FM Data reconciliation is a common task for many contractors and others in the construction industry or at least it should be. When you are reconciling FM data your objective is to ensure the information assembled is complete and accurate reflecting approved submittals, as-built conditions in the field as well as the BIM models while also conforming to the owners expectations. Mistakes happen all the time, making data reconciliation a must-know, but what if there was a way to streamline identifying and fixing those errors? FM Data standards define the owner’s requirements for FM data and establish a process for assembling and verifying the data for the purpose of streamlining and reducing errors in handover while providing tools to ensure completeness.
Data reconciliation without data standards and a process
Typical data collection and delivery is based on what assets are in the model and/or what assets are visible to the collector in the field. Without standards and proper asset tracking, it is easy to lose track of assets within a project especially as models evolve from design through coordination and fabrication. Assets from coordination or fabrication are often left out of the as-built model. Whatever your process is, with so much data to collect across so many different assets, you are guaranteed to miss something. Even if you do account for everything in your model how do you know you model isn’t missing assets defined in a prior version or that it contains everything installed in the field.
Without data standards and a defined process, you are guaranteed to make mistakes including data errors, duplication of assets, and or missing assets altogether. Reconciling this data is an extraordinary effort further complicated by multiple data sources. Your team is forced to essentially start over, as they must export the list of assets from the model, collect data from the field and input the collected data into a spreadsheet before finally handing over the updated FM data to the owner. A huge concern on the owner’s side is that there is practically no way to confirm they actually got what they paid for in the project.
Data reconciliation with data standards and a defined process
With FM data standards, data reconciliation is much easier and faster providing project stakeholders with a clear roadmap of what is required and what has been satisfied. Standards initiate data definition and collection in the design phase and sequentially build on it from there using defined assembly and verification processes. Additionally, they ensure assets are named correctly from the start, eliminating rework later in the project. By implementing standards early on in a project, you’re setting yourself up for more efficient data assembly processes and a means of measuring success.
Standards inform stakeholders what asset types are required and what data is required for each asset type, when it should be collected, the source and who is responsible. This informs all stakeholders, streamlines data collection and eliminates redundancy. Further, data standards provide verification workflows that ensure accuracy and create accountability for the data collection.
Standards provide a defined deliverable for all required assets and a scorecard to compare your data collection against. Most importantly, standards provide a method for the stakeholders to actually know what data has been collected and what data has not for each asset.
In the event that data reconciliation is necessary, data standards make this process far more streamlined. With data standards in place, it is easier to identify an error before it becomes a larger issue and it is far faster to collect and input the accurate data.
You wouldn’t build a building without clearly defined standards and plans. So why would assembling hundreds of thousands of building data points be any different? Unless you want to be left with a missing data or assets, data standards are imperative to develop and implement.
Download this guide to get started with data standards so you can experience streamlined handover collection and delivery, while also eliminating room for errors and making it easier to reconcile in the event of an issue.
