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How Predictive Data Is Revolutionizing Preconstruction
Advancements in data science and predictive analytics are enabling construction firms to supplement the past data with reliable predictions over future costs.
FREMONT, CA: Preconstruction planning has been one of the most challenging aspects of the building lifecycle. Design professionals often must rely on past data to plan future projects. Thanks to data science and predictive analytics, professionals involved in the construction planning phases can now supplement historical data with reliable predictions of future costs. Predictive cost analytics developed by using a hybrid methodology that combines econometric techniques with data mining methodology addresses the shortcomings of traditional forecast data.
Conventionally, forecasts in construction were based on macroeconomic theory. Today, predictive data analytics solutions are based entirely on data-driven approaches. Such evidence is driven by pattern-seeking visualizations and is highly useful to preconstruction planning. Moreover, predictive analytics uses techniques to improve economic modeling methods. Thus, modern analytics is explicitly formulated to analyze observational data during preconstruction planning.
High-quality predictive models in construction are assessed continuously for degenerating relevance across the preconstruction phase. Such analyses are used to identify whether a model is sufficient or it needs amendment. Furthermore, individual model checking can be introduced as transformations in market conditions, and policies are announced. Predictive data can be used to predict the construction cost before the project begins. Apart from assessing cost and market, predictive analytics can also allow the construction firms to optimize the build schedule and determine the best location for construction.
Ultimately, the value of using accurate predictive cost is the unprecedented ability to construction professionals to understand future costs of construction projects.