One would assume the construction domain to be one of the last domains to open their doors to analytics. In fact, the mention of analytics and Construction/Civil Engineering together was discomforting like comparing apples to oranges. According to Forbes, “Number crunching has always been a big part of construction – a commonly heard phrase is that construction companies are accounting companies which happen to erect buildings. It’s an industry where 35% of costs are accounted for by material waste and remedial work. So counting the cost of every screw could be the difference between delivering on budget and bankrupting an organization (or several organizations) financing a build.”
While advanced analytics and big data analytics are tightening their grip on this sector, this presents a golden opportunity to all professionals in the construction industry to upskill themselves for successful careers. A complex web of 2D, 3D data, financial and corporate data, documents, construction parameters, complex processes, and external factors like weather, etc. makes it challenging as well as rewarding for professionals to solve some of the biggest problems faced in the construction industry with so much data at their disposal.
Great Lakes PG Program in Business Analytics (PGP-BABI) helps candidates in the construction domain to apply analytical tools and techniques to various aspects of their job by enabling them to think from a business viewpoint. Our capstone projects are done in various domains ranging from Information Technology to Healthcare to Agriculture and participants are assisted in carrying out their research and resolution to a complex problem in the domain of their choice. Textbook examples are replaced with real-world business problems that enable participants to relate and comprehend the need to look deeper into a problem from different domains moving beyond just tools or programming languages. If you are a professional working in this domain, PGP-BABI can prove to be a game-changer for you.
For professionals unfamiliar with the wonders of big data analytics and its application in the construction industry,
3 Ways to apply Analytics in the Construction Industry
- Predict and Proceed – Predictive Analytics helps you assess and improvise on work breakdown structures (WBS) as you are able to match the right people to the right tasks at the right time. Every task can be broken down into individual work packages for effectively mapping expectations and quick implementation. Furthermore, previous WBSs and project documentation can help recognize roadblocks, wrong steps, and corrective measures by measuring skills deployed against the work carried out, project-critical tasks against deadlines, and developing learning programs that help staff upskill without compromising BAU activities. Cost-effective resource allocation and work delegation using advanced analytics can resolve several issues that delay completion and design recommendations in future have a better chance at approval.
- More and More Insights – The biggest advantage of applying big data analytics in any domain is that dormant unused data can also be analyzed to draw relevant insights. No information in unstructured data gets underutilized this way. Predictive analysis can use upstream operations data to see if your bottom line is in place. Multiple scenarios based on the likeliness of a project can be used to draw estimates and come up with categories to define optimistic and pessimistic estimates. Parameters like duration and feasibility can help assess the readiness of a construction project and reporting can be carried out at a granular level. One other major differentiator on your project portfolio becomes risk-mitigation as big data analytics can help you recognize projects that have a high risk of losing steam.
- Descriptive, Predictive, Prescriptive Analysis – Descriptive analytics creates a master database comprising of failure points from previous projects along with their severity and triggers. Predictive analysis prevents these failures from affecting future projects by reducing their recurrence and impact. Furthermore, it also reduces bench-time drastically when certain human or skilled resources are not available for carrying out a specific task. Digitization of records helps centralize all data to find relevant information and insights from time to time. A combination of predictive and prescriptive analysis can take care of health and safety concerns by diverting workforce from high-risk site disasters.