What do you do if you want to optimize decision-making in construction using data analytics?

Optimizing decision-making in construction using data analytics involves several steps. Here's a general approach: Define Objectives: Clearly define the goals and objectives you want to achieve through data analytics. This could include improving project efficiency, reducing costs, enhancing safety, etc. Data Collection: Gather relevant data from various sources such as sensors, project management software, BIM (Building Information Modeling) systems, historical project data, weather data, and more. This data could include information about project progress, resource utilization, material usage, equipment performance, and safety incidents.

Begin by gathering a wide range of data from numerous sources, including project management software, IoT devices, sensors, previous project data, weather forecasts, and social media. This information could include project schedules, material costs, worker productivity, equipment utilization, safety problems, and so on.

We deal in bid data for specialty contractors. I bet 90% of Specialty Contractors run their bid log in Excel which is at best a tracking tool and rarely complete and comprehensive. Manual data collection and switching between systems can be highly inefficient and error-prone. When data must be manually entered into one system and then transferred to another, the process not only consumes valuable time but also increases the risk of errors. Each transition point is an opportunity for data to be lost or inaccurately recorded. Consider the impact: Are your current practices slowing down workflows and compromising data integrity?

Develop a well-structured Work Breakdown Structure (WBS) for the project, anticipating all direct and indirect events. Establish responsibility levels and professional profiles without subjectivity. Map and qualify critical paths.

Like it or not, alot of construction is run on hunches. I always say, "What is the first question you are going to ask of your data?" Smaller sample sets with experience, gut and intuition can yield good results. Finding patterns amongst data requires ALOT of it.

One thing I found helpfull is using old and new data to be able to define quality even within different production processes.wen you have a lot of data in some cases ai can help you go faster.at the end testing and inspection before placing the part and after the part is placed again testing and inspection of the complete construction

Use advanced analytics tools to find patterns and trends in the acquired data. This could include employing machine learning algorithms to evaluate historical data and find elements that influence project success or failure. Understanding these trends allows you to make better judgments during the construction process.

Perform on-site surveys of annual cycles within the local economy, mapping resource demands during specific times, such as labor, equipment, supplies, logistics, among others. Data richness is an ally of decision-making accuracy.

Use predictive analytics to foresee future outcomes and scenarios based on past data and current project conditions. Predictive models can assist project teams foresee problems like delays, cost overruns, and quality concerns, allowing them to take proactive steps to reduce risks and maximize resources.

Use data-driven insights to optimize construction processes including resource allocation, scheduling, and budget management. AI-powered algorithms can help you find areas for improvement and streamline procedures, increasing productivity and reducing waste.

Use data analytics to help project stakeholders communicate and collaborate more efficiently. Visualization tools and dashboards can assist explain complicated information in a simple and intelligible way, allowing for better decision-making and team alignment.

Managing risks in construction is essential for ensuring project success and avoiding costly delays and accidents. Here's how data analytics can be used to enhance risk management in construction: Risk Identification: Utilize data analytics to identify and assess potential risks associated with the project. This could involve analyzing historical project data, safety records, environmental factors, stakeholder feedback, and external factors such as weather patterns and regulatory changes. By analyzing this data, you can identify common risk factors and prioritize them based on their likelihood and impact. Predictive Modeling: Develop predictive models to forecast potential risks and their potential impact on the project.

Using surveys of annual cycles in the local economy and seasonal demands, such as labor, equipment, supplies, logistics, among others, along with the already established schedule, it is crucial to identify activities competing with others locally. Criteria for risks are defined for each level of the WBS, with special attention to the critical path, where alternatives are mapped and their risks considered. Risks, both known and potential, are identified, analyzed, and integrated into the project planning. Reflecting on similar projects that have succeeded or failed, and considering threats and opportunities throughout the project, is essential.

Here's an example of how data analytics can be applied to manage risks in construction: Imagine a large-scale construction project that involves building a new skyscraper in a densely populated urban area. The project faces various risks, including safety hazards, supply chain disruptions, regulatory compliance issues, and budget overruns. To effectively manage these risks, the project team decides to leverage data analytics throughout the project lifecycle. Risk Identification: The project team analyzes historical data from similar construction projects to identify common risk factors and assess their potential impact on the current project.

Start with the basics: have a well-structured Work Breakdown Structure (WBS) and understand the impact of each hierarchical level on the final outcome, both in terms of physical and financial aspects. These will always be your two main performance indicators.