SAE GEIA-859A-2012 Data Management (Formerly TechAmerica GEIA-859 REV A).pdf

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4、0 (outside USA) Fax: 724-776-0790 Email: CustomerServicesae.org SAE WEB ADDRESS: http:/www.sae.org SAE values your input. To provide feedback on this Technical Report, please visit http:/www.sae.org/technical/standards/GEIA859A TECHNICAL REPORT GEIA-859 REV. A Issued 2004-08 Revised 2012-04 Data Man

5、agement NOTICE This document has been taken directly from the original TechAmerica document and contains only minor editorial and format changes required to bring it into conformance with the publishing requirements of SAE Technical Standards. The release of this document is intended to replace the

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13、user of this Standard to establish appropriate safety and health practices and to determine the applicability of regulatory limitations before its use. (Formulated under EIA Project PN 4888 and the cognizance of the G-33 Data and Configuration Management Committee). Published by 2012 TechAmerica Sta

14、ndards they establish what high quality DM lookslike. For each principles there is a set of enablers; theenablers provide the mechanisms of DM.Custodians: Adopting Activity:Other - SE(Project SESS-2006-007)Army - AMNavy - ASAir Force - 10NOTE: The activities above were interested in this document as

15、 ofthe date of this document. Since organizations andresponsibilities can change, you should verify the currency ofthe information above using the ASSIST Online database athttp:/assist.daps.dla.mil_AREA SESSDISTRIBUTION STATEMENT A: Approved for public release; distributionis unlimited.This page int

16、entionally left blank.GEIA-859 Revision A i Contents 1 Introduction . 1 1.1 Scope 1 1.2 Overview 2 1.3 Terminology . 3 1.4 References . 4 2 Data Management Principles and their Enablers . 5 2.1 Principle 1: Define the Enterprise Relevant Scope of Data Management . 5 2.1.1 Policy Framework 6 2.1.2 In

17、formation Authority . 8 2.1.3 Policy Steward . 8 2.1.4 Compliance Steward . 9 2.2 Principle 2: Plan for, Acquire, and Provide Data in Response to Customer Requirements . 10 2.2.1 Introduction 10 2.2.2 Enabler 2-1: Establish General Requirements for Data 11 2.2.3 Enabler 2-2: Develop Data Strategy an

18、d Data Concept of Operations . 12 2.2.4 Enabler 2-3: Determine Specific Data Requirements . 13 2.2.4.1 Determine the Needs for Data . 13 2.2.4.2 Identify the Users of the Data and Establish the Parameters that Trigger Data Delivery 15 2.2.4.3 Relate Data Requirements to the Functional Areas Responsi

19、ble for Data Generation and Distribution . 15 2.2.5 Enabler 2-4: Perform Risk Analysis . 16 2.2.6 Enabler 2-5: Authenticate Data Requirements 18 2.2.7 Enabler 2-6: Contract for Data . 18 2.3 Principle 3: Develop DM Processes to Fit the Context and Business Environment in Which They Will Be Performed

20、 20 2.3.1 Introduction 20 2.3.2 Enabler 3-1: Determine the Complete Set of Requirements that the DM Solution Must Address 21 2.3.3 Enabler 3-2: Determine the Characteristics of the DM Solution 23 2.3.4 Enabler 3-3: Compare the Proposed, Best Solution to Existing and Planned Enterprise Capability (In

21、frastructure and Processes) . 25 2.3.5 Enabler 3-4: Make Needed Adjustments in Enterprise-Wide Processes, Practices, Policy, Organizational Alignment, and Infrastructure 26 2.4 Principle 4: Identify Data Products and Views So Their Requirements and Attributes Can Be Controlled 28 GEIA-859 Revision A

22、 ii 2.4.1 Introduction 28 2.4.2 Enabler 4-1: Develop Consistent Methods for Describing Data 29 2.4.2.1 Ensure Data Interoperability Among Team Members . 30 2.4.2.2 Apply Processes to Characterize Data and Data Products to Ensure Adequacy and Consistency . 30 2.4.3 Enabler 4-2: Establish Relevant Att

23、ributes to Refer to and Define Data . 32 2.4.4 Enabler 4-3: Assign Identifying Information to Distinguish Similar or Related Data Products from Each Other . 33 2.5 Principle 5: Control Data, Data Products, Data Views, and Metadata Using Approved Change Control Processes 35 2.5.1 Introduction 35 2.5.

