Construction Rework Is ~9% of Cost—and Over Half Is Bad Data: What Owners Can Do About It

Construction projects have a rework problem – and data center builds are no exception. In fact, studies show rework typically eats up 5–10% of total project costs (www.planradar.com) (around ~9% on average), with over half of that rework stemming from bad data and miscommunication (www.autodesk.com). For hyperscalers and neo-cloud providers racing to build and expand data centers, that percentage translates to tens of millions of dollars and months of schedule slippage on a single project. This blog post dives into why *“bad data” is the hidden culprit behind costly rework, and more importantly, what owners and project teams can do to fix it. We’ll explore how fragmented tools and outdated processes fuel mistakes, and how adopting an integrated, automated approach (with platforms like ArchiLabs) can virtually eliminate these inefficiencies. The goal: help data center teams “build it right the first time” by unifying their tech stack into a single source of truth and automating away the errors.*

The Staggering Cost of Rework in Construction Projects

Rework – the unplanned do-overs in construction – has long been recognized as a major drain on budgets. Decades of industry research confirm that correcting mistakes typically consumes between 5% and 10% of total project cost (www.planradar.com). Even at the low end, that represents a significant hit to profit margins on capital projects. At the higher end (~9-10%), it can wipe out the entire expected profit for contractors and add unbudgeted costs for owners. In practical terms, on a $500 million data center build, roughly $45 million could be wasted on tearing out and redoing work – a jaw-dropping figure for any organization.

This waste isn’t just financial – it wreaks havoc on schedules and delivery timelines. Rework means delays, and delays on complex projects are rampant. It’s no wonder that fewer than 25% of large capital projects finish on time (owner-insite.com). Redoing work extends construction durations, pushing out go-live dates for new facilities. For data center operators trying to meet surging demand (for example, to support AI workloads), such delays can mean lost market opportunities and bottlenecked capacity. In an era where speed to market is critical, schedule slippage due to rework is a silent killer of growth.

Perhaps most frustrating is that rework is largely non-value-added – it’s effort spent because something wasn’t done right the first time. Owners essentially pay twice (or more) for the same scope. In construction, where budgets are tight and margins slim, this level of inefficiency is unsustainable. As one industry publication quipped, for every four buildings completed, “at least one more ends up in the wastebasket” due to error-induced rework (acppubs.com). Put simply, rework is eating away at the productivity gains that modern construction desperately needs.

Bad Data: The Hidden Culprit Behind 50% of Rework

What’s causing all this expensive rework? A growing body of evidence points to one dominant factor: bad project data and poor communication. In fact, over 50% of all rework can be traced to inadequate data and miscommunications on projects (www.autodesk.com). Autodesk and FMI research found that disjointed information – whether it’s outdated plans, incorrect specifications, or messages that never reached the right people – is the single biggest source of construction errors. That 52% of rework equates to roughly 9% of total project cost by itself (www.autodesk.com). In the U.S. alone, it translated to $31.3 billion in rework costs in one year driven by bad data and miscommunication (www.autodesk.com). These numbers underscore an alarming truth: the industry doesn’t have a workmanship problem so much as a **data problem.**

But what exactly do we mean by “bad data” in construction? It’s not just one thing – it’s a spectrum of information issues that plague projects:

• Inaccurate information: For example, design drawings or BIM models that contain errors, or engineering calculations based on wrong assumptions. Build something to flawed specs, and you’ll be ripping it out later.
• Outdated documents: How often have teams built off an older revision of a plan because the latest update didn’t reach them in time? Working off the “wrong version” of drawings or schedules is a classic recipe for rework.
• Missing data or details: Incomplete plans (like missing dimensions or unclear requirements) force field crews to make guesses or halt work to seek clarifications. This slows progress and often leads to mistakes that require fixes.
• Miscommunication between stakeholders: Perhaps the electrical subcontractor wasn’t informed about a late change in rack layout made by the design team, or a coordination meeting didn’t include a critical team. These communication gaps translate directly into things being built incorrectly.

