In today’s technology-driven world, data is becoming more important in almost every industry. In the construction industry, even a small slip or miscalculation can result in millions of dollars in losses, or missing critical project deadlines.
Predictive analytics, a new advanced form of data analytics, helps firms to complete jobs more efficiently and on time by using data to identify trends and helping builders to better identify potential problems before they impact a project.
Today, you no longer have to be a Fortune 500 company to take advantage of big data and cloud technology. You just need to learn how to effectively use it. Predictive analytics is one way to take advantage of the data available.
So What is Predictive Analytics?
Predictive analytics is a process that uses existing data to uncover patterns, trends, and relationships. Its purpose is to solve a problem using data to deepen understanding and predict future behaviors based on past actions.
You actually encounter predictive analytics every day. When you shop online, retailers like Amazon use predictive algorithms to compare product information, purchase history, and other shopper activity to make a prediction about what you’re shopping for and provide recommendations in real-time.
Today, predictive analytics in construction are being used to solve all kinds of problems, some of which can contain an amazing number of sometimes hidden, sometimes superfluous variables. Predictive analytics simply attempts to identify and analyze key variables within a data set to make “educated” predictions.
Two Practical Uses of Predictive Analytics in Construction
Construction produces a large amount of useful data that can be used to help run a project much more efficiently, avoid potential issues before they arise, and better manage the project to bring them in on time and on budget. Here are some practical uses of predictive analytics on and off the job site.
Materials obviously play a major role in influencing a project’s bottom line. Predictive analytics can help you to forecast how much of a particular material you’ll need for a job. For example, on a larger project, something as simple as over-ordering screws can be enough to throw the entire budget off.
This is why it’s important to keep good records of purchase orders for every project – to build a database for the future. Analytics helps with the supply chain as well, helping to determine where to place materials at any point in time. Managing resource placement increases efficiency and decreases the cost of materials transportation.
Predictive analytics is useful when creating annual budgets. Using and comparing data from multiple sources can help you more accurately determine seasonal cash flow needs. This is especially beneficial in industries like construction which are seasonal. This helps prevent cash flow shortages during off-months, like winter when there is less work available, or in climates where adverse weather can delay or damage projects during construction.
If cash flow becomes a problem using accounts receivable factoring (a type of predictive analytics) is a useful tool to pay employees and start new jobs before you normally would be paid.
Predictive analytics is another technology that has multiple uses in the construction industry. By keeping track of expenses, income, scheduling issues, and other critical data you can build a database of information that is beneficial for predictive analytics.
By analyzing these multiple data sources you will be able to recognize trends in your business, identify potential problems before they occur and provide possible solutions, while helping you understand relationships that can affect timeliness and profitability of projects.