Thorough, reliable information is a necessary component for making sound decisions. While many sources of data are available in the construction industry, they can be difficult to obtain or may be inaccurate. Lately, companies have undertaken initiatives to compile past results, making the information available and usable for better predictions. Also, recently developed technological systems and tools are improving the quantity, quality, and timeliness of information available to the construction industry. Although past data sources and new tools are providing a wealth of information, a critical issue remains; ensuring the data is trustworthy. Creating a trusted historical database requires consistent, methodical data collection techniques, analysis, monitoring, verification, and normalization. While improving data quality is necessary for improving predictions, it can also better position companies to take advantage of upcoming machine learning and artificial intelligence (AI) capabilities. Developing proper strategies to ensure useful quality data can help improve tracking, decisions, and predictions related to future works.
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