On fast-paced data center projects, these data issues are exacerbated. A change in one system – say a server vendor updates the weight or power of a new rack model – can have ripple effects on floor loading, cooling requirements, and power distribution. If that change isn’t communicated across every tool and stakeholder (structural plans, HVAC designs, equipment lists, etc.), some team will be working with bad info. The result? Maybe the cables get installed undersized for the actual load, or the cooling system ends up under-capacity – mistakes that surface only during testing or operation, forcing expensive retrofits. As an ArchiLabs analysis noted, a tweak that would’ve been trivial early in design can snowball into a millions-of-dollars problem if discovered late in construction or commissioning (archilabs.ai).

Critically, bad data doesn’t just directly cause rework – it also saps productivity in less visible ways. Project teams often spend an outrageous amount of time simply searching for correct information or clarifying uncertainties. One survey found that construction professionals lose almost two full work days each week resolving avoidable issues and hunting down project data (www.forconstructionpros.com). Think about that: in a five-day workweek, two days are wasted on non-productive work caused by disorganized or missing information. This includes tasks like chasing the latest drawings, verifying specs, or sorting out version conflicts – all stemming from fragmented data sources. It’s an enormous drain on human resources and morale, and it ultimately manifests as lost dollars and delays.

The “data fragmentation” problem is deeply rooted in how construction projects have historically managed information. Even today, many teams rely on a patchwork of Excel spreadsheets, email threads, point solutions, and paper drawings – systems that “look like they are out of the 1930s,” as one industry expert put it (owner-insite.com). Each discipline (architecture, engineering, construction, operations) might use its own tools and databases, with little integration. Silos abound: the BIM model might not sync with the equipment inventory list; the commissioning team’s checklist lives in a SharePoint separate from the design documents; field mark-ups on paper don’t get back to the digital model. With no single source of truth, it’s almost guaranteed that some decisions will be made on outdated or wrong information. By the time those discrepancies come to light, rework is already baked in.

Why Data Center Projects Are Especially Vulnerable

Any construction project can fall victim to data issues, but data centers present a perfect storm of complexity, speed, and high stakes that make data fidelity absolutely critical. These projects involve dense coordination among many systems – power, cooling, IT racks, security, fire suppression – each often designed by different teams using different software. The pace is aggressive: hyperscalers and cloud providers are pushing to deliver capacity faster than ever, sometimes compressing timelines beyond traditional schedules. This leaves little room for error; a single mistake in a design parameter can require change-orders and retrofits that ripple across the entire facility.

Consider a typical scenario: A data center design is 80% complete when the owner’s capacity planning team realizes they need to accommodate a new high-density rack type in part of the building. This change means higher power and cooling loads in those rooms. In a perfect world, that change request would seamlessly propagate through all models and documents – electrical plans, mechanical layouts, rack layouts, specs – so that construction executes the updated design correctly. In reality, if the project’s tools are not integrated, such a late change is a nightmare. The electrical engineer might update the one-line diagrams, but the HVAC drawings might lag behind. The BIM coordinator might not catch a clash with the new cooling units. Procurement might have ordered equipment based on the earlier spec. Fast forward to construction or commissioning: the team discovers the cooling system in that area can’t keep up with the heat output of the new racks. The fix? Installing additional cooling units and re-distributing electrical circuits – classic rework that costs huge money and time at the eleventh hour. This kind of late-stage design miss is all too common. As we highlighted above, catching such issues only during build or testing makes a trivial early design tweak turn into a million-dollar change order (archilabs.ai).

Another factor is scale and repetition. Data centers often feature repetitive modules or rows (think dozens of identical server hall layouts). This repetition means a single error in a template design, if not caught, can multiply across many instances. For example, a miscalculation in one typical rack row layout (say the clearance between racks is short of spec) could be copied 100 times across the facility. Discovering that after installation might require re-spacing or moving hundreds of racks – a rework scenario that could derail the project schedule. With AI and HPC (high-performance computing) data centers, requirements can be moving targets (new hardware generations, evolving reliability standards), so designs are more fluid. Keeping documentation and plans continuously updated as things change is an immense challenge with traditional tools. It’s in these dynamic, large-scale projects that having accurate, up-to-date data is literally mission-critical to avoid massive rework.