24、2 Enabler 5-1: Control the Integrity of Data, Data Elements, Data Structures, and Data Views . 36 2.5.2.1 Establish a Change Control Process that Imposes the Appropriate Level of Review and Approval 37 2.5.2.2 Provide a Systematic Review of Proposed Changes within the Change Process 38 2.5.2.3 Deter

25、mine the Impact of Change to Include Associated Products, Data, Data Elements, Data Structures, and Data Views 39 2.5.2.4 Gain Approval or Disapproval of Changes to Data, Data Elements, Data Structures, and Data Views (Data Products) by a Designated Approval Authority . 40 2.5.3 Enabler 5-2: Establi

26、sh and Maintain a Status Accounting Process, Reporting Tool, and Mechanism 41 2.5.4 Enabler 5-3: Establish and Maintain an Internal Validation Mechanism . 42 2.6 Principle 6: Establish and Maintain a Management Process for Intellectual Property, Proprietary Information, and Competition-Sensitive Dat

27、a 44 2.6.1 Introduction 44 2.6.2 Enabler 6-1: Establish and Maintain a Process for Data Access and Distribution 45 2.6.2.1 Define Access Requirements 46 2.6.2.2 Ensure Entitlement to Access and Use of Data Is Validated and Documented by the Proper Authority . 46 2.6.3 Enabler 6-2: Establish and Main

28、tain an Identification Process for IP, Proprietary Information, and Competition-Sensitive Data 47 2.6.3.1 Distinguish Contractually Deliverable Data . 47 2.6.3.2 Establish and Maintain Identification Methods 48 2.6.3.3 Establish and Maintain Tracking Mechanisms for Identification of Data . 48 2.6.3.

29、4 Ensure Compliance with Marking Conventions and Requirements 49 GEIA-859 Revision A iii 2.6.4 Enabler 6-3: Establish and Maintain an Effective Data Control Process 49 2.6.4.1 Establish and Maintain Control Methods . 49 2.6.4.2 Establish Mechanisms for Tracking and Determining Status of Data . 49 2.

30、7 Principle 7: Retain Data Commensurate with Value 51 2.7.1 Introduction 51 2.7.2 Enabler 7-1: Plan to Ensure Data Are Available to Meet Future Needs . 51 2.7.3 Enabler 7-2: Maintain Data Assets and an Index of Enterprise Data Assets 53 2.7.4 Enabler 7-3: Assess the Current and Potential Future Valu

31、e of Enterprise Data Holdings 55 2.7.5 Enabler 7-4: Dispose of Data 56 2.7.6 Enabler 7-5: Legal Hold . 57 2.8 Principle 8: Continuously Improve Data Management 58 2.8.1 Introduction 58 2.8.2 Enabler 8-1: Recognize the Need to Continuously Improve the Quality of Data 58 2.8.3 Enabler 8-2: Establish a

32、nd Maintain a Metric Process and Reporting Strategy . 58 2.8.4 Enabler 8-3: Monitor the Quality of Data to Improve Data and Processes . 60 2.8.5 Enabler 8-4: Improve Data Management Through a Systematic and Self-Diagnostic Process 60 2.8.6 Enabler 8-5: Establish the Necessary Tools and Infrastructur

33、e to Support the Process and Assess the Results . 61 2.9 Principle 9: Effectively Integrate Data Management and Knowledge Management 63 2.9.1 Introduction 63 2.9.2 Enabler 9-1: Establish the Relationship Between Data Management and Knowledge Management 63 2.9.3 Enabler 9-2: Cooperate with Knowledge

34、Management Where DM and KM Intersect as KM Methods Develop . 64 2.9.3.1 Understand the State of KM in the Enterprise . 65 2.9.3.2 Coordinate DM and KM Efforts . 65 3 Application Notes . 66 Annex A Contributors to GEIA-859 67 Annex B GEIA-859 Glossary 69 Annex C Data Management Functional Skill Table

