The construction industry is a complex and multifaceted field that requires the use of large amounts of data to support a variety of processes and decisions. From project planning and design, sourcing materials and workforce to operations and maintenance, data plays a critical role in ensuring the success of construction projects. However, the quality of this data can have a significant impact on each area and ultimately the overall outcome of a project. In this article, we will explore some of the benefits of improved data quality for the construction industry.

But first, what is Data Quality Management?

Data quality management is the process of ensuring that data is accurate, consistent, compliant, available, reliable and trustworthy. This includes identifying and correcting errors, inconsistencies, and missing data at source, ensuring all data held is compliant to regulatory and company policies, as well as implementing processes and procedures to prevent these issues from occurring in the future.

Until recently ensuring the quality of data meant employing a team of people to manually fact-check all the data records on a daily or weekly basis, but as the volume, variety, velocity, value and veracity of data sets increase, this becomes less practical, scalable and sustainable.

Many companies are starting to use automated tools to help with the problem. Software tools that provide an initial data assessment provide sound ground for companies to identify what their data estate looks like and where they should be focusing efforts to cleanse the dirty data and subsequently put processes in place to ensure that new data flowing into the business is cleansed and conforms to the new data DNA policies.

To maximise the potential of today’s data quality tools we need to first understand the six dimensions of data quality. Embracing these as the foundations of your data policies will not only boost the quality and performance of your data, but also reduce costs, improve performance and customer satisfaction, help expedite projects and sales and reduce compliance risk and associated reputational damage.

What are the six dimensions of data quality?

• Accuracy is the degree to which data correctly describes the “real world” object or event.
• Completeness is the proportion of stored data against the potential of 100% compete.
• Consistency is the absence of difference, when comparing two or more representations of a data asset against its definition.
• Validity is data that is valid if it conforms to the syntax (format, type, range) of its definition.
• Timeliness is the degree to which data represent reality from the required point in time.
• Uniqueness is that no object will be recorded more than once based upon how that object is identified.

How do we improve the quality of our data assets in today’s data driven world?

Firstly, we need to develop and implement clear guidelines for data collection and management. This will ensure that your data is consistently collected and organised in a way that is both accurate and easy to use. We then need to regularly clean and validate your data. This will help to identify and correct any errors or inconsistencies in your data and will improve its overall accuracy and reliability. Use established industry methods for analysing and interpreting your data. This will help to ensure that your conclusions are based on sound statistical principles and are not subject to bias or other errors. Put policies in place where the data is regularly reviewed and updated as new information and data sets become available. It is important to incorporate new data sources it into your data assets in a way that maintains their accuracy and relevance. Invest in tools and technology to support your data management policies. This can include data management and visualisation software, as well as hardware and infrastructure to support large-scale data processing and analysis.

What are the benefits of Data Quality Management?

A key benefit of data quality management within the construction industry is improved project planning and budgeting. The construction industry is increasingly using technology such as Building Information Modelling (BIM) and other digital tools to improve project planning and execution. However, these tools are only as good as the data they are based on. Garbage in, garbage out as they say. Accurate and reliable data is essential for creating realistic project schedules and budgets. Without it, there is a risk of underestimating the resources and time required to complete a project, which can lead to delays and costly overruns. By implementing data quality management processes, construction companies can ensure that they have the most accurate and up-to-date information available, which can help them make more informed decisions about project planning, budgeting, workforce management and help mitigate hazards such as the risk of accidents and injuries and avoid errors and inaccuracies in the final project.

Another benefit of improved data quality is that it can lead to increased productivity and efficiency during the construction process. When data is accurate and up-to-date, it can be used to streamline construction processes, such as by identifying bottlenecks and inefficiencies in the workflow. Improved data quality can also help to reduce the risk of errors and rework during the construction process. When data is accurate and complete, it is less likely that errors will occur, which can save time and money by reducing the need for rework.

Finally, improved data quality can also lead to better operations and maintenance of the completed construction project. When data is accurate and up-to-date, it can be used to create more effective maintenance schedules and to identify potential issues early on, which can help to prevent costly repairs and downtime. Additionally, accurate data can be used to optimise the use of resources, such as by identifying where energy or water can be conserved.

In conclusion, improved data quality can bring numerous benefits to the construction industry. By investing in data quality, construction companies can improve the outcome of their projects and ultimately, the overall success of the industry.