What Owners Can Do: From Single Source of Truth to Automated Workflows

Rework may be rampant, but it is also highly preventable. Forward-looking owners and project teams are now attacking the root causes – the data silos, the manual processes, the communication gaps – to drive rework out of their projects. In particular, data center builders at hyperscale are recognizing that a relatively small investment in better data management and integration can yield outsized savings in cost and time. Here are some key strategies for owners to reduce rework and build it right the first time:

• Establish a Single Source of Truth (SSOT) for Project Data: The foundation of preventing rework is ensuring everyone is working off the latest, correct information. Owners should insist on a common data environment where all plans, models, schedules, and specs are stored, version-controlled, and accessible to all stakeholders. A reliable SSOT means that when a change or update occurs, it’s made in one central place and everyone sees it. No “secret spreadsheets” or local copies of BIM files allowed – those breed inconsistencies. As Autodesk’s construction blog puts it, having one source of truth is “an absolute must in today’s building industry” (www.autodesk.com). When teams have a shared, trusted data repository, misinterpretation and guesswork drop dramatically. If you’re an owner, this may involve investing in a cloud-based project management platform or collaboration hub that all contractors and designers use. Even simple practices like maintaining a centralized RFI (Request for Information) log and drawing set can pay huge dividends by eliminating parallel, conflicting info streams. The goal is that at any given moment, there’s no ambiguity about where the latest truth lies – be it a model, a spec, or a schedule.
• Integrate Your Tech Stack – Don’t Let Tools Live in Silos: Most data center teams already use advanced software (Revit or CAD for design, a DCIM system for equipment tracking, Excel for calculations, perhaps a scheduling tool, etc.). The problem is these tools often don’t talk to each other. Owners can mandate and facilitate integration between systems so that data flows automatically instead of being re-entered manually. For example, if a change is made in the BIM model (say a piece of equipment is moved or a part number is updated), that update should ripple through to the equipment database, the BOM (bill of materials) in Excel, and even field installation drawings without human intervention. Achieving this might involve using APIs, adopting standards like IFC (Industry Foundation Classes) for data exchange, or leveraging middleware that connects different applications. The payoff is huge: when your tech stack is connected, inaccuracies and omissions are caught early (or avoided entirely) because inconsistencies can’t hide in one team’s silo. The data center industry is starting to embrace this with initiatives around digital twins and model-based system integration, but you don’t need a fancy buzzword to get started – you just need to ensure your critical software tools are sharing data. If they aren’t natively integrated, consider specialized integration platforms or partnerships to bridge those gaps. The fewer times a piece of information has to be translated or re-entered from one system to another, the less chance for error.
• Leverage Automation to Eliminate Human Error in Routine Work: A significant portion of construction errors (and subsequent rework) comes from manual, repetitive tasks where humans are prone to make mistakes or overlook details. Think about updating 200 drawings by hand with a new cable ID, or copying values from an Excel sheet into a CAD annotation – it’s tedious and error-prone. This is where modern AI and automation tools can shine. By automating repetitive workflows, you not only save time, but also ensure tasks are done consistently and correctly every time. For instance, generative design algorithms can lay out server racks or equipment rows following all spacing guidelines and produce a clash-free plan in minutes – something that might take a human days of effort and likely still contain a mistake or two. Similarly, automated routines can generate cable pathway drawings or perform lighting calculations across a whole building without fat-fingering a single value. ArchiLabs is one example of a cross-stack platform enabling this kind of automation for data center projects. It’s essentially an AI-powered operating system for data center design that connects your entire tool ecosystem – Excel spreadsheets, DCIM databases, CAD/BIM platforms like Revit, analysis tools, and even custom software – into one always-in-sync hub. With ArchiLabs, when you change something in one place, every other representation of that data updates automatically in the background, so you’re never working off stale info. On top of that unified data layer, ArchiLabs lets teams automate the heavy lifting in planning and operations workflows. Repetitive tasks such as rack and row layout generation, cable pathway planning, or equipment placement can be handled by AI agents that follow your design rules and standards. Instead of redrawing the same layout variations or manually routing hundreds of cables, you can let the system do it – and do it correctly in seconds. The result is not just speed, but reliability: no more forgotten connections or mis-tagged components due to oversight.
• Deploy AI Agents for End-to-End Workflow Orchestration: Beyond individual tasks, owners should look at automation holistically across the project lifecycle. Modern AI “agents” can be trained to perform complex, multi-step processes that span design, construction, and operations – essentially acting as virtual project assistants. With a platform like ArchiLabs, teams can create custom agents to handle workflows from start to finish. For example, imagine a commissioning agent that automatically generates all the test procedures for a new data hall, then orchestrates the execution: it pulls the latest design data to populate each test (checking specs against as-built values), schedules tests in sequence, records the results, flags any issues against the design criteria, and finally produces a comprehensive commissioning report. All of those steps, which normally involve many hand-offs and separate tools, are managed by one integrated process. Similarly, a documentation agent could continually sync specs, drawings, and operational documents into a single repository, handling version control and ensuring that facilities teams always have up-to-date info post-handover. These AI-driven workflows don’t replace human expertise – they augment it by taking care of the grunt work and cross-checks. Teams can even teach the system new tricks: for instance, an agent could be taught to read and write directly to a Revit model, convert and exchange data in open formats like IFC, pull live data from external APIs or sensor feeds to validate capacity, and then push updates into other systems (like updating a maintenance management system when a piece of equipment spec changes). The key advantage for owners is consistency – when you codify a process into an automated workflow, it will run the same way every time, adhering to standards and catching any deviations. This dramatically reduces the kind of slip-ups that lead to rework. It’s telling that 61% of construction firms report that implementing technology to streamline processes has significantly reduced errors on their projects (owner-insite.com). Automation is a big part of that story. In fact, the World Economic Forum estimates that full-scale digitization in design, engineering, and construction could save over $1 trillion in rework and related costs (owner-insite.com). Owners who embrace these innovations stand to reap massive benefits in efficiency.
• Foster a Culture of Data-Driven Collaboration: Finally, technology alone isn’t a silver bullet – it must be paired with a project culture that values data accuracy and transparency. Owners can lead by example here, by demanding rigorous data practices from all partners and providing training/support to use new systems. Make it clear that decisions should be made based on data, not hunches or outdated habits. Encourage all project members to contribute to and reference the centralized data systems – for example, field crews should report changes or issues through the common platform (with photos, notes, etc.), not via ad-hoc texts that never get logged. Break the mentality of “my data vs your data” between designer, builder, and operator; instead reinforce that everyone is working off the same playbook. Some organizations even appoint dedicated data managers for large projects – individuals whose job is to ensure information is flowing correctly and to troubleshoot any disconnects between systems or teams. When people trust the data they have, they rely on it more, creating a positive feedback loop. Over time, as your single source of truth becomes central to daily operations, you’ll find fewer mistakes to fix because someone caught them digitally or the system prevented them outright. The construction teams of tomorrow will likely include as much IT and data expertise as traditional trades – a sign that maintaining data integrity is becoming as important as pouring concrete on these projects.

