You can’t have BIM without information modeling (it’s right there in the acronym!), and one of Revit’s strengths is its ability to capture design data in the model environment.
However, inconsistency slips into project data, slowing down Revit teams and perplexing BIM managers, who wonder why reports and schedules are telling a story that doesn’t match the reality they deal with every day, on every project.
Messy data is obviously a problem, but it underlies a bigger issue: lost efficiency at scale. Teams can’t reach their full potential when they become continually mired by Revit’s shortcomings.
Firms don’t need to accept inconsistent data as a fact of Revit life. Accurate reports and automated standards build consistency and a reality that truly does reflect the data.
What Project Data Actually Means in Revit
Part of the struggles that firms may experience with Revit project data is a lack of understanding of what it truly encompasses. Although all data is worthwhile, focusing on less significant info risks overlooking what drives progress and efficiency.
Therefore, be sure to pay careful attention to:
- Instance and type parameters
- Project Information parameters
- Shared parameters
- Naming conventions
- Schedules and reporting
- View and browser organization
- Sheet and documentation data
Where Revit Project Data Inconsistency Starts
Obviously, no firm sets out to make Revit project data inconsistent, incompatible, or inaccessible. Yet, information snafus happen — and the reasons aren’t always Revit just being Revit. Consider these scenarios:
- Different teams/offices: Teams across the cubicle or across the world may be working on the same Revit project, and they inevitably bring to the table their own standards, best practices, and workflows that muddle the data.
- External partners in project models: Similarly, third parties may work in Revit projects and not adhere to established firm standards and best practices, which also can affect data quality.
- Legacy templates, content, and projects: Old templates and content designed for other projects can sneak into a project, sometimes intentionally but often unintentionally, and throw off the data.
- Imported or copied content: End-users may import libraries and other content that may not necessarily slow down the model but still impact the project data.
- Manual entry plus human variation: When end-users, often not the original designer, are tasked with manually inputting data into Revit after the fact, their own preferences and foibles, along with natural human error, result in mistakes that introduce inconsistency into the data.
Unfortunately, these issues could be happening quietly or unknowingly — and they also compound. As a result, the data becomes less trustworthy.
The Hidden Impacts of Inconsistent Revit Project Data
Inaccurate Revit project data can lead to short- and long-term implications and plenty of unintended consequences. Here are a few of the risks:
Reporting Becomes Unreliable
Teams should trust the data as it appears in Revit. But if Revit schedules don’t align, BIM managers can lose confidence in the information and stop relying upon what they’re seeing — and something they need to effectively manage.
Standards Drift
Inconsistent inputs equal inconsistent outputs. End-users may not know which standards to follow because the project is showing multiple ways something should be done. The variations lead to more variations, creating even more inconsistency, to the point where users start believing the incorrect standard is the norm they should be applying.
Automation Breaks Down
Revit automation — workset management, properties syncing, and much more — is only as good as the data or parameters the automation is drawing from. Inconsistent data can paralyze otherwise efficient workflows, which slows down teams that rely on automated processes to work efficiently and serenely in Revit.
Cleanup Work Multiplies
One of the benefits of automation is how it prevents errors, competing standards, and other issues from spreading through the model. That saves time during design, but it also saves BIM managers time during review and QA — they aren’t spending hours, even days, cleaning up problems introduced into the model. When inconsistent data interferes with automation, those problems slip through and multiply remediation work, pulling reviewers away from focusing on true quality.
Models Become Disorganized
Inconsistent parameters open a literal mess of problems for everyone working within a Revit model. Sheets and views become difficult to find and navigate, requiring more time for the end-user and increasing the risk of errors. And the data just gets worse from there …
Why Manual Fixes Don’t Scale
Manual cleanup would seem to be the answer to inconsistent data and congested Revit projects. A parameter is off or a bit of data is wonky? Just fix it and move on to the next inconsistency …
This strategy simply doesn’t scale, for three big reasons:
- BIM Managers can’t police everything: Nobly, BIM managers may try to catch every error, but they aren’t superhuman and won’t be perfect. Something — probably many things — will go undetected, and the project will suffer as teams race to make deadlines.
- Spreadsheets and documentation aren’t enough: Teams may take a proactive approach by using documented standards and preferences, outside of Revit, to guide their design. However, will end-users consult that documentation as they work, especially if they are in a groove or when it just feels inconvenient? And how often are those spreadsheets updated?
- Issues are often discovered too late: Problems within a project might not become apparent until they can’t be easily fixed. When one bit of cleanup is creating a couple hours of rework, on deadline, BIM managers face tough choices on how much quality they’re willing to sacrifice just to be on time.
The best solution — a strategy that truly scales — requires ensuring project data consistency from the start.
Fixing the Problem by Standardizing and Normalizing Project Data
Consistency with Revit project data is achievable, and it’s not as arduous as it might seem. The initiative requires two basic principles:
- Standardization: Defining the right structure within the Revit model
- Normalization: Ensuring data actually follows the structure that has been defined
With those two goals in mind, these next steps will move projects toward consistent data:
Define What ‘Good Data’ Looks Like
Varying, often competing standards and parameters almost guarantee that Revit project data will be inconsistent. What’s needed is “good data,” which includes:
- Consistent naming
- Approved values
- Controlled shared parameters
Eliminate Variations Automatically
Often, the challenge for BIM managers and their teams is ensuring consistent data is generated in Revit, which doesn’t always automatically memorialize design decisions. When a team member who wasn’t the original designer is tasked to input information manually, they often must reverse engineer the design after the fact — or just guess — and hope for the best.
Automation shifts the human element earlier in the process to capture data from the start. For example, Guardian’s Parameter Prompts request users to input at the point of creation. Even if the underlying shared parameters differ, the software, through its Model Properties feature, standardizes and aligns those shared parameters. Consistency improves because reliance on user memory and guesses is removed from the equation.
Reinforce Standards in Real Time
Data can still go awry if standards aren’t followed during design. Sometimes, inconsistent end-users are the bigger obstacle to consistent data.
Guardian offers a means to reinforce project and firm Revit standards in real time. End-users receive gentle guidance and reminders, while they work, if they deviate from standards or do something that may negatively impact the model. As a result, BIM managers don’t need to rely on excessive cleanup to ensure data is consistent.
What Automation Unlocks at Scale
Achieving consistent data delivers more benefits than just, well, consistent data. At scale, incorporating automation into data initiatives results in:
- Trustworthy data: BIM managers and project leadership can be confident that information is current, useful, and accurate.
- Cleaner models: With parameters and standards in order from the start, models experience fewer errors and issues, leading to higher quality when a project is turned over to the client.
- Faster QA: BIM managers, freed from fixing mistakes and questioning data they see, spend less time reviewing models, or they can devote more time to deeper, high-value QA that might not have been otherwise feasible.
- Less rework: Let’s not mince words — rework stinks. With cleaner models and consistent data, BIM managers aren’t flirting with missed deadlines and can shift their priorities to proactive responsibilities.
- Stronger standards foundation: Because standards are such a vital part of Revit project data consistency, they logically benefit from automation. And when standards are stronger, data becomes stronger, which further strengthens the data — and both continue to complement each other.
Consistent Data Is Consistently Guardian
Inconsistent data isn’t a small issue — it’s a scaling problem that affects all areas of a Revit project and hinders teams from doing their best work. Guardian’s Revit add-in software overcomes this struggle by standardizing project data across teams, educating users in the moment, and minimizing the obstacles that make Revit a challenge to work in.
Want to learn more about how Guardian for Revit can help your firm? Book a demo and let us show you how.
