This chapter will discuss aviation data governance standards, regulations, and policies. It summarizes the perspectives both aviation and non-aviation governing bodies take on protecting industry data. The chapter also summarizes applicable data governance bodies and the standards they have created that can be implemented or adopted at airports.
There is no one best data standard, as each is derived to accommodate certain data objectives and then evolves alongside the data it standardizes. Compliance with a data standard allows the comingling and analysis of various data sets collected from different sources over time. As information is exchanged and routinely converted in an efficient data interoperability workflow, data in compliance with similar standards can have improved data consistency and quality. Data “crosswalks” are defined to map out similar or related data in different data standards.
Various standards are presented in this chapter and are generally listed in order of relevance to facets of the aviation industry, starting with airports. The information in this chapter is intended to guide airports as they consider and select the standard or standards that align with their specific business needs.
The aviation industry is heavily regulated, and compliance with specific standards is often a legal requirement. By following these standards, the aviation industry can demonstrate their adherence to regulatory requirements, avoiding potential penalties and legal consequences. The aviation organizations that collect and maintain data should choose the standards that are most appropriate for their needs and improve their decision making. The choice of standards will depend on several factors such as the type of data being collected, the purpose of the data, and the budget for data collection and maintenance. Establishing standards for data collection and maintenance is important for several reasons, including the following:
Data and information are generated and used by the FAA to conduct its daily activities across organizations and collaborators at the local level and throughout the globe. Therefore, the FAA issued the Federal Data Strategy–A Framework for Consistency (Order 1375.1F), which discusses the agency’s ethical governance, conscious design, and learning culture that promotes proactive governance and management of data and information (FAA 2021). This order is in alignment with the Open, Public, Electronic, and Necessary (OPEN) Government Data Act.
As the FAA transitions to a more open data environment, the agency’s protection of controlled, private, sensitive, and unclassified critical infrastructure data and information is critical. This order promotes a culture of transparency and ensures data is available and accessible across organizations and business lines. This data transparency will lead to usage of data and information in operations and decision making.
The main purpose of the order is to establish a policy for the following use cases:
This FAA policy and its elements can be a template for airports to develop an internal data governance policy. The policy would include the following headings:
The standards set forth in FAA AC 150/5300-18B (AAS-100 2014) are based on the older versions of the Spatial Data Standards for Facilities, Infrastructure, and Environmental (SDSFIE) and National CAD Standards. The FAA expanded on these standards to better facilitate use at aviation facilities. They tend to focus on exterior (outside the building) features and are typically lacking for interior features.
AC 150/5300-18B provides the specifications and requirements for the collection and submittal of airport data through field and office methodologies in support of the FAA. Additionally, it details the forwarding of safety-critical data to the National Geodetic Survey (NGS) to independently verify and validate the data. This AC is not mandatory. However, the usage of the guidelines is mandatory for the collection of geospatial and aeronautical data funded by federal grant assistance programs (AAS-100 2014).
The development of this guidance and specification is to maximize the level of data collected while minimizing the cost to airports. The collected data under this specification is mainly used to
Airports establish the CAD and BIM standards to communicate with the architectural and engineering communities regarding their design. The FAA documented the agency’s standards and recommendations for airport design under AC 150/5300-13B (AAS-100 2022). This standard is used by airports to
This standard is mandatory for projects funded under certain federal grant assistance programs, such as the Airport Improvement Program (AIP), and this AC is required by regulation for projects funded by the Passenger Facility Charge (PFC) Program.
The standard covers a wide range of sizes and performance characteristics of aircraft planned to operate at an airport. It establishes an acceptable level of safety that ensures optimum operation of the most demanding aircraft type or group of aircraft with similar characteristics independent of individual operational controls that may affect the utility and efficiency of the airport operations.
Airports rely on design manual standards as comprehensive guidelines that document, communicate, and inform their continuous planning, design, and construction initiatives. These documents detail the series of codes, standards, details, products, and practices to be followed by architecture and engineering firms when working on the facilities at the airport.
Currently, most of the design work is done through CAD; however, more airports are adopting the implementation of BIM in their design process. ACRP Research Report 214: BIM Beyond Design Guidebook provides additional information regarding BIM implementation (Ray 2020).
The European Aviation Safety Agency (EASA) expanded its air traffic and air navigation services by adding technical requirements for Aeronautical Information Service (AIS). The EUROCONTROL Guidelines Supporting the Implementation of Aeronautical Information Requirements (AIR) have been developed to support the collaborators originating,
processing, storing, collecting, integrating, or providing aeronautical data and information (EUROCONTROL 2020).
