GEIA Standard
Data Management
GEIA- 859
This Standard Was Developed under EIA Project PN 4888
Contents
Foreword vii
Introduction 1
Scope 1
Overview 2
Terminology 3
References 4
1.0 Principle: Define the Enterprise Relevant Scope of Data Management 5
2.0 Principle: Plan for, Acquire, and Provide Data Responsive to Customer Requirements 109
Introduction 109
2.1 Enabler: Establish General Requirements for Data 1110
2.2 Enabler: Develop Data Strategy and Data Concept of Operations 1211
2.3 Enabler: Determine Specific Data Requirements 1312
2.3.1 Determine the Needs for Data 1312
2.3.2 Identify the Users of the Data and Establish the Frequency of Data Delivery 1615
2.3.3 Relate Data Requirements to the Functional Areas Responsible for Data Generation and Distribution 1615
2.4 Enabler: Perform Risk Analysis 1716
2.5 Enabler: Authenticate Data Requirements 1918
2.6 Enabler: Contract for Data 2018
3.0 Principle: Develop DM Processes to Fit the Context and Business Environment in Which They Will Be Performed 2220
Introduction 2220
3.1 Enabler: Determine the Complete Set of Requirements that the DM Solution Must Address 2321
3.2 Enabler: Determine the Shape of the DM Solution 2523
3.3 Enabler: Compare the Proposed, Best Solution to Existing and Planned Enterprise Capability (Infrastructure and Processes) 2826
3.4 Enabler: Make Needed Adjustments in Processes, Practices, Policy, Organizational Alignment, and Infrastructure 2927
4.0 Principle: Identify Data Products and Views So Their Requirements and Attributes Can Be Controlled 3129
Introduction 3129
4.1 Enabler: Develop Consistent Methods for Describing Data 3230
4.1.1 Ensure Data Interoperability Among Team Members 3331
4.1.2 Apply Processes to Characterize Data and Data Products to Ensure Adequacy and Consistency 3331
4.2 Enabler: Establish Relevant Attributes to Refer to and Define Data 3532
4.3 Enabler: Assign Identifying Information to Distinguish Similar or Related Data Products from Each Other 3634
5.0 Principle: Control Data, Data Products, Data Views, and Metadata Using Approved Change Control Processes 3836
Introduction 3836
5.1 Enabler: Control the Integrity of Data, Data Elements, Data Structures, and Data Views 4037
5.1.1 Establish a Change Control Process that Imposes the Appropriate Level of Review and Approval 4138
5.1.2 Provide a Systematic Review of Proposed Changes within the Change Process 4239
5.1.3 Determine the Impact of Change to Include Associated Products, Data, Data Elements, Data Structures, and Data Views 4340
5.1.4 Gain Approval or Disapproval of Changes to Data, Data Elements, Data Structures, and Data Views (Data Products) by a Designated Approval Authority 4441
5.2 Enabler: Establish and Maintain a Status Accounting Process, Reporting Tool, and Mechanism 4442
5.3 Enabler: Establish and Maintain an Internal Validation Mechanism 4644
6.0 Principle: Establish and Maintain a Management Process for Intellectual Property, Proprietary Information, and Competition-Sensitive Data 4846
Introduction 4846
6.1 Enabler: Establish and Maintain a Process for Data Access and Distribution 4947
6.1.1 Define Access Requirements 5048
6.1.2 Ensure Entitlement to Access and Use of Data Is Validated and Documented by the Proper Authority 5149
6.2 Enabler: Establish and Maintain an Identification Process for IP, Proprietary Information, and Competition-Sensitive Data 5250
6.2.1 Distinguish Contractually Deliverable Data 5250
6.2.2 Establish and Maintain Identification Methods 5350
6.2.3 Establish and Maintain Tracking Mechanisms for Identification of Data 5351
6.2.4 Ensure Compliance with Marking Conventions and Requirements 5451
6.3 Enabler: Establish and Maintain an Effective Data Control Process 5452
6.3.1 Establish and Maintain Control Methods 5452
6.3.2 Establish Mechanisms for Tracking and Determining Status of Data 5552
7.0 Principle: Retain Data Commensurate with Value 5653
Introduction 5653
7.1 Plan to Ensure Data Are Available When Later Needed 5753
7.2 Maintain Data Assets and an Index of Enterprise Data Assets 5855
7.3 Assess the Current and Potential Future Value of Enterprise Data Holdings 6157
7.4 Dispose of Data 6259
8.0 Principle: Continuously Improve Data Management 6360
Introduction 6360
8.1 Enabler: Recognize the Need to Continuously Improve the Quality of Data 6360
8.2 Enabler: Establish and Maintain a Metric Process and Reporting Strategy 6461
8.3 Enabler: Monitor the Quality of Data to Improve Data and Processes 6562
8.4 Enabler: Improve Data Management Through a Systematic and Self-Diagnostic Process 6663
8.5 Enabler: Establish the Necessary Tools and Infrastructure to Support the Process and Assess the Results 6764
