Before I start, I want to thank Darien Liu, Principal Software Engineer at Mott MacDonald, for agreeing enthusiastically to collaborate on this short piece. It is always a pleasure to share thoughts with you!
Excel spreadsheets are still widely used in the Architecture, Engineering, and Construction (AEC) industry despite the availability of advanced digital tools that ensure better data quality and accuracy. This creates an interesting paradox: while Excel is easy and familiar, nowadays, it’s used for everything from cost estimation and project scheduling to quantity takeoffs, resource planning, and engineering calculations. However, relying on it too much often leads to errors and inefficiencies that can derail projects.
This isn’t an article to bash Excel—we’re not here to start a fire. It’s about recognising that just because you can do things with it doesn’t mean it’s always the best tool for the job. And yes, we get it—sometimes, or maybe even most of the time, you don’t have any other options.
The Convenience Traps
Spreadsheets are intuitive, and when paired with 3D visualisation capabilities, they can seem like a quick fix for many tasks. But is this really a viable option for large-scale projects? Just because Excel is now cloud-based, does that mean it should be the infrastructure for developing tools? Not exactly.
Engineers aren’t always concerned with UI/UX—they’re focused on getting things done with the data they have on hand.
This is where the UI challenge comes into play. Excel as a user interface might seem convenient based on the column row rules, but it was never designed with user experience in mind. When we start using Excel as a UI, we limit our ability to scale and improve. Let’s be honest—sometimes, using Excel isn’t about choice but necessity. IT roadblocks often push engineers into using Excel for things it wasn’t meant to handle. Even developers don’t always have access to the IDEs and packages they need, so expecting “citizen developers” to do better with limited resources isn’t realistic. But just because you can create interfaces in Excel doesn’t mean it’s the right solution.
And then there is the data challenge. Engineers like to tinker, and Excel allows quick updates and easy data manipulation. However, this flexibility also becomes its biggest weakness in maintaining data accuracy. Excel is not a database. Manual data entry and updates are prone to human error, leading to inconsistencies and inaccuracies that can seriously affect project outcomes.
These challenges generate the issue of prototypes. Many of these Excel-based solutions start as “just a prototype,” but without a clear framework to evolve them into real, scalable solutions, they stay stuck in prototype mode forever. The result? It is a quick fix that turns into a long-term headache.
Do you really, sincerely, think that you’re only going to use it just this once?
These quick-fix Excel solutions will keep multiplying without a consolidated way of translating prototypes into effective distributed internal solutions. Every project will end up with its version—slightly different copies, tweaked by various teams across different locations. Instead of solid solutions, you’ll have a dozen variations scattered across the company and stored elsewhere.
Relying on Excel for both UI and data management becomes more of a risk than a solution. UI and data challenges reinforce each other, creating a cycle where convenience leads to inefficiency.
The cost of inaccuracies
Studies have shown that approximately 90% of construction management’s large spreadsheets (more than 150 rows) contain significant errors.
In an industry where accuracy and teamwork are crucial, Excel’s limitations become more apparent as complexity grows, leading to a careful review of its usefulness and the quest for better-suited solutions. There are so many learnings we can take from other industries that process data more effectively—a quick look at any SaaS architecture or event-driven system shows us that we can handle transactional data properly and track the status of data as it flows from source to report.
This leads to issues like:
- Project delays: Misinterpreted or incorrect data can cause delays in project timelines.
- Increased costs: Errors often result in rework, which increases project costs.
- Poor decision-making: Decisions based on flawed data can lead to suboptimal outcomes. Engineers often neglect to include proper data validation in their spreadsheets, increasing the risk of inaccurate results.
- Lack of version control: Excel files can easily become outdated or corrupted, making it difficult to track changes and maintain data integrity over time.
- Scalability issues: As projects grow in complexity, Excel workbooks can become unwieldy and difficult to manage effectively.
A few months ago, an article highlighted how the Williams Formula 1 team ran into serious inventory management issues because they relied too much on Excel. This sparked a lot of discussions, with people arguing for and against the use of Excel. What’s interesting about this case is how the team decided to fix the problem. They spent “millions of pounds of cost cap money” to completely revamp their processes, systems, and structures. While that might seem like a lot of money, it was seen as necessary to overcome the challenges they were facing. It wasn’t just about getting rid of Excel—it was a full-scale transformation to modernise the way they worked. Along with the new tech, the team also went through a big cultural shift, focusing on becoming more efficient, even though the changes came with a hefty price tag.
Unfortunately, our industry often lacks these shared lessons learned, but more importantly, the capacity to redirect the right capital into proper cultural and process shifts.
Digital solutions: the answer to data quality
In construction, the need for “real-time” data—though in our case, real-time often means days or even weeks, far from the true real-time data seen in industries like banking—has pushed companies to move beyond Excel. We still struggle to achieve this level of timeliness; we can look to other industries that handle real-time data effectively. Banks, for example, manage truly instantaneous data updates, something construction could benefit from as we improve our systems. Digital tools can provide:
- Real-time data updates: Ensuring that all stakeholders have access to the most current information.
- Enhanced collaboration: Facilitating better communication and coordination among project teams.
- Data integrity: Automated processes reduce the risk of human error, ensuring higher data accuracy.
The need for a mindset shift
A shift in mindset is needed for the AEC industry to fully take advantage of digital tools. While Excel is convenient, it’s not the best tool for ensuring accurate data. Teams need to commit to learning new technologies and be open to changing how they handle data. Embracing these changes can help avoid the expensive mistakes we’ve seen in the past.
- Training and support: While training is essential for upskilling teams, the tight deadlines in project delivery mean we need to structure teams differently. They should be supported by Data Scientists, Developers, and Computational Designers who can help engineers turn their ideas into the right solutions. There needs to be someone with experience in managing data (perhaps from another industry) who can guide teams on what’s possible and how to get there.
- Commitment to change: A willingness to move away from traditional processes and adopt more reliable digital tools.
- Data maturity: Developing a mature approach to data handling where accuracy and integrity are prioritised over convenience.
Conclusion
To truly make this shift, internal platform teams must play a key role in identifying the right digital solutions, while product teams must be invested in promoting these tools and supporting their adoption across projects. To achieve the data accuracy and reliability that modern projects demand, we must move beyond the convenience of Excel and embrace solutions designed for scale and collaboration.
So much valuable business logic and data are trapped in spreadsheets, and unlocking this can significantly improve efficiency and accuracy throughout all project phases. However, the real focus shouldn’t just be on unlocking all the data—as many suggest—but rather on asking the right questions first. We should begin by determining what key insights we need and then seek out the best way to collect and manage that data. Simply unlocking all data without thought won’t necessarily lead to better outcomes. The AEC industry often talks about becoming more digital, but it won’t happen if we keep using Excel as a one-size-fits-all tool—especially with patches of Visual Basic code or UI slapped on top to extend its functionality.
Instead of making Excel do everything, we need to adopt well-established tools for databases or coding for more complex calculations. As McKinley points out, the goal should be to optimise globally, not locally, by adopting technology that is stable, scalable, and fit for purpose. This shift isn’t about jumping on the latest trend—it’s about making thoughtful, measured decisions that drive real, long-term improvements.