Construction projects can generate valuable data that, if properly gathered and analysed, could bring enormous benefit to a construction company. Therefore, it is essential for all stakeholders to coordinate on a comprehensive data management strategy which will guide them in collecting information from the site of operation, analysing its significance and then communicating those findings with the key decision makers.
Gathering and analysing data from their projects can help construction businesses, contractors and suppliers:
- Increase their productivity
- Improve profit margins
- Improve bid accuracy
- Predict problems before they occur
Companies that fail to leverage the data available are missing out on potential profits. Even small-scale contractors can benefit from collecting and interpreting data, so it is essential for all companies to focus their efforts on gathering information that will be useful in driving their business forward. The key points a company should analyse changes over time – e.g., initially analysing costs then moving onto productivity once these have been optimized; but with the help of insightful data, businesses can always have visibility on where to begin making improvements.
How to develop a successful construction data strategy
Developing a successful construction project data strategy involves leveraging the power of construction management and predictive analytics. By collecting data from each stage of the project, it is possible to analyse and identify patterns and trends that can be used to inform decision-making and improve efficiency.
Data collected during the pre-construction phase should include information about project scope, construction methods, materials to be used and estimated costs. During the construction phase, data should include information about labour hours, materials management and progress tracking. The implementation of a dashboard that tracks all data can help ensure accuracy and up-to-date information for stakeholders to access.
Developing a data strategy is typically a multi-phase process:
1. Identify the objectives
For a successful data strategy, the initial step is to identify what challenges you are trying to address. Although there might be more than one issue that needs resolution, it is recommended not to focus on more than three major areas concurrently. For example, construction companies may prioritize costs or productivity first since these factors directly influence job profitability.
Companies should involve all of their employees in the process of leveraging data to identify and solve problems. Project managers may be looking for ways to reduce costs or increase productivity, while superintendents want more insight into job site safety – it is important to get input from everyone involved before deciding which objectives need addressing during initial data analysis. Additionally, objectives should be revisited frequently as new insights come up with further analytics.
2. Determine data requirements
Once you’ve crafted your data strategy goals, it’s time to understand which types of information are needed and how to obtain them. Your approach for attaining the required data will vary depending on each pre-defined goal.
Cost data should be obtained from your accounting software, while productivity can be gathered from individual timecard entries. If the two programs are integrated, you may get all the info in one source. But if not, there’s a need for companies to understand how to access and compile information into one place for evaluation.
3. Ensure compliance with local data governance
Data governance is a set of processes and practices that ensure the efficient, effective, secure, and compliant use of data. It helps organizations create construction projects that are better managed and more predictive in nature by taking into account the changing landscape of construction data. By implementing principles such as accountability and transparency, Data Governance allows organizations to make informed decisions about how construction data is used. In turn, this facilitates more effective predictive analytics and better construction management outcomes. Data governance can include, but is not strictly limited to:
- Data quality
- Ethics
- Privacy
- Access
- Security
To protect yourself from a potential data breach or loss, it is critical to prioritize the procurement of high-quality information that is securely stored. All contractors must be mindful when dealing with data quality issues; thus, your data strategy should take into account steps and procedures that help guarantee precise and current insights before you dive into analysis. Knowing when to expect up-to-date info will prove instrumental in ascertaining the best time for conducting an analysis.
4. Find the right technology
Choosing the right technology, tools and platforms for transforming construction management into a data-driven strategy can be overwhelming. However, with careful consideration, identifying the best options to ensure success is possible.
First, you will need to assess your current construction management processes in order to identify gaps where improved automation and analytics could enable more efficient and effective construction management. Then, you can begin to identify the most suitable technology for your needs.
When selecting technology for construction data and predictive analytics, it is important to determine what type of construction data will be collected, how the data will be managed and stored, as well as which types of analyses can be performed with the available tools and platforms. It is also important to ensure that the construction data platform is secure and robust enough for your specific needs. Additionally, consider any existing platforms you may have in place and are compatible with the technology you intend to use.
Finally, when evaluating any potential technology or tool for construction management transformation, assess its scalability and ability to integrate into all existing processes. A flexible and easily adaptable construction data platform can help to reduce implementation costs and ensure a smooth transition, while helping your organization remain competitive in the digital age.
5. Examine existing skills and capacities
Following this, you’ll want to carry out a practical skill and capacity assessment for your team. Take an honest look at where assistance is needed in carrying out your data strategy. It’s possible that while your team may be adept at gathering information, they don’t quite know how to interpret it. Alternatively, maybe they can assemble and examine the data but struggle with delivering its contents in a comprehensible way for all users.
After you have determined what areas of your business need improvement, training or outsourcing can help bridge those gaps. Perhaps you would like to provide extra software education for your employees in order for them to effectively complete the analysis that is necessary. On the other hand, it may be more beneficial to outsource data analysis completely so that your team can direct their energy towards gathering better information and performing tasks based on results produced from this information.
6. Implementation and management
Before implementing your data strategy, anticipate any potential roadblocks and develop plans or procedures to counter these issues. By preparing for problems before they arise, you decrease the risk of slowing down progress. Your team will feel confident knowing how to tackle a challenge if it does occur – allowing them to act quickly and continue with the implementation process.
After the data strategy planning is complete, you are now ready to begin implementing it. Ensure that your adaptation process remains adaptable and be prepared for any roadblocks which arise during implementation. The more data collected, the deeper insight can be gained into how your company functions- use this knowledge to further improve processes and track progress so positive effects of implementation can continue being seen.
Conclusion
With data often being sparse and poorly organized, construction companies have been left with limited visibility into their projects. However, having abundant and specific data about your business operations can bring massive benefits to you. That’s why it is essential to invest in a software system that allows for customizing the information you need to assess and analyse as your organization progresses in its use of data.
By collecting and analysing data throughout the project cycle, it is possible to gain insights that can be used to inform future projects. This includes predictive analytics, which can help identify potential issues and areas of risk before they become major problems. Additionally, data analysis can also be used to optimize the project timeline and improve efficiency.
By leveraging construction management and predictive analytics, it is possible to develop a successful construction project data strategy that can improve the accuracy and efficiency of the construction process. Through careful planning, collecting data throughout each phase, analysing it for insights and using predictive analytics to identify potential issues, it is possible to create a successful construction project data strategy. This can lead to improved quality control, cost savings and better overall results for stakeholders.
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