9.0 Principle: Effectively Integrate Data Management and Knowledge Management 6965
Introduction 6965
9.1 Enabler: Establish the Relationship Between Data Management and Knowledge Management 6965
9.2 Enabler: Cooperate with Knowledge Management Where DM and KM Intersect as KM Methods Develop 6966
9.2.1 Understand the State of KM in the Enterprise 6967
9.2.2 Coordinate DM and KM Efforts 6967
10.0 Application Notes 7068
List of Figures
Figure 1. Data Management Principles 2
Figure 21. Contemporary Data Management Model 109
Figure 22. Principle 2 Enablers—— 1110
Figure 23. Data Environmental Assessment 1211
Figure 24. Review Project Life Cycle to Identify Data Requirements and Determine the Needs for Data 1514
Figure 25. Identify Users of the Data Products and Establish When Data Will Be Needed 1615
Figure 26. Relate Data Requirements to the Functional Areas Responsible for Generating the Data 1716
Figure 27. Example Risk Portrayal 1917
Figure 31. DM Requirements 2220
Figure 32. Process for Understanding Requirements 2321
Figure 33. Process for Determining the Shape of the DM Solution 2624
Figure 34. Process for Comparing Proposed Solution to Existing and Planned Enterprise Capability 2927
Figure 35. Process for Making Needed Adjustments in Processes, Practices, Policies, Enterprise, and Infrastructure 3028
Figure 41. Data Product Identification Enables the Control of Requirements and Attributes 3129
Figure 42. Process for Consistently Describing Data 3330
Figure 43. Develop a Process for Selecting Attributes 3533
Figure 44. Assign Identifying Information to Distinguish Among Similar Data Products 3734
Figure 51. Establishing Control 4037
Figure 52. Example Change Control Process 4139
Figure 53. Maintenance of Metadata for Project Use in a Status Accounting Database 4643
Figure 54. Validation of Status Accounting Data and Stored Data to Ensure Integrity 4744
Figure 61. Principle 6 Flow Diagram 4846
Figure 62. Process for Managing Data Access to Intellectual Property, Proprietary Information, and Competition-Sensitive Data 5048
Figure 63. Process for Identifying, Controlling, Tracking, and Protecting Intellectual Property, Proprietary Information, and Competition-Sensitive Data 5250
Figure 71. Planning Decision Tree for Data of Sustained Value 5653
Figure 81. Improving Data Management 6360
Figure 82. Process and Reporting Strategy 6461
Figure 83. Monitoring Data Quality 6562
Figure 84. Improvement Strategy 6562
Figure 85. Self-Diagnostic Process 6663
Figure 86. Development of Objective Evidence of Improvement 6663
Figure 87. Process to Establish Tools and Infrastructure to Support the Process and Assess Results 6764
Figure 91. Understanding the Interdependence of DM and KM 6965
List of Tables
Table 1. Types of Data 1
Table 1-1. Common Functions of Traditional Data Management 5
Table 12. Overview of Data Management Tasks, Subtasks, and Needed Skills 7
Table 31. Creation and Acquisition of Data 2422
Table 32. Responsibility for Updating and Disposing of Data 2422
Table 33. Interdependent Requirements 2725
Table 41. Metadata Examples 3432
Table 51. Example Elements of Database Functionality 4644
Table 71. Representative Refresh and Migration Intervals 6057
Table 81. Examples of Data Management Metrics 6461
Table 91. Relationship Between Data and Knowledge 6965
Foreword
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 management (DM) 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 customers receive the data they need when they need it, in the form they need, and of requisite quality.
When DM principles are applied using effective practices, the return on the investment in data is maximized and product life-cycle costs are reduced. This standard is intended to be used when establishing, performing, or evaluating DM processes in any industry, business enterprise, or governmental enterprise.
This standard describes DM principles and methods using a neutral DM terminology. Sections 1 through 9899 8 are normative. Annexes are informative.
The methods of DM have undergone significant changes as paper documents transitioned to digital data and continue to evolve. As a result, many policies, manuals, and instructions for DM, which mostly addressed DM for defense products, became obsolete; they described procedures that were adapted to efficient paper-based management of paper deliverables. This standard is intended to articulate contemporarycontemporarycapture current DM principles and methods that are broadly applicable to management of electronic and non-electronic data in both the commercial and government sectors.