Aeronautical data processes cover the data chain from data origination activities through to making products available by the AIS provider. These guidelines support a data-centric environment, recognizing advancements in automation, with the intention that the quality of the data is achieved and maintained from origination to delivery by the AIS provider through the application of a data assurance process. They provide detailed guidance and are intended to be generic enough to be applied across various platforms and organizations.
The objective of the Aeronautical Information Exchange Model (AIXM) is to provide digitally formatted aeronautical information within the scope of EASA’s AIS. The AIS information and data flows are complex and made up of interconnected systems that involve multiple suppliers and consumers. The AIXM standard defines a data model for exchanging aeronautical information between myriad international aviation data management systems to support global air traffic management. AIXM takes advantage of established information engineering standards and supports current and future aeronautical information system requirements.
The following topics are included in the scope of the AIXM standard:
The International Air Transportation Association (IATA) is the entity that supports the aviation industry with standards for airline safety, security, efficiency, and sustainability. IATA, in 2020 issued an Airport Governance Guide (Reece 2020). The guide includes several best practices that are recommended to be in place at the airports. For example, the Operational Data Governance Program encompasses a multitude of collaborators associated with governing data to monitor and improve the quality of data, leading to improved operational efficiency and capacity utilization of an airport. The program can focus on the following:
The International Organization for Standardization (ISO) Standard for Geographic Information–Metadata (ISO 19115-1) drives the internationalization of data sharing. This standard includes a comprehensive set of metadata terms and definitions that describe digital geospatial data and outlines the characteristic properties of the data being recorded. It provides information about the identification, extent, quality, spatial and temporal schema, spatial reference, and distribution of digital geographic data (ISO 2014). There are many other ISO standards that pertain to geospatial concepts, including two other parts of ISO 19115 (19115-2 for gridded
data set metadata and 19115-3 for schema implementation). The Federal Geographic Data Committee (FGDC) endorsed the International Committee for Information Technology Standards (INCITS)/ISO 19115-1:2014 Geographic Information–Metadata–Part 1: Fundamentals (2014) and INCITS/ISO 19157:2013 (2013) Geographic Information–Data Quality in December 2016. The FGDC website provides links and a list of commonly used GIS-related standards on their website at https://www.fgdc.gov/standards. INCITS has a catalog of their standards at https://www.incits.org/standards-information/catalog-of-standards.
In 2018, the Geospatial Data Act (GDA) was signed into law. GDA was included as a component of the FAA Reauthorization Act. The GDA codifies the committees, processes, and tools used to develop, drive, and manage the National Spatial Data Infrastructure (NSDI). The Act established the FGDC as the lead entity in the executive branch to develop, implement, and review policies, procedures, practices, and standards in relation to geospatial data.
FGDC authored the Content Standard for Digital Geospatial Metadata (CSDGM) to provide a common set of terminology and definitions for the documentation of digital geospatial data. Through this standard, the names of data elements and compound elements are established. This standard supports the collection and processing of geospatial metadata relied on by users internal or external to the organization (NSDI 1998). The main objectives of the CSDGM include
The FGDC Standards Working Group interacts with other federal standardization activities and external standards organizations to ensure the best possible technical foundation for data and web services, consistent with standards policies. Many ISO standards have been adopted by the FGDC, and in some cases, an FGDC standard has been adopted for use by ISO as well. Table 5-1 contains standards applicable to geospatial data governance, and for ease of reference, indicates the number of the ISO Standard.
In addition to the INCITS website, searching for these standards through ISO’s website (ISO.org) or the American National Standards Institute’s (ANSI) website (ANSI.org) for the latest standard will help. The standards are constantly undergoing revision, and not all parts or amendments are captured here.
The NSDI is a body authorized by the FGDC that leverages investments in people, technology, data, and procedures to create and provide the geospatial knowledge required to understand, protect, and promote our national and global interests. Many public, private, and non-profit organizations collect and distribute geospatial data. Most of this data is appropriate for public release and consumption. NSDI provides a place-based framework for connecting public and private data for understanding and decision making. The FGDC develops or adopts geospatial standards for implementing the NSDI, in consultation and cooperation with state, local, and tribal governments, the private sector and academic community, and—to the extent feasible—the international community.
NSDI developed a standard procedure guide (NSDI 2005) to identify sensitive data and information content of geospatial data that pose a risk to security, which is covered in greater detail in Chapter 8.
