How bimspot can Improve Bad BIM Data in Construction

The construction industry relies heavily on the collection, analysis, and use of data. It is a vital component for the monitoring and improvement of operations. Putting findings to use can mean enhanced efficiency for construction companies. So, what happens when this data is “bad”? Well, this means it doesn’t give way to enhanced efficiency. In fact, it does the opposite. “Bad” data lacks accuracy and consistency. It’s incomplete or inaccessible. 

Luckily, there’s a way to make sure that you’re only spending time collecting and accessing the good data: bimspot. This web-based platform takes Building Information Modeling to a whole new level. In this blog post, we’ll explore how “bad” data can (and has) affected the construction industry. We’ll also give you 5 important strategies needed to fix the problem – and we’ll let you in on exactly how bimspot can help you achieve these. 

Representation of bim data and building

The Impact of “Bad” BIM Data on the Construction Industry 

In a recent study titled ‘Harnessing the Data Advantage in Construction’, Autodesk and the FMI Corporation laid out some pretty shocking stats. 

After surveying 3,900 construction industry professionals across the globe, one thing became clear: “bad data” is expensive – more specifically, “US$ 1.8 trillion”. This means that inaccurate, inconsistent data is costing the global industry a fortune.

Delving deeper, the report showed that over 30% of these 3,900 professionals (close to 1,200 people) indicated that the data they collect and use on their sites leads to poor decision-making about 50% of the time. 

That is to say, it’s “bad” data. It doesn’t add to profitability or efficiency but instead detracts from it. The study also showed that these poor decisions made using “bad” data have cost the global industry around US$ 88.69 billion – and that’s just in rework! 

So, if the word “bad” didn’t make it clear, it should be obvious now: bad data needs to go. Fortunately, the report didn’t stop there – it also lays out 5 data strategy skills recognised by participants as important for steps in the right direction. 

All of these skills can result in reduced rework, reduced safety incidents, and reduced delays in projects.

In order of what the respondents deemed most important, they are as follows:

  1. Optimising the workflow (57%)
  2. Implementing a good data management strategy (51%)
  3. Using data analytics (47%)
  4. Showing data reports visually (40%)
  5. Keeping data secure (39%)

Let’s take a look at these skills, and discuss how bimspot can help you out.

5 Important Skills Needed to Solve the “Bad” BIM Data Problem 

Workflow optimisation

If you want to turn bad data into good data, one of the most important things you can do is optimise your workflow. Why? Because an optimised workflow gives way to transparency. It’s far easier to collect and manage data when everything is laid out in a clear and concise way.

How can bimspot help you with this? Instead of manually setting up workflows and tasks, the platform offers an automated solution. This saves time and allows for data to be logged and analysed through the platform. 

Bimspot also lets you easily integrate other tools through API (Application Programming Interface). This way, you can create a more seamless workflow with connected systems. 

Data management strategy

Another great skill that turns bad data on its head is to have a good data management strategy. Think about this: there are countless parties involved in construction projects. They’ll all have different things to analyse and data to report. Data runs the risk of going “bad” when there’s a lack of communication between parties.

Clear communication and better management lead to optimised data analysis across the board. With its information requirements, bimspot enables proper Information Management. As a result, you can collect and store data in a way that ensures clarity.

Tablet showing BIM data analytics dashboard

Data analytics

What exactly are bimspot’s information management requirements? First off, it provides users with strong data analytics through the use of a KPI system. Using Key Performance Indicators is a valuable strategy. They provide teams with precise targets to reach for and enables you to see how the team is performing. 

The objective or target (the completion of projects on-site in this case) is achieved through the proper use of data analytics. Bimspot’s data analysis allows users to track the status of the project, and act on a specific area depending on each indicator.

Data visualisation

Another Information Management requirement is data visualisation. To be able to see where things need to be worked on is made all the more simple by the transferring of data into graphs and visuals. For example, you can track your KPIs via bimspot’s dashboard.

This makes it far easier to find out where you need to take action. This doesn’t only refer to actions required for project completion. It also helps users to see what they need to work on to improve the quality and accuracy of the data available. 

Data security

One final thing to remember is that “bad” data doesn’t only refer to inaccurate, incomplete, inconsistent data. It also refers to data that isn’t secured properly. Data with no security runs the risk of being tampered with – or even worse, leaked. So another thing that is very important is keeping your data secure.

Bimspot has you covered on this front. The platform allows for privacy, and will only share data with the third parties that you have designated. Data is kept secure and airtight so that tampering or leaking can’t occur.

Final Thoughts 

It’s clear that “bad” data really does live up to its name, costing the global industry over 1 trillion US dollars a year! When data is inaccurate, inconsistent, and other “bad” things, it can compromise the efficiency and profitability of a construction project.

Luckily, bimspot can help guide you in the right direction. Our platform offers a complete approach to the planning and management of building projects. And how better to optimise projects as a whole than to successfully collect and use data? If done properly, this will lead to a framework for future success. 

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