use of Document Object Model (DOM) is classified as Preferred.

Products:

Use of ESRI Metadata Parser and Validator is classified as Preferred.

3.3.11 Data Transformation

Data Transformation consists of the protocols and languages that change the presentation of data within a graphical user interface or application.

Standards:

Use of W3C XPATH is classified as Preferred.

Use of W3C XSLT (Version > 2.0) is classified as Research.

Products:

Use of ESRI ARCGIS is classified as Preferred.

Use of ESRI ARCIMS is classified as Preferred.

3.3.12 Service Discovery

Service Discovery defines the method in which applications, systems or web services are registered and discovered.

Use of OASIS Universal Data Description Interface (UDDI) is classified as Preferred.

3.4 Select Best Practices

The Domain Principles, because they are derived from Interior's business direction and strategies, provide the primary direction and guidance around technology decisions within this domain. Additional benefit may sometimes be obtained by reviewing Select Best Practices. These reflect the valuable insights from either domain team members’ experiences or other public sector organizations.

SRM Focused

Select

Best Practice 1: Classify Data Sensitivity- Establish and use a consistent process to classify the sensitivity of all data and information as a basis for ensuring the security, privacy and confidentiality of Interior's data and information assets.

Select

Best Practice 2: Data Stewardship Roles- Establish an Interior data stewardship program with clearly defined roles and responsibilities.

Select

Best Practice 3: Data Standards Process- Establish and follow a consistent standard & process for defining, maintaining and archiving Interior data.

Select

Best Practice 4: Data Exchange Protocols- Establish a process for determining data exchange protocols and identify the protocols to be used across Interior.

Select

Best Practice 5: Metadata Definitions- Establish and follow a consistent process for determining and maintaining metadata definitions. Seek guidance from FGDC and Bureau Data Stewards.

Select

Best Practice 6: Adopt Standards - Adopt existing national / international standards based on OMB Circular A-16, A-119, and A-130.

Select

Best Practice 7: Corporate Metadata - Describe all databases in the Corporate Metadata Repository.

Select

Best Practice 8: Reuse to Facilitate Sharing - Reuse data models and data sets to facilitate sharing of Data across the Department and with business partners.

Select

Best Practice 9: Data Lifecycle - Develop and use a data lifecycle process to promote the release of Interior data to the public in a timely fashion.

Select

Best Practice 10: Metadata Integration - Integrate metadata into all data management processes not only as a documentation tool but also as a dynamic reference for all applications that access or update data.

Select

Best Practice 11: Data Cleansing - Develop and use consistent data cleansing rules. Data Dictionaries and Data Models should assist in describing how the data should be cleansed. Knowledge of data dependencies, constraints, data types etc. is important.

Select

Best Practice 12: Minimize Duplication - Minimize data duplication by identifying and using authoritative data sources. Consult Data Management Steering Committee for guidance on availability and identification of authoritative data sources.

Select

Best Practice 13: Backup & Recovery - Develop and document back-up and recovery procedures to support the published Continuity of Operations Plans (COOP).

Select

Best Practice 14: Data Version Control - Develop Enterprise standards for time stamp (version) control of data to enable near-real-time data recovery.

Select

Best Practice 15: Authoritative Sources - Use authoritative data sources when developing data marts and data warehouses.

Select

Best Practice 16: Business Rules - Design new Enterprise applications employing rules based engines instead of embedding business rules into the application.

Select

Best Practice 17: Data Quality - Data Stewards are responsible for monitoring the quality of the data in their repository.

Select

Best Practice 18: Business Rules for Data - Data Stewards are responsible for determining the business rules for the data in their repository.

3.5 Contributors

The quality of the Interior-wide guidance provided within this TRM chapter is a reflection of the efforts of the Data Management Architecture team. The members of the team are:

Organization Name

Bureau of Reclamation Gary Hardman

Minerals Management Service Joe Chetodal

Minerals Management Service Gwendolyn Young

National Business Center Richard Sullivan

National Park Service Lance Gridley

Office of Surface Mining Donna Hale

US Fish and Wildlife Service Barb White

US Geological Survey Raymond Obuch

Bureau of Land Mgmt. Melanie Rhinehart

Bureau of Land Mgmt. Stephen Adams