In 2023, the Interstate Natural Gas Association of America Foundation (INGAA) sponsored a Joint Industry Project to develop and publish two documents: 1) Framework for Geohazard Management and 2) Recommended Practice for Pipeline Integrity Management of Landslide Hazards. Both papers identify data management as a key component to a successful pipeline Geohazards Management Program (GMP). This paper expands upon the INGAA publications to provide a more comprehensive overview of geohazard data management best practices, examples of the importance of data management in a GMP, and additional guidance on key components and methods. The importance of data management in the success of a GMP can often be overlooked or undervalued. Data are central to the outcomes of a GMP in that they provide the basis for nearly all decision-making. Data help Operators determine where threats are located, the nature of the threats, the types of responses that are warranted, and whether additional data are needed. Unnecessary risks and uncertainty develop where data are not properly or consistently compiled or filed, resulting in potential for misunderstanding and mismanagement of threats, over- or under-responding to threats, or costly or unnecessary work. Data pertaining to geohazard management are generated and collected throughout the life cycle of a pipeline, from a variety of sources and in a variety of formats, which can present challenges to development and maintenance of comprehensive repositories for relevant data. However, it is imperative that critical data are not only maintained, but also organized such that they can be rapidly accessed and analyzed when needed, for either emergent local concerns or for a system-wide review and assessment. A variety of approaches and platforms are available and used by Operators to manage data. Platform and technology choices are typically driven by available resources such as internal and external hardware, software, and personnel. There are numerous configurations that can accomplish critical key objectives, including: publicly available software to proprietary solutions; local to cloud-based data storage; and small to large internal and/or external teams of data managers, data generators, and data users. This paper does not seek to provide a specific roadmap to data management. Instead, it seeks to layout the core components of data management that are essential for a GMP and to define the objectives of each. An example of an ideal baseline data management system will be presented, based on real-world Operator systems.
