Gareth Parkes is Head of Data and Analytics at Sir Robert McAlpine
Construction has a long-established data problem, and while recent years have seen significant improvement, we are still lagging behind.
“Data is a consistently undervalued cog in the construction machine”
Industries like manufacturing have hundreds (sometimes thousands) of sensors that generate performance data, optimizing control and maximizing performance. However, the average construction project dashboard includes 10-20 data points reported well after the end of the month. A recent report by the Royal Institution of Chartered Surveyors even went so far as to claim that 95% of project data is not actually used during construction projects. The world is being driven by a “big data revolution,” but it still feels like we’re planning our first protest.
Fortunately, corners of the industry are still moving forward. Earlier this year, the Project Data Analytics Task Force drafted a manifesto based on six promises to increase efficiency and use of data within the construction industry. These include using data analytics, rehabilitating the digital and data world, aggregating data to maximize insights, and collaborating on open source data. Every player in our industry would be wise to take note. If companies don’t join the data revolution, they will be left behind.
Where is the profit?
In the construction industry they tend to have high expectations. As part of the government’s deal for the construction sector, we are currently striving to achieve the target of projects being completed 50% faster by 2030. However, there appears to be a disconnect between our ambitions and our reality, with a recent study finding that only one in 200 projects (0.5%) met their goals in terms of budget, time and profit.
While we can’t just pin this disparity on data underutilization, we can say with some degree of certainty that data inefficiency will contribute significantly to it. Data is our greatest weapon and our greatest weakness as we strive for efficiency. Exploiting structured, uniform and high-quality data in an intelligent and forward-looking manner will be key to maximizing project efficiency and delivery.
For example, taking a collaborative approach (as opposed to the lone wolf tendency of construction companies) by sharing data with external organisations, key stakeholders and internal channels will avoid miscommunication and provide more insight. By enabling access to a broad set of data and the integration of teams at all stages of project development and implementation, we will streamline efficiency and improve productivity.
Previous project data can be used to create learning legacies, which can be used to isolate the location of carbon emissions in supply chains, while also informing emissions problem solving. Data analysis can inform companies about adverse external risks to a project or the potential impact of socio-economic trends. The opportunities are endless.
From promises to reality
We need a fundamental change of attitude in the way construction companies operate. Organizations must move away from viewing projects as temporary initiatives with manual data collection and analysis followed by rapid data disposal, to a long-term vision of improving operational efficiency. Companies need to incorporate the use of data at all stages of the project until it is a routine process.
While collecting and analyzing this data can be a seemingly formidable task for companies inherently stuck in the old mindset, ensuring that companies are equipped with data-centric habits and analytics tools correct digital to optimize its use of data will be crucial to achieve the vision of the manifesto.
Data is a consistently undervalued cog in the construction machine. However, by adopting and leveraging digital innovation, organizing and storing data effectively, organizations can save time, resources and can monitor their projects in real time. With the manifesto expected to be released in the coming weeks, it’s finally time for our industry to draw a line in the sand and push towards a more data-efficient future.
