Knowledge is power, but knowledge requires the acquisition of hard information. It requires data. Construction industry data has the potential to advance both field and administrative capabilities. Harnessing data promises to be powerful. But what data? Where to begin?

In our first article on data metrics, we introduced the basic concepts of data and the challenges construction companies face: selecting which data sets to analyze and getting people in the field to adopt data collection and analysis tools. We observed the primary data types: cost, schedule, productivity, and material procurement. We cited two contractor use cases where better data collection led to more successful project outcomes by using the information to help plan job-site manpower and obtain better material pricing and deliveries. 

However, the construction industry is dealing with significantly more data that can optimize construction activities. What are the types of data, their categories and their relative importance? 

First, there are two basic types of data: digital and analog. Analog data, which is in "old-fashioned" media: printed documents, drawings, photographs, audio and video recordings, and digital data, which is electronic, residing on computers and servers in the cloud. Over the past 20 years, data has become overwhelmingly digital.  

Not only has data become increasingly digital, but the volume of data is growing exponentially. According to Nick Decker of cloud document management company Egnyte, the volume of data today has quadrupled from just a few years ago. Decker also notes that with 5G and satellite communications, data is much more accessible to those in the field. With this increase in data, there also is an increase in data quality and duplication issues.

This week, we identify various types of data. Future articles and case studies will focus on how companies cope with massive data and determine which data metrics might provide value. Value might be improved safety, quality and worker satisfaction, reduced project cost or improved schedule performance.

Types of Data on a Construction Project – Three Categories

We can segment data into three broad categories: pre-construction, construction and post-construction. Some of the segments will overlap. For example, design data will carry over to the project's construction phase. The types presented here are in phase order. Not all these data sets would apply to all projects. Note, in this listing, the construction phase data was sub-classed into two types: project management, data to manage the construction process, and contract administration, data needed for contract management functions, primarily document and record keeping.

Pre-Construction Data

1. Design and Engineering Data: Construction projects begin with the design and engineering phase, which involves creating detailed plans and specifications. This phase produces data such as architectural drawings, structural plans, 3D models, digital twins, material specifications, and load-bearing calculations. Analyzing this data can help identify potential design flaws, optimize material usage, and ensure compliance with building codes.

2. Building Information Modeling (BIM) Data: BIM data includes virtual 3D models and associated metadata about the construction project's elements. Analyzing BIM data can help optimize design and construction processes, improve clash detection, and enhance building performance. BIM models could be considered part of Design and Engineering Data, but BIM models are or should be used throughout the construction phase and, ultimately, for maintenance and operations.

3. Environmental Impact Data: Environmental impact data includes information about the project's environmental impact, such as emissions, waste generation, and resource consumption. Analyzing this data can help project managers minimize the project's ecological footprint, meet sustainability goals, and comply with environmental regulations.

4. Site Data: Site data includes information about the physical construction site, such as site surveys, soil tests, and weather and climate conditions. Analyzing site data can help identify potential site-related issues, optimize construction site logistics, and ensure the site is safe and suitable for construction.

5. Budget and Financial Data: Budget and financial data provide information about the project's estimated costs, actual expenses, and funding sources. Analyzing this data can help project managers identify cost overruns, assess the project's financial health, and ensure that adequate funding is available.

6. Schedule Data: Construction schedules contain critical information about project milestones, task durations, resource allocation, and sequencing. Analyzing schedule data can help identify potential delays, optimize resource allocation, and adjust the project timeline to meet deadlines.

7. Stakeholder Engagement Data: Stakeholder engagement data tracks the involvement and feedback of stakeholders in the construction project, including clients, regulators, and community members. Analyzing this data can help improve stakeholder relationships, manage expectations, and enhance project outcomes. Stakeholder considerations can be particularly important for contractors working with the same clients or types of projects.

Construction Data – Project Management

8. Geospatial Data: Geospatial data provides information about the geographic site and the construction elements' spatial relationships. Analyzing geospatial data can help optimize site layout, plan construction logistics, and monitor site progress.

