Why we need more data scientists on engineering teams

Engineering teams: You need data scientists to join, and here’s why

By Microsol Resources August 9, 2022
Courtesy: CFE Media & Technology

Building information modeling (BIM) is software that applies 3D modeling to processes involved in designing, constructing and managing buildings. The engineering team responsible for making decisions must coordinate their activities with architecture, engineering and construction (AEC) professionals to achieve optimal results. An important role to add to that team is data scientists.

Potential role of data scientists

Data scientists aren’t included in this core group, but that should change. The insights and tools a data scientist has can help inform BIM professionals in AEC occupations on how to improve a project’s outcome. Building maintenance also is an issue during the early stages of planning, designing and constructing the facility. Recruiting these scientists onto a team of engineers might require a large investment up front, but the potential benefits outweigh these concerns.

The era of Big Data has ushered in additional levels of complexity into current engineering models. Data scientists can work with engineering teams to help bridge this gap. While there is ample support offered through BIM software products for managing a project, data scientists are best equipped for parsing the implications of large data sets that have started to affect the industry.

Role of engineering teams in BIM

Engineering teams are responsible for crafting a design that represents the interests of stakeholders while maintaining a system that maximizes workflow efficiencies. This can be done in part using 3D models, BIM software and consultations with the AEC group. However, data scientists are key players in the initial stages of the process. The design will only be capable of integrating the information available to the core engineering team.

There are large amounts of data being produced, which is the result of changes that came with the digital age and the information economy. However, civil engineering hasn’t kept pace with these developments, and a disconnect has occurred because of this divergence. Data scientists can help facilitate the information gap between big data and the engineering team. This helps create an efficient workflow through coordination and collaboration in a project.

Reducing human error through data science

Project managers used to have to go through plans to choose the final project’s design. There was a way to view the result before the construction process, but it could lead to problems. The level of expertise required to coordinate these projects was intense and time-consuming. Mistakes were common, and there wasn’t a way to review the plans and make adjustments during the planning phases.

BIM changed this situation. The capacity for stakeholders to review the digital model of a building prior to construction has big implications. However, data scientists are rarely invited to these meetings. It’s important the engineering team has access to the most minute details of the building project, site of construction and relevant products. However, the same information will be viewed by data scientists so they can provide key insights and missing information.

Data scientists and BIM processes

BIM has changed because of the new world of information technology (IT). Artificial intelligence (AI) and machine learning (ML) are coupled with access to diverse data sets that can inform the project’s process. Gaps in research and data science are delineated. This is an area where a data scientist can help engineers prevent future problems while in the first stages of the project.

A team can benefit from a data scientist with a keen eye for looking through dense data-driven documentation. Since a team views the same documents from one location, the they can update the engineering team regarding the key data points. As the project unfolds, the relevance of this information also becomes more visible. Guesswork is reduced in the process, and predictability based on scientific data sets improves the sense of team confidence in the outcomes.

Here are the three key elements to keep in mind:

1. The 3D modeling communicates detail about the project’s lifecycle to the entire team.

2. Investors can evaluate the quality of their decisions based on accurate, updated information driven from data scientists.

3. The use of BIM relays this information to the client through walkthroughs and virtual renderings, which reduce the information gap.

Measuring outcomes with data scientists

Designers in previous generations relied on their immediate tools and skill set. The advent of BIM technologies enabled them to communicate across teams. This is crucial because the construction team is now able to provide insights to the engineers and architects at the project’s beginning. This should allow these teams to understand how inviting data scientists into the process can provide the following additional benefits:

1. Real-time editing enables meaningful team participation, and data scientists could bring additional insights.

2. Data scientists help reduce the need for revisions when consulted in the earliest stages of the BIM design process.

3. Data scientists can receive insights from the construction team while participating in BIM groups on the engineering team. This can bridge experience gaps on both sides.

How engineering teams benefit from data scientists

Digital engineering can fuse the knowledge of engineers with the insights of data scientists to produce outcomes that reflect the benefits contained within both of these fields. The ability to test a building design prior to construction can be enhanced with the aid of data scientists.

These scientists can help engineering teams troubleshoot common issues and resolve them. Accuracy is essential for accelerating production schedules without mistakes. The BIM software allows real-time decision-making and reduces redundant meeting times. This enhances the team’s capacity to stay on schedule.

Communicating back to the office is another key step in the workflow. Stakeholders benefit from details present when engineering teams are coordinating with data scientists. This reduces ambiguities. The labor costs involved in having data scientists are recouped in the value added by their participation on the engineering team.


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