Table 5-1. FGDC-endorsed standards.
| Standard Number | Standard Name | Release Date |
|---|---|---|
| ISO 19115-1 | Geographic Information—Metadata—Part 1: Fundamentals | 2014 |
| ISO 19115-2 | Geographic Information—Metadata—Part 2: Extensions for Acquisition and Processing | 2019 |
| ISO 19115-3 | Geographic Information—Metadata—Part 3: XML (Extensible Markup Language) Schema Implementation for Fundamental Concepts | 2022 |
| ISO 19157 | Geographic Information—Data Quality | 2013 |
| ISO 19103 | Geographic Information—Conceptual Schema Language | 2014 |
| ISO 19104 | Geographic Information—Terminology | 2016 |
| ISO 19107 | Geographic Information—Spatial Schema | 2019 |
| ISO 19108 | Geographic Information—Temporal Schema | 2002 |
| ISO 19109 | Geographic Information—Rules for Application Schema | 2015 |
| ISO 19110 | Geographic Information—Methodology for Feature Cataloguing | 2016 |
| ISO 19111 | Geographic Information—Referencing by Coordinates | 2019 |
| ISO 19112 | Geographic Information—Spatial Referencing by Geographic Identifiers | 2019 |
| ISO 19113 | Geographic Information—Quality Principles | 2002 |
| ISO 19118 | Geographic Information—Encoding | 2011 |
| ISO 19119 | Geographic Information—Services | 2016 |
| ISO 19123 | Geographic Information—Schema for Coverage Geometry and Functions | 2005 |
| ISO 19123-2 | Geographic Information—Schema for Coverage Geometry and Functions—Part 2: Coverage Implementation Schema | 2018 |
| ISO 19127 | Geographic Information—Geodetic Register | 2019 |
| ISO 19131 | Geographic Information—Data Product Specifications | 2007 |
| ISO 19132 | Geographic Information—Location-Based Services—Reference Model | 2007 |
| ISO 19133 | Geographic Information—Location-Based Services—Tracking and Navigation | 2005 |
| ISO 19134 | Geographic Information—Location-Based Services—Multimodal Routing and Navigation | 2007 |
| ISO 19135 | Geographic Information—Procedures for Item Registration—Part 1: Fundamentals | 2015 |
| ISO 19136 | Geographic Information—Geographic Markup Language (GML)—Part 1: Fundamentals | 2020 |
| ISO 19139 | Geographic Information—XML Schema Implementation—Part 1: Encoding Rules | 2019 |
| ISO 19141 | Geographic Information—Schema for Moving Features | 2008 |
| ISO 19144-1 | Geographic Information—Classification Systems—Part 1: Classification System Structure | 2009 |
| ISO 19144-2 | Geographic Information—Classification Systems—Part 2: Land Cover Meta Language (LCML) | 2012 |
The United States NCS has been developed by the National Institute of Building Sciences (NIBS) to create a unifying CAD standard based on several other international standards. The NCS is based on the American Institute of Architect’s CAD Layer Guidelines, the Construction Specification Institute’s Uniform Drawing System (Modules 1–8), and the NIBS BIM Implementation and Plotting Guidelines. Although there are subtle differences, FAA AC 150/5300-18B (AAS-100 2014) generally follows these standards.
The National BIM Standards–United States (NBIMS–US) Project Committee developed a consensus-based standard that references existing standards. The standard documents information exchanges and delivers the best business practices for the building industry. BIM standards
help build facilities based on detailed data models, and then deliver the building model data used in design for use in commissioning and operation. The BIM data can help maintain asset data throughout the life cycle of the facility. In 2022, NIBS developed an implementation and launch plan for the U.S. National Building Information Management (BIM) Program. The BIM program’s aim is to achieve a new level of industrial efficiency through digitalization.
There is a difference between the NCS and the NBIMS. The NCS addresses traditional drafting used to develop design and construction drawings on paper. NBIMS defines the data standards for electronic exchange of data created and managed for building conception, creation, and facility operation. NCS will continue to be the standard to define design and construction drawing output from the BIM process.
The Construction-Operations Building Information Exchange (COBie) standard defines a data exchange approach to take data from an electronic design file assembled by building designers and contractors and harvest applicable information for loading directly into an electronic asset management system to better maintain assets over their life cycle. Prior to electronic design files and the COBie data exchange standard, new asset information was typically imported manually, requiring time and manual resources.
The Indoor Mapping Data Format (IMDF) was developed by Apple and approved in 2021 by the Open Geospatial Consortium, a voluntary geospatial industry group. The IMDF standard enables an organization to develop map data using standard tools for their facility interiors and has the data displayed in the Apple Maps application so users can view and navigate mapped buildings using a mobile phone. Data collection uses the facilities’ Wi-Fi networks to establish locational accuracy.
