Getting Started with Predictive Analytics in Construction

Discover how construction technology helps teams work on projects more efficiently and effectively with data analytics.

Analytics in construction continues to grow as the digital transformation in the construction industry becomes a priority. The introduction of big data in construction has opened the doors for courses that support data science programs with skills targeting applications in the design, construction, and operations of buildings.

Big data in construction is transforming the construction industry, automating systems that used to be on paper. A 2019 McKinsey report highlighted the impact of analytics to drive smarter engineering and construction decisions and why construction companies need to create digital initiatives that establish a new operating model. Such a shift requires adapting processes and organizational structures that apply analytics to review operations and performance. Companies that utilize predictive analysis, along with real-time data sharing, will become industry leaders.

Why use construction data?

Construction companies use data to make better decisions, increase productivity, improve safety on a job site, and reduce risks. By effectively utilizing the data gathered, they can predict future outcomes on projects, estimate and bid on projects better, and adjust as needed.

Examples of how to use construction analytics include:

  • Analytics improves worker productivity by reducing the amount of time needed to access the materials, tools, and equipment for a task. Workers can be tracked using wearables or via smartphone, and the materials and devices with sensors to determine how things are handled throughout the day. This information is then analyzed to find solutions that make things more efficient.
  • A company leveraged data from over 100 of its past projects. They analyzed several variables, allowing them to pinpoint characteristics in their projects that influenced profit margins.

What Is Construction Data Analysis?

Prescriptive analytics focuses on analyzing structureless data. For example, construction data is unstructured if the project can not meticulously track things. Using predictive analytics monitors and maps project progress with a short-term analysis of the work that still needs to be done.

Types of Construction Technology for Data Analytics

Using construction analytics software helps with improving the processes and productivity on a job site. Other key benefits are reducing risk while increasing safety for workers, bidding smarter to win more work, and transitioning from basic analytics to predictive analytics.

Accessing more construction data is vital but making sure it is appropriately analyzed is what drives change. Firms need and want consistent, up-to-date financial and project information and to be warned ahead of time when specific situations occur that could become problematic. Project managers also want to forecast to prepare for best and worst-case scenarios, and review online analytics to see which factors affect profitability.

Procurement analytics are the next steps in understanding data. Procurement data is collected and analyzed to form insights and assist in making effective business decisions. A procurement analytics report usually contains data from different source systems, classifying it and displaying it in a dashboard or other business intelligence tools.

Why would you need procurement analytics?

Construction companies want to consolidate how they view their procurement spend. There are different types of procurement analysis:

  • Descriptive analytics
  • Diagnostic analytics
  • Predictive analytics
  • Prescriptive analytics

Research and Reports on Construction Technology for Data Analytics

There are several places to find reports for data analytics and construction labor productivity. Deloitte engineering and construction have a few areas addressing these types of reports, such as the Deloitte construction report which showcases the trends in construction and large capital projects. In 2019, McKinsey released McKinsey modular construction explaining the impact it would have on construction and how it accelerates project timelines.

What types of materials and resources prove effective when working with analytics? Here are a few:

  • Data science architecture diagram

This type of diagram shows the relationship between data entities, business services, and application components through a diagram showing how the logical entities are physically realized.

  • edX construction and edX data engineering

These are free courses on edx.org. Students will learn why data science is vital for the construction environment, why building industry professionals should learn how to code, an overview of the Pandas data analysis library, basic machine learning concepts applied to build data, and more.

  • Architecture and engineering edX

Free edX courses focus on how professionals in the building environment sector develop data science skills. Participants learn tools and skills to supplement spreadsheets, like data loading, processing, visualization, and fundamental machine learning using Python.

  • Architecture and engineering NUS

The National University of Singapore offers many courses and symposiums in various areas of architecture and engineering.

  • Data analytics in construction courses

There are a host of free and paid courses available for architects and engineers.

Career Opportunities in Construction Technology Data Analytics

Several opportunities revolve around data analytics when it comes to a career in the construction, architecture, and engineering field.

Construction analyst jobs are growing in popularity and are available across the globe. These positions have several tasks, such as visiting project sites to determine off-site construction or demolition conditions, reviewing and evaluating housing construction proposals, examining properties for structural soundness, and more. Depending on the location and firm, the construction analyst’s salary varies. On average, this role makes $57,500 per year, but the top 10% makes over $90,000 per year.

How these roles use data science?

Construction technology data analytics use data science for several use cases, such as predictive analysis, accurate simulation before construction, design issue prediction, warranty analysis, analyzing construction project risks, tracking construction equipment and assets, optimization, contractor performance optimization, accurate budgeting, and planning, robotization, and product development.

There are a variety of titles for construction data scientist or construction data analyst. For instance, in the construction field, these roles may also be referred to as project control analysts. Here is a sample job description:

Job Description Sample

The ideal candidate is an ambitious construction professional with at least five years of experience managing and analyzing cost and schedule in large, multi-project construction programs. In this role as Project Controls Analyst, you will work closely with our global construction teams in New Builds, Retrofits, and various internal departments to support all aspects of project cost management and risk identification for our clients’ growing Infrastructure Construction team. Excellent communication, presentation, and analytical skills are a must in this highly collaborative role. The Project Controls Analyst will work closely with site Project Management to report project health and influence how the Site Teams understand and mitigate project risks.

Responsibilities:

  • Provide preconstruction support during the project approval phase, provide historical cost data, and assist in developing annual contractor purchase orders.
  • Manage internal project budget/schedule of values within project management software for monthly financial reporting on multiple New Builds and Retrofit projects on campus.
  • Manage external project budget and communications within external project management
  • Software including commitment management, change management, document control, etc.
  • Manages monthly forecasts and cash flows, understands forecast variances, develops monthly executive reports.
  • Manage monthly vendor invoice review process within invoicing software, including project management review, 3rd party auditing services, and communication with Accounts Payable.
  • Responsible for accurate quarterly accrual reporting for all existing PO’s on campus, including communication with vendors and Finance.
  • Facilitate change order routing and review processes within project management software, content analysis, and executive approval as required
  • Review project financial health with the management team each month (via dashboards, budget software, schedule comparisons, productivity reports, etc.)
  • Management of Internal and Contractor Risk Register update & contingency evaluation.
  • Financial closeout of internal budgets, Vendor POs, etc.
  • Work closely with Site Scheduler to compare financial forecasts and risks are in alignment with schedule updates.
  • Assistance with programmatic initiatives, training, and alignment opportunities.

Qualifications:

  • BA/BS or equivalent in construction management or engineering.
  • Minimum of (5) year’s professional experience in construction and project planning for large multi-project construction programs.
  • Strong collaboration skills and a problem-solving mindset.
  • Experienced with Primavera P6, Microsoft Office (Excel, Powerpoint, Word), and Google Suite.
  • Must be familiar with cost control tooling (such as eBuilder, Procore, or others).
  • Ability to clearly communicate financial status and schedule details from multiple construction sites to project managers.
  • Excellent communication, presentation, and analytical skills are a must in this highly collaborative role.

While it was not on the list, Python for the construction industry is becoming essential in construction data science. It is now recommended for building-industry professionals to learn how to code.

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