By Jon Gustafson PS, CFedS, PMP, GISP and Clint Johnson

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 strategies that can be implemented to improve safety, accelerate maturity, and improve program and project delivery. These recommended strategies focus on data governance, data risk management, effectively using location-enabled technologies, standardization, and data-driven decision support systems.

These recommendations are foundational to understanding the relationships between the representative digital and physical environments, improving project performance and collaboration, and improving decision making of all stakeholders, including project managers, engineers, constructors, owners, and public agencies.

Figure 1. Key data governance dimensions.

Geospatial Data Governance

Since the widespread adoption of the internet and subsequent advancements in digital technology for the engineering and construction industry, 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 organizations looking to make decisions using data, which has led to an emphasis on addressing key data governance dimensions (see Figure 1) that significantly impact data use. For instance, large transit agencies and high-speed rail authorities have shifted their asset management and capital improvement 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 understanding 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 considerations, but also bring clarity on project-specific requirements and specifications that ensure sufficient geospatial data quality and integrity.

Asset lifecycle and project level data governance ensures data is properly 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.

Figure 2. Geospatial data risk profile through optimal surveying/design practices: (1) stronger control network and improved data accuracy/coverage in specific areas; (2) more time for parametric design and automation for contract documentation; (3) data verification and more detailed data in areas significant for design intent; (4) less time incorporating new data and traditional stakeout in favor of automation; (5) verification of control network reinforces data confidence; and (6) sustained effort for real-time verification, measurement, and as-built records. Source: US Federal Highway Administration

Data Risk Management

As the engineering and construction industry evolves through digital innovation, the importance of data risk management will be a paramount 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 various 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 continuously 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 integrated into project delivery is an effective mitigation strategy to minimize 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 strategy and process early in project planning to continuously identify data risks, apply mitigation measures, track and monitor risk exposure, and communicate risk severity and likelihood to project stakeholders.

Effectively Using Location-Enabled Technologies

Effectiveness in applying location-enabled technologies on projects is achieved through incorporating suitable solutions that meet or exceed geospatial data specifications and expectations. There are many technologies and solutions that can meet specifications; however, many can fall short with meeting expectations concerning cost or quality if not properly applied.

It is critically important that a suitability analysis be done by geospatial experts to ensure the right tool is being used for the right purpose. Geospatial experts are equipped with the required knowledge and expertise to advise on integrating location-enabled technologies focusing on specific project criteria and requirements as opposed to first trying to apply a specific technology to meet project requirements.

The effective and suitable use of location-enabled technology such as UAS enables accurate contextual understanding for decision making. However, there are many instances where the use of UAS does not meet the data quality requirements for the project. In such cases, other technologies are applied to achieve more meaningful outcomes with geospatial data quality and integrity. It’s important to let the project characteristics and requirements lead to objectively applying location-based technologies.

Standardization

Consistent and repeatable processes drive project success when properly applied. Overly prescriptive standards create an imbalance with conformance activities and value delivery, so following a practical and consensus-based approach to standards development is arguably more important. Advancements in UAS technology quickly surpassed industry standards development in key areas, which has stifled the use of this technology in many applications.

However, standards development organizations have made great strides over the past couple years with key standards that are enabling the safe integration of UAS technology into regulated airspaces and for more advanced operations. This includes the ability to collect, transfer, and otherwise handle resultant location-based data. While there are many industry standards to support these activities, standard architectures and related best practice guidance are now being developed for various UAS operations.

Leveraging standard practices and data formats for applying UAS technology to projects opens the door for continuous value creation through repeatable operations in change detection (e.g. quantity verifications, site monitoring, etc.) and consistent UAS technology specifications for distinct project activities. This ensures sufficient geospatial data quality and integrity. Project leaders should build and incorporate open data model and exchange standards to maximize interoperability and integration between proprietary systems. Also, adopt or create digital-enabled standard practices for key project activities that require repeatability and predictability.

Figure 3. Suitability criteria for location-enabled technologies. Source: US Federal Highway Administration

Data-Driven Decision Support Systems

The power of effective decision-making is fueled by accurate and reliable data organized and delivered through appropriate mediums. Achieving clarity and meaning from the available data requires a keen focus on geospatial data quality and integrity management. Engineers use a variety of tools and systems to make design decisions and compile their design intent into CAD models for construction plan development. Limited interoperability between these tools and systems combined with an incremental data flow discourages engagement, collaboration, and innovation.

Incorporating more innovative and iterative processes with early engagement of key disciplines brings the right data at the right time to inform the right decisions. This allows engineers to spend less time on data cleansing and manipulation, which will achieve more effective project delivery.  Removing the subjectivity of decision making by integrating and exploiting accurate data improves the quality and reliability of important decisions.

UAS technology is rapidly transforming how video and imagery data is being used for decision-making. Bridge inspectors are relying heavily on its ability to collect high resolution data of key bridge components at close range instead of renting snooper trucks, using rope access, etc. The improved safety and cost savings combined with rapid deployment and data access, illustrates how integral UAS has become to decision workflows. Cybersecurity risks notwithstanding, the ability to stream actionable video data from UAS platforms to decision makers in other remote locations enables near real-time situational awareness for emergency or crisis situations.

To effectively harness this technology and improve decision-making, project leaders need to expose accurate and reliable (as determined by data stewards) data to decision support systems (e.g. CAD, design visualization, dashboards, business intelligence, etc.) to enable simplified fit-for-purpose integration of geospatial data. This ensures geospatial data is used properly and propagates benefits throughout project delivery.

Digital Enablers Focused on Data Quality and Integrity Improve Project Delivery

The engineering and construction industry is on the cusp of a digital transformation with asset owners and project leaders in a unique position to influence and drive this industry shift by adopting a robust digital ecosystem comprised of the right frameworks, technologies, processes, and workflows. Cultivating this digital ecosystem on projects using specific data quality and integrity enablers can be achieved through incremental (or transformational) changes including more effective geospatial data governance, deliberate data risk management, integration of suitable technologies, application of standards, and enrichment of data-driven decision support systems.


Jon Gustafson PS, CFedS, PMP, GISP, is a Senior Principal and the Geospatial Services Leader for the U.S. East region at Stantec Consulting Services.
Clint Johnson is the Sector Leader for Geospatial Services in Canada at Stantec Consulting Services.

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