The Building Interior Space Data Model (BISDM) goal is to define a standard for GIS-based interior space data. The data model is designed to be inclusive, accommodating common data from various disciplines, including site and architectural design, construction, facilities management, environmental management, and security or emergency preparedness. The BISDM model has been evolving for nearly two decades and what started as an industry-led voluntary committee, is now led by Esri.
Esri has established a set of utility standards to define a networked utility model. To date, utility mode networks have been released for the gas, electrical, telecommunications and water utilities. These templates are preloaded with feature rules, attribute values and advanced functionality for utility tracing, all within an Esri geodatabase. With the FAA’s simplified approach to utilities only containing a few attribute fields, the Esri standards can assist airports that manage their utilities at a higher level of detail.
There are other international and national standards organizations that have defined data standards, not specifically for geospatial data, but whose standard terminology can be used
as attribution. Data developed for other standards and useful for geospatial data attribution includes data such as defining a list of approved material types, establishing valid domain ranges for structure size or capacity, and generally defining appropriate attribute terminology. Several of these standards and organizations that can help standardize data attributes are as follows:
The aerospace industry is undergoing huge growth and rapid digital transformation. The digitization and implementation of tools and technologies has increased the risk of data and information misuse, as well as threats to individuals and organizations such as flight safety and security (ISO/IEC 2018).
The ISO/IEC 27001 standard from ISO and the International Electromechanical Commission (IEC) is a framework that helps organizations manage and protect their data and information so that they remain safe and secure. ISO/IEC 27001 implementation ensures the confidentiality, integrity, and availability of data and information, such as financial data, or sensitive customer information. The standard helps identify the risks and puts appropriate security measures in place to manage them. This standard allows organizations to conduct their work safely and securely. Additionally, it provides business partners with a high level of confidence that data and information is being handled securely.
Through the implementation of this standard, organizations—regardless of size—will be able to manage their data and information to prevent cybersecurity threats, including the following:
IATA has a dedicated board that deals with Passenger and Airport Data Interchange Standards (PADIS). The board develops and maintains electronic data interchange and XML message standards for passenger travel and airport-related passenger service activities. The PADIS standards are released twice a year.
PADIS governs the following:
When it comes to standards airports can reference for implementation, take advantage of existing standards, and consult other airports as they will often share what works well for them and any lessons learned from when things did not go as planned. Adaptation is easier than authoring and is accepted within the industry. AC 150/5300-18B relies upon NCS for formatting layers and is similar to SDSFIE standards for GIS attribution.
FHWA developed and published a comprehensive data governance plan. This plan was created by the FHWA Data Governance Advisory Council (DGAC), which was formed as an advisory council to the FHWA Investment Review Board (IRB) that is chaired by the FHWA chief information officer. FHWA developed this data governance (FHWA 2015) to achieve the following goals:
Tables 5-2 through 5-4 provide details from FHWA’s Data Governance Plan, including goals and objectives.
FHWA developed and adopted several data governance policies to ensure that the data is treated and managed consistently and appropriately throughout the organization, have procedures in place to identify and rectify data management deficiencies, and establish organization-wide data standards. Table 5-3 lists the data governance policies in place at FHWA.
Table 5-2. FHWA data governance goals and objectives (FHWA 2015).
| No. | Goal Title | Goal Description | Objectives |
|---|---|---|---|
| 1 | Leadership | Champion data solutions to ensure accountability and increase the value of data assets. |
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| 2 | Quality | Oversee efforts to provide acceptable quality data that is accurate. |
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| 3 | Prioritization | Prioritize efforts to address data gaps and needs. |
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| 4 | Cooperation | Facilitate cross-organizational collaboration, data sharing, and integration. |
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| 5 | Flexibility | Encourage creative and innovative solutions to data needs. |
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| 6 | Utilization | Improve data utilization and ease of access. |
|
Additionally, FHWA developed data standards to achieve consistency across the organization. These standards ensure that data functionality, uniformity, consistency, flexibility, and integrity are documented. These standards ensure that data being collected have a clear, unique, business requirement. Table 5-4 summarizes data standards.
Further, FHWA follows the National Information Exchange Model (NIEM) standard. This standard is driven by the community and across the government to best approach data and information exchange across platforms and systems.
