Leveraging data to minimize construction risk and maximize results.
The adoption of technologies in the construction industry is changing the building business model, improving collaboration and productivity, and also driving up profit margins and salaries.
Tools such as 5D BIM, data analytics, drones, mobile solutions, and collaborative tools such as standard data environments are becoming more common among larger construction companies worldwide.
That is no shock; it's one of the fastest-growing IT business areas. In 2014, the Wikibon community valued the Big Data business at $18.3 billion. By 2026, this market is projected to increase 14.4% annual rate and reach $92.2 billion.
Various methods the construction industry is using big data to maximize results
Improving onsite productivity:
It is possible to improve work processes, find ways to automate workflow, save expenses, and do other things by analyzing data gathered from job locations. With data analytics, tracking employee movements, in addition to the more conventional methods of measuring cash flow and expenses, is possible.
Tracking construction equipment process:
Many types of tools, trucks, and equipment are used in the construction sector. Properties are physical assets. Several tools are used for tracking the performance of assets and equipment in the management and tracking of construction assets. It can be costly and wasteful if assets and equipment are unused.
Data from building projects can be collected and analyzed to reveal information about potential dangers and issues. For instance, data analytics enables the detection of issues with the material supply chain, unplanned weather events, and labor and equipment productivity. This will provide information about potential project delays, duration, and budget overruns.
Combining business and project-related data can be used to find both positive and negative trends. These interpretations can aid in risk mitigation and encourage successful project management.
Predictive analysis outcomes can be utilized to enhance decision-making. It is a method for identifying patterns, trends, and linkages in existing data. Its goal is to address a problem by utilizing data to improve comprehension and forecast future behavior based on past behavior. For example, construction companies use predictive analysis to identify the variables that may impact project punctuality and profitability.
By keeping track of expenditure, income, scheduling problems, and other critical data and analyzing it, construction companies can recognize potential issues before they occur and develop fitting solutions.
Increase work site safety:
Working on construction sites presents several safety risks, including working at heights, electrocution, slipping, falling, and getting stuck between or struck by moving objects. However, contractors can take action to decrease chances and stop such occurrences by tracking and analyzing safety-related data to identify high-risk activities and hazardous environments.
No matter the sector, correctly preparing data for data analytics is crucial to extracting insightful information from it. It's necessary to filter, organize, reformat, and clean up raw data. Data entry, document conversion, coding, editing, and even transcribing would all be part of this. Also, Digitizing workforce management ensures construction organizations recognize some impressive benefits, including a reduction in labor costs through more accurate budgeting and planning, risk mitigation, and much more.