Defining New Terms in Construction Technology

Despite the construction industry being one of the least digitized industries on the planet, 78% of contractors believe that tech will help them increase productivity, and 52% consider investing in tech to be their top priority. In other words, most construction firms are on the cusp of major tech investments. Before you plan your investment strategies, here are the must-know construction technology and data analytics terms that are defining the industry landscape.

Predictive Analytics

Predictive analytics is the method of using continuous and historical data to predict future bidding needs and financial requirements. In a sense, predictive analytics is the outcome of most of the new technologies the construction industry gets excited over. The value of predictive analytics goes far deeper than simply understanding how, why, and how much to bid on projects. It helps you understand, predict, and react to future outcomes using non-static, constantly evolving data. Typically, predictive analytics leverage both machine learning and AI to feed data into algorithms and models to provide predictions.

Descriptive Analytics

Descriptive analytics uses past data (either static or continuous) to help you understand past requirements and needs. You can use descriptive analysis to build forecasts, but they won't be as solidified or as accurate as predictive analytics. Ideally, you want to combine both predictive and descriptive analysis to create best-in-class financial forecasts.

Prescriptive Analytics

Prescriptive analytics takes predictive data to formulate outcomes. In other words, descriptive analytics helps you understand past data. Prescriptive analytics converges descriptive analytics and continuous data feeds to help you predict the future. Prescriptive analytics also helps you understand which courses of action you should take to create ideal outcomes.

Automation

Automation can refer to either physical-software automation (i.e., autonomous machines, drones, robots, etc.) or software automation (e.g., software bots, automated workflows, etc.) Typically, the latter gets the bulk of investment from the construction industry. If we continue our line of thinking, automation is how predictive analytics ingests data. It does it constantly and automatically. In other words, automation turns painstaking manual processes into background noise.

Machine Learning (ML)

Machine learning is the use of computer algorithms that evolve and learn constantly and automatically. For example, predictive analytics software may leverage machine learning to constantly evolve its predictive capabilities. The more data that's fed into the system, the more it learns about how your business operates, your specific needs, and how that data impacts and influences those needs. Machine learning virtually eliminates humans from the software learning and configuration processes.

Artificial Intelligence (AI)

Artificial intelligence is the use of human-grade intelligence in machines. The original definition was any machine process that, if applied to humans, would require intelligence to perform. In construction technology, it may be easiest to think of AI as machines that can ingest information from their environment to perform actions relating to that information — such as pattern recognition or problem-solving. For example, if you use solutions like Briq, we use AI to help generate outcome suggestions for prescriptive analytics.

Software-as-a-Service (SaaS)

Software-as-a-Service is a model of technology delivery that allows construction companies to access managed technology via the cloud for a subscription price. In other words, SaaS brings billions of dollars in R&D to your business for a monthly fee. It's hard to grasp just how impactful SaaS really is for construction and finance. Services like SaaS account for 45% of the overall value in the global supply chain, and that number is growing rapidly. SaaS unlocks AI, ML, and predictive analytics for construction firms of any size without the need for multimillion-dollar proprietary systems.

Structured Data & Unstructured Data

Structured data is any data that resides in a relational database. These are things like costs, project names, or dates. Anything that you would typically plug into a spreadsheet is structured data. It's easy to search, analyze, and utilize across your firm.

Unstructured data is any data that isn't structured. This could be word files, videos, sensor data, scientific data, or social media posts. Some unstructured data can be turned into structured data by relating data to a broader data schema (e.g., plugging information from a word file into a spreadsheet), but a significant amount of unstructured data is simply unstructured by nature.

Leveraging Modern Technology to Produce Better Outcomes

Briq was quite literally built for construction. All of the technologies listed above are utilized in our ConTech solution. We leverage automation to continuously grab data from disparate sources, like PDF’s Excel files, and plain text documents. Then, we take that data and turn it into predictive and descriptive analytics using machine learning. Next, we use AI to deliver prescriptive analytics, which drives data-driven insight and improved financial decisions.

In time, predictive analytics, machine learning, and artificial intelligence solutions will enable huge changes to the ways construction businesses bid on and deliver projects.