The Statewide Longitudinal Data System (SLDS) is designed to help districts, schools, and teachers make informed, data-driven decisions to improve student learning. In 2019, SLDS developed “Developing Effective Data Policies and Processes,” a guide (SLDS 2019). The guide describes in detail some of the best practices to consider and include in data governance policies and processes for
Table 5-3. FHWA data governance policies (FHWA 2015).
| No. | Policy Name | Policy Description |
|---|---|---|
| 1 | FHWA data are an enterprise asset | Data, structured and unstructured, and the corresponding metadata, are business technical resources owned in whole or in part by FHWA. FHWA data includes shared data about managed entities, interests, finances, employees, resources, customers, providers, business affiliates, best practices, operating procedures, experimental results, etc. All employees must recognize that the proper management of strategic enterprise data is critical to the success of the organization. |
| 2 | FHWA data programs and activities must undergo IT investment process | FHWA data programs or data-related activities within IT projects require IRB approval prior to and during an ongoing effort. This process is typically initiated, liaised, communicated to IT project managers, or executed by data stewards. They are ultimately responsible for following the FHWA Information Technology Investment Process in order to gain IRB approval prior to and during all planned/ongoing data activities. |
| 3 | FHWA data must be consistent | All strategic FHWA data shall be modeled, named, and defined consistently, according to standards, across the organization. Efforts must be made by management to share data and not maintain redundant data without justification. Originating business stewards of data must recognize the informational needs of downstream processes and business units that may require FHWA data. |
| 4 | FHWA data must be of acceptable quality | Quality data are critical to ensuring FHWA mission success. Data stewards are responsible for ensuring that FHWA data are accurate and correct for the intended purpose and use, and that data providers follow all reporting requirements regarding the collection, processing, and reporting of FHWA data, and meet all requirements of the Data Quality Act. Data quality standards shall be managed and applied actively to the approved reliability levels of FHWA data as defined by the business owners. |
| 5 | FHWA data must be interoperable with dependent systems | All enterprise data (structured and unstructured) must conform to a common set of standards and schema across all data sharing parties. Data sharing must also be accounted for and facilitated through a designated authority. |
| 6 | FHWA data must be maintained at the source | All FHWA data must be maintained as close to the source as feasible, to reduce the collection and storage of redundant data. |
| 7 | Enterprise data must be safe and secured | FHWA data, in all electronic formats, shall be safeguarded and secured based on recorded and approved requirements and compliance guidelines. These requirements are to be determined by the Office of Information Technology Services (OITS). Appropriate backups and disaster recovery measures shall be administered and deployed for all FHWA data. The enterprise data must adhere to the privacy rules and requests made by each respective business steward both internal and external to FHWA. |
| 8 | FHWA data must be accessible | FHWA data, information, and metadata shall be readily accessible to all, except where determined to be restricted. When restrictions are made, business stewards of the data are accountable for defining specific individuals and levels of access privileges that are to be enabled. The OITS will be responsible for the implementation of proper security controls. |
| 9 | Metadata will be recorded and utilized | All FHWA information system development and integration projects will utilize the defined metadata program for data naming, data modeling, and logical and physical database design purposes. The DGAC is responsible for developing plans to capture and record specific data administration-focused metadata consistent with the defined metadata program. |
| 10 | Data stewards will be accountable by job description | Individuals designated as stewards will have specific enterprise data accountabilities incorporated into their job descriptions. |
| 11 | Timeliness of data | Data must be obtained, processed, and be made available in a timeframe consistent with its intended use. |
Table 5-4. FHWA summarization of data standards (FHWA 2015).
| No | Information Characteristic | Characteristic Description |
|---|---|---|
| 1 | Names and Attributes | The variable names and associated attributes must be unique across all systems. The names may be static or determined during system execution run-time. |
| 2 | Container Format | The FHWA content data must be accurately documented to reflect the expected character types, formats, field min/max lengths and all other format specific characteristics. |
| 3 | Content Length | All uniquely defined variables must specify reasonable data length. All mapping variables should in turn conform to the specified content length. |
| 4 | Data Definition Conformity | Data definitions must be established and specified between mapping entities and variables. |
| 5 | Schema Uniformity | All XML/Database schemas developed as the result of a cross-boundary information exchange must be uniform and conform to the developed Information Exchange Packages (IEPs). |
| 6 | Central Metadata Repository | All metadata associated with the information exchange must be stored in an agreed upon central location and accessible to all parties having a business need for access to the data. |
Microsoft’s data governance journey started by taking a top-down approach that was not scalable and sustainable. This approach made managing data take a reactive approach. Microsoft decided to improve on the data governance practice to use automated controls engineered into the process and address root causes of data issues throughout the data life cycle (Microsoft 2022).
Microsoft decided to make the data more accessible, trusted, and connected. Clear data standards were embedded and built within the application development process. Scalable, automated controls for data architecture and life cycle health also were introduced. When developing its data governance strategy, Microsoft had the following goals in mind:
Microsoft provides the following suggestions when implementing a data governance strategy: