Data analytics is changing the world by helping businesses get deeper insights into operational aspects, understand consumer behavior, improve competitiveness, drive efficiencies, and ultimately, boost revenue. Data cleansing companies play a crucial role helping businesses prepare, transform and consolidate data so that it is easily and quickly available for analytics. Many industries have been impacted by big data analytics and the construction industry is one of the latest entrants in the list.
Big data refers to the huge volumes of information that accumulate on a daily basis. For businesses, it includes data from customer databases and emails, e-commerce and omnichannel marketing, connected devices on IoT, images, social network posts, log files, sensor data, and much more. Data analytics involves working on this raw data using specific processes and tools to gain insights from it and make informed business decisions.
With the use of innovative technologies, the amount of data generated by the construction industry has increased significantly. Though the volume of data may be less in comparison to the retail or banking industry, data in the construction industry is unique in that it comes from a variety of sources such as:
- Drawings, design, building information modelling, and other construction aspects
- On-site workers
- Material supply chains
- Cranes, bulldozers, earth movers and other heavy equipment
- Utilities and building services
- Infrastructure and building management systems
- Maintenance and replacement systems
- ICT systems and equipment
- Purchasing network
- Performance reporting and work scheduling
- Operational cost management and monitoring
There are many more kinds of data generated during the entire life cycle of a construction project. Analyzing and extracting insights from this data is crucial to assess performance, allocate resources, help solve problems, optimize processes, and make evidence based decisions in construction.
Managing large construction projects is extremely challenging. They are often not completed on time, overrun the budget, and are poorly implemented. Common challenges in the construction industry, according to Deloitte, include high failure rates of large projects, repetitive reporting, multiple data systems, unprioritized and unorganized data, largely reliant on paper based reporting, and lack of connectivity.
A Sage survey of construction companies found that:
- 57% want consistent, up-to-date financial and project information
- 48% want to be warned when specific situations occur
- 41% want forecasting, allowing them to better prepare for best and worst-case building events, and
- 14% want online analytics to see for instance precisely which factors are affecting profitability and by how much
As the volume of big data in construction grows, it’s becoming increasingly important to get actionable information from it. By collecting and analyzing data, analytics can convert information into insight, enhancing decision-making and improving overall project delivery.
Here are the various ways the construction industry using big data and data analytics:
Improving Onsite Workflow and Productivity: Analyzing data collected from jobsites can provide valuable information that can be used to improve work processes, identify methods to automate workflow, reduce costs, and more. In addition to traditional tracking of cash flow and costs, data analytics can provide information about employee movements. In one case, this information was used to optimize placement of materials and equipment, which saves time and money (viewpoint.com).
Tracking Construction Equipment Performance: The construction industry uses wide variety of tools, vehicles, and equipment. Physical assets also include premises. Construction assets management and tracking involves the use of various tools for tracking the performance of assets and equipment of all sizes. If assets and equipment are not utilized to their full potential or sit idle, it can be very costly and wasteful. Tracking construction equipment can help determine:
- When heavy machinery is operating
- If unauthorized usage is occurring which can help prevent theft and equipment loss
- Wear and tear and ensure the equipment is properly maintained. This can help manage costs and increase equipment lifespan
- Whether equipment needs to be replaced or not
Tracking construction equipment and assets can maximize utilization.
Mitigating Risks: Collecting and analysing data from construction projects can provide information about potential risks and problems. For instance, data analytics offers the ability to identify problems related to the material supply chain, unforeseen weather conditions, and productivity of labor and equipment. This will provide useful information about possible project delays, project time, and budget overruns. Combining project-related information with business data can help identify both positive and negative trends. These interpretations can help reduce risks and promote effective project management.
Increasing Worksite Safety: Work on construction sites comes with safety hazards such as slips, trips, and falls, being caught between and struck by moving objects, electrocutions, airborne fibres and materials, and working at heights, to name a few. Tracking and analyzing data related to safety issues can help pinpoint high-risk activities and risky conditions can help contractors take steps to reduce such risks and prevent future incidents.
Enhancing Decision Making: The results of predictive analysis can be used to improve decisions making. Rhumbix defines predictive analytics as “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”. predictive analysis is being used in construction to understand the factors that can affect project profitability and timeliness. By keeping track of expenditure, income, scheduling problems and other critical data and analyzing it, construction companies can recognize potential issues before they occur and develop fitting solutions.
Regardless of the industry, properly preparing data for data analytics is essential to glean useful insights from it. Raw data needs to be sorted, structured, reformatted, and cleaned. This would involve everything from data entry and document conversion to coding, editing, and even transcription. Back office outsourcing support is the best way to get data processed efficiently and in a timely manner. A reliable company can help transform large volumes of raw data into quality information that can be used for data analytics.