The standard is intended to introduce the basic principles of Data Management, provide an introduction to the enablers for each principle, and to introduce what may constitute some new concepts and tasks associated with the management of data. GEIA-HB-859 is the associated manual which contains how-to information for implementation of this standard in a variety of environments.
Development of this standard began in August 2000 when the Electronic Industries Alliance’s (EIA) G-33 Committee on Data and Configuration Management initiated task PN 4888 to develop a consensus standard for data management. This is the firstsecond release of the standard. Contributors to this standard are identified in Annex A.
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1
Introduction
Scope
Data is information (e.g., concepts, thoughts, opinions) that has been recorded in a form that is convenient to move or process. Data can bemay representbe tables of values of various types (numbers, characters, and so on). Data can also take more complex forms such as engineering drawings and other documents, software, pictures, maps, sound, and animation.
For the purposes of this standard, commercial and governmentmany enterprises concern themselves with three broad types of data. Table 1 lists them, indicates how each is used, and provides examples.
Table 1. Types of DataType / Usage / Examples
Product / Collaboration / Cost, schedule, and performance data
Scientific data such as written notes and observation of phenomena
Engineering drawings and models, parts catalogs, software applications and their components, operational and maintenance instructions, and training materials
Business / Collaboration / Plans and schedules, financial information, software applications, inventory status, medical, facility, vacancy and human resource information
Operational / Transactional records exchange / Orders, issues, receipts, bills of lading, usage data and invoices
Data management, from the perspective of this standard, consists of the disciplined processes and systems that plan for, acquire, and provide stewardship for product and product-related business data, consistent with requirements, throughout the product and data life cycles. Thus, this standard primarily addresses product data and the business data intrinsic required for to collaboration during product acquisition and sustainment. It is recognized, however, that the principles articulated described in this standard also have broader application to business data and operational data generally. It is also recognized that the data addressed by this standard is subject to data administration, metadata management, records management, and other processes applied at the enterprise level, and that these principles must be applied in that enterprise context.
Data has many purposes, including stating requirements, providing proof of achievement, establishing a basis for long-term product support, and many others. Deliverable data (customer-accessible information) represents only a small fraction of the project data. In general, a vast amount of design, development, fabrication, and manufacturing data remains the intellectual property of the developer/producer. Further, the value of data is not limited to its use in support of a particular specific product: data may have a life cycle longer than that of the product it describes. For instance, data from previous projects forms part of the foundation for new product and process design. Data also supports the enterprise in process redesign and quality. Thus data is essential to competitive position. An enterprise’s data¾if not properly safeguarded¾can also be misused by a competitor to the competitor’s advantage. For these reasons, data is an integral part of an enterprise’s intellectual assets and overall enterprise knowledge.
Overview
This standard hascomprises has nine eight fundamental data management principles (Figure 1). Principles are high-level descriptive statements about describing high-quality DM; they establish what high-quality DM looks like. Each principle has a set of enablers, which provide the mechanisms of DM.
Figure 1. Data Management Principles
Two different viewpoints, corresponding to product and data life cycles, are important to DM. Product data (and related business data) is normally acquired or created as part of the development of a new product or similar initiative. This is the project perspective. Principle 2, which addresses the planning for and acquisition of data, and Principle 4, which deals with the identification of products, views, and related data elements, are written primarily from the perspective of the individual project. The remaining principles apply at both the project and enterprise levels. Principle 9 relates DM to knowledge management (KM).
The degree to which the DM principles in this standard apply to a product varies over the product’s life cycle. Similarly, they vary in applicability over the data life cycle. Some principles may not apply during every phase of either life cycle.
This standard addresses the functions of DM but not how to organize for DM. Each enterprise, for valid reasons, locates the functions of DM within enterprise elements that make sense within its own enterprise environment.
This standard is not intended for use as a compliance document or an evaluation mechanism for DM projects. It is intended for use as a source and reference document for either purpose. Appropriate application of the functions and principles in this standard enables the user to plan and implement a DM program for a product, project, or enterprise.
Terminology
During creation of this standard, significant effort went into using neutral terms wherever possible. Please see the Glossary, (Annex B) for the definition of those terms. Neutral terms used in this standard are provided in the glossary (Annex B). There is no intent to express preference for any particular terminology set. When planning and documenting a DM program, other aliases may be substituted for the neutral terminology. Three particular sets of terms deserve special mention. The first of these is the pair of terms “program” and “project.” In practice, the term “program” is often used to represent an undertaking that is larger in scope than a “project,” but such is not universally the case. This standard consistently uses the term “project.”