35、 74 Annex D Non-Commercial Practices for Data Management Introduction 75 GEIA-859 Revision A iv List of Figures Figure 1 Data Management Principles . 2 Figure 2 Program Level Bill of Information . 4 Figure 3 Contractual Data Management Model . 10 Figure 4 Principle 2 Enablers . 11 Figure 5 Data Envi

36、ronmental Assessment . 12 Figure 6 Review Project Life Cycle to Identify Data Requirements and Determine the Needs for Data 14 Figure 7 Identify Users of the Data Products and Establish When Data Will Be Needed 15 Figure 8 Relate Data Requirements to the Functional Areas Responsible for Generating t

37、he Data 16 Figure 9 Example Risk Portrayal 17 Figure 10 DM Requirements 20 Figure 11 Process for Understanding Requirements . 21 Figure 12 Process for Determining the Characteristics of the DM Solution . 23 Figure 13 Process for Comparing Proposed Solution to Existing and Planned Enterprise Capabili

38、ty 25 Figure 14 Process for Making Needed Adjustments in Processes, Practices, Policies, Enterprise, and Infrastructure . 26 Figure 15 Data Product Identification Enables the Control of Requirements and Attributes 28 Figure 16 Process for Consistently Describing Data 30 Figure 17 Develop a Process f

39、or Selecting Attributes 32 Figure 18 Assign Identifying Information to Distinguish Among Similar Data Products . 33 Figure 19 Establishing Control . 36 Figure 20 Example Change Control Process . 38 Figure 21 Maintenance of Metadata for Project Use in a Status Accounting Database 42 Figure 22 Validat

40、ion of Status Accounting Data and Stored Data to Ensure Integrity . 43 Figure 23 Principle 6 Flow Diagram . 44 Figure 24 Process for Managing Data Access to Intellectual Property, Proprietary Information, and Competition-Sensitive Data . 45 Figure 25 Process for Identifying, Controlling, Tracking, a

41、nd Protecting Intellectual Property, Proprietary Information, and Competition-Sensitive Data 47 Figure 26 Planning Decision Tree for Data of Sustained Value . 51 Figure 27 Improving Data Management . 58 Figure 28 Process and Reporting Strategy 59 Figure 29 Monitoring Data Quality 60 Figure 30 Improv

42、ement Strategy 60 Figure 31 Self-Diagnostic Process . 61 Figure 32 Development of Objective Evidence of Improvement 61 GEIA-859 Revision A v Figure 33 Process to Establish Tools and Infrastructure to Support the Process and Assess Results 61 Figure 34 Understanding the Interdependence of DM and KM 6

43、3 List of Tables Table 1 Types of Data 1 Table 2 Common Functions of Traditional Data Management . 5 Table 3 Creation and Acquisition of Data . 21 Table 4 Responsibility for Updating and Disposing of Data . 22 Table 5 Interdependent Requirements . 24 Table 6 Metadata Examples . 31 Table 7 Example El

44、ements of Database Functionality . 42 Table 8 Representative Refresh and Migration Intervals . 54 Table 9 Examples of Data Management Metrics . 59 Table 10 Relationship Between Data and Knowledge . 63 GEIA-859 Revision A vi Foreword Data management (DM) has evolved significantly in the past decade a

45、nd this standard has played a significant role toward that progress. The scope of data management has also evolved, correspondingly. DM is now considered a valued and critical partner in enterprise architecture design and application, and has found a strong, meaningful, and acknowledged role with in

46、formation technology. The second release of this standard has been undertaken in order to provide updates that reflect significant progress and new understandings in the processes, scope, and application of data management. No matter how far or fast the evolution is, or how broad the acceptance is f

47、or including data management in the information definition and exchange processes, the critical first step in managing data is to apply the traditional functions and processes within data management. Without the right data in the system in the first place, the outcomes will be disappointing, no matt

48、er what level of technological innovation, planning or rigor is applied. The identification, definition, preparation, control, archiving, and disposition of data all require a sizable investment in labor, supporting systems, and time. The purpose behind enacting consistent, high-quality data managem

49、ent is to make certain that the enterprise reaps a return on this investment. DM applies effective processes and tools to acquire and provide stewardship for data. A well-designed DM process ensures that the enterprise and/or customers receive the data they need when they need it, in the form they need, and of the