C+S December 2020 Vol. 6 Issue 12 (web)

Digital innovation in the built environment is increasingly evident in many areas of project delivery. This includes – but isn’t limited to – automating workflows and processes, enriching practice and service delivery, and implementing various technologies such as unmanned aircraft systems (UAS) for remote geospatial data collection. In general, the engineering and construction industry is slow to adopt many of these digital innovations for a variety of reasons; however, there is growing interest and acute need to understand specific strate- gies that can be implemented to improve safety, accelerate maturity, and improve program and project delivery. These recommended strate- gies focus on data governance, data risk management, effectively using location-enabled technologies, standardization, and data-driven deci- sion support systems. These recommendations are foundational to understanding the relation- ships between the representative digital and physical environments, improving project performance and collaboration, and improving deci- sion making of all stakeholders, including project managers, engineers, constructors, owners, and public agencies. Geospatial Data Governance Since the widespread adoption of the internet and subsequent advance- ments in digital technology for the engineering and construction indus- try, most project activities create, transfer, and repurpose troves of digital data (geospatial and non-geospatial); however, this data is managed ad-hoc (often in duplicate) using personal computers, network file share, removable storage media, and cloud environments, among others. The variety of data locations presents significant challenges for organiza- tions looking to make decisions using data, which has led to an emphasis on addressing key data governance dimensions (see Figure 1) that signifi- 5 Recommendations to Cultivate Digital-forward Innovation in the Built Environment By Jon Gustafson PS, CFedS, PMP, GISP and Clint Johnson

cantly impact data use. For instance, large transit agencies and high-speed rail authorities have shifted their asset management and capital improve- ment practices towards a broader geospatial data governance strategy that prioritizes location intelligence in decision making throughout the asset or facility lifecycle. The recognition of how important this contextual un- derstanding is with managing assets and facilities created a need to have a robust geospatial data governance framework and roadmap that describes short and long-term objectives underpinned by location-based data. In order to develop this scalable data governance framework at an enterprise level, a specific methodology can be applied that seeks to understand the gaps between current practices and operations, and desired levels of maturity as well as those strategies and activities that support incremental advancements with using geospatial data. Similarly, a scalable (asset lifecycle or project level) geospatial data governance framework can be integrated during early project planning activities to not only align with enterprise data governance consider- ations, but also bring clarity on project-specific requirements and spec- ifications that ensure sufficient geospatial data quality and integrity. Asset lifecycle and project level data governance ensures data is prop- erly organized, managed, described, integrated, and communicated. Project leaders are encouraged to spur digital-forward innovation by enriching asset and project governance documents with a plan for geospatial data governance. The right plan will deliver innovation and value by describing how geospatial data is being collected, analyzed and processed, managed, and shared with project stakeholders. Data Risk Management As the engineering and construction industry evolves through digital innovation, the importance of data risk management will be a para- mount consideration for all projects. Parametric design is becoming a popular method for design with the ability to encapsulate design intent into 3D models, which will continuously reduce the need for paper construction plan sets. Several transportation agencies have completed pilot projects of vari- ous methods of delivery to evaluate the use of digital delivery practices including elevating the design intent model above plan sets in order of precedence when a conflict arises. The shift towards parametric design requires a more astute risk management strategy (see Figure 2) that not only scrutinizes data uncertainty, but also incorporates an optimal mix of bringing key specialty disciplines in early and continu- ously throughout project delivery, and proactively communicating risk characteristics with stakeholders. Early awareness and action allow for better cost and quality control. Optimizing how geospatial activities should be sequenced and inte- grated into project delivery is an effective mitigation strategy to mini- mize reactive data analysis and risk exposure. Data risk management strategies that focus on early recognition and communication of risks associated with data gaps, data inconsistencies, and artifact detection, encourage collaboration around digital innovation at all levels. There is an enormous benefit to incorporating a data risk management strat- egy and process early in project planning to continuously identify data

Figure 1. Key data governance dimensions.


december 2020


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