Big data and AI: the building blocks of the future of engineering

By Jon Horden

Construction is one of the largest industries in the world, employing around seven percent of the world’s working age population. At this scale, it is no surprise that the sector generates huge volumes of data on a daily basis; from field data captured by drone, to reports and contracts, data is continually being gathered from multiple sources but is frequently unstructured and difficult to access. Through necessity, contractors, engineers, and suppliers have pivoted to working and collaborating digitally – using video calls to host meetings, site visits, and transact business, further increasing the amount of data being generated. However, 96 percent of data from infrastructure projects is not used and 90 percent of engineering and construction industry data is unstructured. 

Big data – extremely large, unstructured datasets that can be analyzed by computers to reveal patterns and trends – is one of the most valuable commodities in the construction industry, enabling firms to improve cost effectiveness and operational efficiencies, while reducing business risk. In fact, analysis of big data increases a business’s chance of making better strategic decisions by 69 percent, so the desire to harness the power of big data is understandable – but the question is how?

One of the biggest challenges facing today’s firms is accessing the data they already possess, to gain valuable business insights. The industry remains one of the least digitized sectors globally, partly because of the fragmented nature of its projects involving an array of contractors and subcontractors. The problem of accessing siloed data points is exacerbated by the diverse computing systems used by construction firms. There is a clear opportunity for organisations to use Artificial Intelligence (AI) technology to process the unstructured data stored on legacy systems, collating it to enable users to discover knowledge from many sources across the organisation, even different geographical locations. 

Commercial and procurement teams are routinely hampered by the limited visibility of information and, additionally, lose a large percentage of their working day searching for the information required to perform their role effectively. With the industry facing increasing labour shortages and the need to improve profitability and cost efficiencies, now is the time for companies to prioritise adopting AI solutions that can be used to discover data already in an organization’s possession. By harnessing the data that is already available, but currently undiscoverable, firms will be able to deliver improvements in the productivity, procurement, and planning associated with a construction project. 

Productivity

Skilled labor shortages, which were a challenge before the pandemic, will potentially become even more acute according to models developed by the Association of Builders and Contractors. More than 650,000 workers will be needed in addition to the usual quota to meet the demand for labour in 2022 alone. This is where knowledge management software, underpinned by AI, can offer real advantages, thanks to its ability to break down data silos and process information quickly and with extreme accuracy, enhancing resource productivity. 

Preparing information for search access is time-consuming. Traditional methods require investing significant amounts of human time and labour to interpret, label, and rank data, ready for inclusion in a search database. It is impossible to pre-empt every keyword and synonym that may be used and documents will require multiple labels to prepare for all eventualities that cannot be preempted. This leads to irrelevant returns when using traditional search methods. 

There has also been a huge increase in the use of email, video chat, messenger services, and other non-traditional channels for dispensing advice and information which is not readily accessible for collation and indexing. Capturing and reusing information conveyed via email, instant message or even video call transcripts is even more complex than with traditional documentation. Accessing knowledge created in MS Teams, for example, is challenging, especially since one meeting can cover multiple topics around a project. Adding to the challenge, all of these communication tools – iManage, email, MS Teams, Sharepoint – have different search interfaces, which require multiple and repeated searches to find information.  

With no mechanism for easy discovery, the majority of current retrieval systems rely on keyword searches, which enables users to combine words and modifiers to retrieve relevant information, but these often yield irrelevant results as the search parameters are so large and there is a lack of context. 

AI technology can overcome this by bringing all of these sources of information together in one place, indexing and segmenting the knowledge created, and allowing it to be discoverable. Using AI-enabled technology allows a richer input of information as a query, including dragging and dropping full documents into the insight engine – which means results are more accurate, relevant, and refined. This is particularly useful for engineers working on projects where there are potentially thousands of different results per keyword. The context enabled by AI data discovery refines the returned results to a manageable number that can easily be reviewed and utilised to avoid a loss of productivity. 

New cloud-based software solutions, such as those offered by iKVA, can instantly be implemented and integrated with existing workflow systems to relieve the strain on resourcing issues. iKVA’s solutions, which harness AI, Advanced Machine Learning, and vector mapping technology, enable construction organisations to leverage the unstructured data being generated from multiple sources to reduce wasted time and improve profitability. This avoids sourcing highly-specialised employees as well as costly upgrades to the legacy systems which are still frequently used across the industry.   

Project bids

It is no surprise that 57 percent of construction companies want access to consistent financial and project data. More competitive bidding environments in a fragmented ecosystem can be challenging for contractors to navigate and firms often need to decide whether to bid on a project with incomplete information. Historically, organizations have relied on individual knowledge and experience, which is valuable, but may be subject to unconscious bias or affected by individual motivations. With projects of five to 10 years lead time, it is difficult to accurately predict scope, complications and market shifts; misjudging financial risks can have a significant impact on a construction firm operating at, say, 7 percent margin, in an industry with an average bid success rate of 25 percent. 

AI tools can discover siloed information from historical projects to provide data that commercial teams can harness to make informed decisions to balance portfolios and calculate accurate contingency levels. By enabling access to data that may not traditionally be easily available, such as historic contracts, workforce capacity, and geographical average spend, teams can uncover variables that may predict project profitability and enable more efficient and successful tendering. 

Procurement

The subcontractor procurement process can be a lengthy one, with multiple specialists involved and a lack of empirical evidence to inform outcomes. Adopting AI technology can reduce the time and labour intensive process from weeks to days, and avoid any element of unconscious bias in the process. In an era where sustainability continues to dominate the agenda, organisations in the construction industry can also use AI technology to visually map projects against criteria, such as the United Nation’s (UN) Sustainable Development Goals, to identify areas of alignment and whether a project will further the organization’s social and environmental objectives.  

The engineering industry can use AI-based technology to build partnerships to solve the challenges that its clients, and the wider engineering sector, are facing. With the help of iKVA’s knowledge management technology, large engineering and infrastructure organizations can compare and map out the content of pitches they receive against a framework that is tailored to incorporate their overall strategy and goals. This means that internal and external stakeholders are instantly able to assess the relevance of a subcontractor for future investment or partnership. By visually mapping unstructured data from different locations, large firms are able to  identify similarities between new and existing datasets, which allow them to quickly and easily navigate emerging engineering technology solutions offered by a range of potential partnerships. A further benefit of the technology is that it enables a variety of organisations to anonymously submit pitches through the portal, which helped to remove any element of unconscious bias when reviewing submissions.

It is clear that this type of tool has wider applications in the industry to create connections between contractors and subcontractors efficiently, accurately and without bias. 

Conclusion

Thanks to advances in AI technology, big data can now benefit construction teams from the earliest stages of project development by enabling commercial and procurement teams to identify opportunities to successfully secure new project business, improve time and labour intensive review processes and reduce business risk at every stage of a project. 

Jon Horden is the CEO and founder of iKVA, which develops AI-enabled knowledge management software for organizations.