Construction isn’t making the most of the data it generates, argues Sir Robert McAlpine head of data and analytics Gareth Parkes. Here he discusses the problem and the ways forward.
As well as his role at Sir Robert McAlpine, Parkes is also a director of the Construction Data Trust. BIMplus has spoken to him before about construction’s data problem, in particular the role of the data trust. But here we delve more into the industry’s issue and the importance of the Project Data Analytics Taskforce’s manifesto.
The draft manifesto, published last year, has six pledges:
- We use data analytics to bust project management myths and beliefs;
- All projects are data designed and enabled;
- We pool our data to maximise insights;
- We collaborate on opensource data analytics solutions tackling priority challenges;
- We reskill for a digital and data-enabled world;
- Data Analytics is codified in all aspects of project delivery best practice and culture.
BIMplus: Construction has a data problem – why?
Gareth Parkes: The reality is that it’s more of a sector-wide societal problem we need to tackle.
“Data and analytical tools can assess huge amounts of data and provide project managers with a clear picture of how best to tackle a problem.”
Construction is in a state of immaturity when it comes to digitisation. Even when vast amounts of data are collected, not enough time is spent analysing it and understanding what it’s trying to tell us.
Essentially, this data is just not being used, which results in a cyclical problem: data isn’t used, and so people get frustrated at having to collect it, in turn making the quality of the data worse – and so on.
This cycle is further perpetuated by the fragmented nature of the supply chain. Contractual arrangements can make it difficult to understand ownership of data and to share data across project boundaries. And often what gets shared is a summary of a summary rather than the source data – an approach that only contributes to the perception of construction’s data problem.
What do you see as the key negative impacts of under-used data?
Construction has lagged the UK’s productivity average for decades. A lot of this is because of the amount of manual labour involved in the industry, but also the lack of digitisation. Productivity is a people and value calculation and as such, through more efficient use of data, you can streamline workflows and improve project delivery, thus increase productivity, and unlock new opportunities for UK plc.
Data is also key from the joint perspectives of tackling our climate and nature emergencies, helping us find more effective and efficient ways of reducing our impact on the planet. Underusing it makes achieving our net-zero targets and environmental commitments much harder.
We should also be considering health and wellbeing of the whole workforce. Mental health issues in the sector have been well documented, and the number of lives lost on construction sites annually has ranged between 31 and 47 for the past five years. There’s also a problem with long-term sickness and physical health – repetitive tasks, manual handling, noise, vibration are all physical risks. Using technology that already exists to collect and properly analysing data around these risks could unlock the key to improved health, safety, and wellbeing – both mentally and physically.
“The fear of the unknown, misplaced concerns, unfamiliarity all lead to people unconsciously or consciously avoiding using data.”
What are the key barriers preventing the greater use of data in the sector?
What the sector really needs is a mindset shift. For too long, there’s been a culture of people relying on instincts and experience to make decisions. But as projects become increasingly complex this isn’t possible – there are too many variables to consider and calculations to make.
This is exactly what data and analytical tools excel at. They can assess huge amounts of data and provide project managers with a clear picture of how best to tackle a problem. But cultural challenges are blocking progress. The fear of the unknown, misplaced concerns and unfamiliarity all lead to people unconsciously or consciously avoiding using data.
I’ve already mentioned the challenges of poor data quality, which leads to lack of trust in the data. Lack of trust in what people will do with it means a lack of access to data. These cultural barriers are also the most difficult to overcome and the solution is to create the right environment.
What is the Project Data Analytics Taskforce hoping to achieve with its draft manifesto?
The Taskforce is trying to work out how project data analytics can help improve project performance. As it stands, the probability of a project being delivered on time and on budget is 0.5%.
With the manifesto, we are trying to show project professionals why data needs to be routinely collected and a project’s performance consistently analysed as part of an operational process rather than when it’s convenient. The data is there, we just need to establish mechanisms to allow us to understand it – more often, and from the outset.
“We need to establish mechanisms to allow us to understand data – more often, and from the outset.”
How can firms work towards the manifesto’s goals?
Each of the manifesto’s six pledges is an opportunity for companies to identify and work out what needs to be done internally for their organisation to improve its use and collection of data. Ultimately, the pledges are a means to accelerate a company’s existing goals and objectives.
What are your top tips for firms to implement better data handling?
Test and learn from each iteration of data initiatives – don’t try and do too much all at once. Have a vision of what you’re trying to achieve and a roadmap for where you’re heading that links back to what value you can add to your organisation. And throughout the process, always ask yourself how and where is value being added through this data.
How can we ensure this is a future across the sector and not just for a few forward-thinking firms?
Cooperation and collaboration are key – and collaboration on a really granular data level. Why would multiple organisations all collect exactly the same information for a particular project when we can equip one organisation to collect it, and then share it with the others?
By streamlining data collection in this way, you can vastly improve productivity, as well as significantly increase the quality of data collected. This means everyone can sit on the same side of the table, critiquing the data together and learning from the same base point.
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