The Bottom Line: Build it Right the First Time

For data center owners and developers, the message is clear: rework is not an inevitable cost of doing business – it’s a symptom of broken data practices. In an industry where speed, scale, and reliability are at a premium, eliminating rework through better data management can be a game-changer. By investing in integrated technologies and automation, owners can ensure that their project teams always have the right information at the right time, dramatically cutting down errors and changes. The result isn’t just cost savings – it’s faster project delivery, higher quality builds, and more predictable outcomes. When your design models, spreadsheets, and field notes are all connected and up-to-date, you empower your team to catch issues on a computer screen rather than out in the field with a sledgehammer. Preventing one major rework incident can pay for the software or process change many times over.

Ultimately, reducing rework comes down to a simple principle: plan and execute with accurate data from the start. That requires effort up front – setting up the single source of truth, configuring integrations, training the staff – but the payoff is a smooth project with far fewer “oops” moments. Owners who champion these improvements send a strong message to all stakeholders that doing it right is the only option. And as we’ve seen, the technology to support this is more accessible than ever. Whether it’s embracing a platform like ArchiLabs to knit together your entire tech stack and run automated workflows, or just enforcing stricter version control and communication protocols, any step toward a data-driven approach is a step away from costly rework.

In the words of an old maxim, “measure twice, cut once.” By bringing that ethos into the digital age – measuring with data, coordinating via integrated systems, and cutting with the help of automation – construction teams can drastically reduce mistakes. For those building the next generation of data centers, it’s an opportunity to save 9% or more on costs and deliver projects faster by design. The winners in the hyperscale era will be the teams that execute with precision. And precision is born from good data. By fixing the data, we fix the process – and finally break the cycle of rework that has burdened construction for too long.