9. Workforce and Productivity: Workforce data tracks the construction workforce's performance, productivity, and availability. Analyzing this data can help optimize labor allocation, improve worker productivity, and reduce the risk of labor shortages.

10. Procurement Data – Permanent Materials and Installed Equipment: Procurement data provides information about materials, equipment and services procurement for the construction project. Analyzing procurement data can help optimize strategies, reduce costs and ensure timely procurement.

11. Temporary Materials, Supplies and Construction Equipment Data: Material and equipment data track the quantity, type, and location of materials and equipment used on the construction site. Analyzing this data can help optimize material procurement, reduce equipment downtime, and prevent material and equipment theft and attrition.  

12. Quality Control Data: Quality control data includes information about inspections, tests, and quality assessments conducted on the construction site. Analyzing this data can help ensure that the project meets quality standards, identify potential quality issues, and ensure compliance with regulatory requirements.

13. Safety Data: Safety data includes information about safety inspections, incidents, and near-misses on the construction site. Analyzing safety data can help identify potential safety hazards, improve safety protocols, and ensure compliance with safety regulations.

14. Reality Capture Data: With cameras, sensors and other devices, there's a wealth of data that you can capture and analyze to provide insights into job progress, equipment usage and environmental conditions, helping to identify potential issues and create a visual documentation of the project. Data collection can be through drones, laser scanners and IoT (Internet of Things) sensors. 

15. Subcontractor Data: Subcontractor data tracks the performance of subcontractors working on the construction project. Analyzing this data can help assess the performance of subcontractors, optimize subcontractor selection, and improve subcontractor relationships.

16. IoT Data: With the advent of the Internet of Things (IoT), construction projects can generate real-time data from sensors and devices deployed on the construction site. Analyzing IoT data can provide insights into equipment usage, environmental conditions, and worker productivity.

Construction Data - Contract Administration

17. Document Management Data:  Construction projects generate vast documentation, including contracts, permits, drawings, and manuals. Analyzing document management data can help optimize document workflows, ensure compliance with documentation requirements, and reduce the risk of lost or misplaced documents.

18. RFIs and Issues Data:  Requests for Information (RFIs) and issues data provide insights into the questions, clarifications, and problems arising during construction. Analyzing this data can help project managers resolve problems more efficiently, improve communication, and prevent recurring issues.

19. Change Order Data: (Includes potential change orders and contract modifications.) Change orders are modifications to the original construction contract that may arise during the project. Analyzing change order data can help identify trends in change orders, assess the impact of changes on the project timeline and budget, and optimize change order management. A contractor must identify change orders, determine how they comport with the contract specifications and whether and how they impact the project schedule, then put them through an approval process and ultimately negotiate the cost.

Post Construction

20. Warranty and Post-Construction Data: Warranty and post-construction data track the performance and issues of the constructed facility during the warranty period and beyond. Analyzing this data can help identify potential performance issues, manage warranty claims, and improve the design and construction of future projects.  

21. Project Turn-Over / Facilities Maintenance and Operation Considerations: BIM models, digital twins and COBIE individually or collectively represent a rich data set for the owner's use once the project is complete. If information has been adequately maintained and the data quality is trusted, there is not much needed to do for data collection at this point. Analysis might be conducted to compare the final product to the original to uncover possible areas of interest.

22. Lessons Learned: An often overlooked aspect of a construction project is capturing and documenting lessons learned and making this information available for future projects.

23. Dispute Resolution: In the event of unresolved claims or incomplete change order negotiations, data collected at various phases of the project can be gathered, collated and analyzed to help resolve these disputes. The more data collected, the more likely it is to resolve any factual issues that might hinder dispute resolution. Additionally, the better and more complete the data is, the less future expenses of hiring claims consultants to collect or recreate the data.

In conclusion, construction projects generate a wide variety of data that can be analyzed to enhance project management, improve productivity, and ensure project success. Leveraging these additional data types can provide valuable insights into various aspects of the construction process and drive continuous improvement in the industry. The volume of the data is considerable, but the application of AI makes analysis possible and speeds its delivery.