Reliable data are a foundation for using AI in construction. This article explains how to organise, check and prepare information from different sources for analysis.
Why Is Data Cleansing Critical to AI Success?
Poor input data can produce unreliable results. Data cleaning is therefore not an optional finishing step but a foundation for a useful AI system.
The Unique Challenges of Construction Data
The following data quality problems are particularly challenging in the construction industry:
Heterogeneous data sources: Blueprints, budgets, schedules, supplier information - all in different formats, in different systems.
Manual data entry: Data is still entered manually in many places, which increases the possibility of errors.
Variable terminology: The same building element, material or process may appear under different names in different documents.
Missing data: This is especially typical for subsequent documentation, when it is difficult to reconstruct the exact facts.
Unstructured data: Construction logs, e-mails, minutes, which may contain valuable information, but in a non-standardized format.
Steps of the Data Cleaning Process in the Construction Industry
For construction data to be AI-ready, a consistent and thorough data cleansing process is required:
1. Examination and Evaluation of Data
Before performing any cleaning, it is important to thoroughly familiarize yourself with the available data. This includes:
Identifying the source of the data and evaluating its reliability
Creation of statistical summaries (e.g. pivot tables)
Examination of missing values, outliers, and inconsistencies
Determination of basic quality indicators
This step will help you get an overall picture of the overall health of the dataset and the required cleanup tasks.
2. Application of Data Cleaning Techniques
Particularly effective data cleaning techniques in the construction industry:
Eliminate Duplications
One of the most common problems is the presence of duplicate data, especially when merging multiple data sources.
Construction industry example: During plan changes, the same building element may appear in the register several times, with different versions. Providing elements with unique identifiers and removing duplicates is key.
Handling Outliers and Irrelevant Data
Outliers often indicate errors, although sometimes they are real but unusual data points.
Construction industry example: If the price of a window type is significantly different from that of other similar windows, it may be an actual premium product, but it may also be a data entry error. A combination of industry knowledge and data analysis can help you manage outliers appropriately.
Repair of Structural Defects
Structural errors include inconsistent names, spelling errors, or formatting inconsistencies.
Construction example: The same room can be marked in different ways in documents: "AA.1023", "AA 1023", "1023". These should be standardized before data analysis.
Handling Missing Data
There are three main methods for dealing with missing data:
Delete: Remove lines with incomplete data (recommended only if there are few such lines and non-critical information)
Imputation: Estimation of missing values based on other data (e.g. based on average costs of similar rooms)
Marking: Informative marking of the missing value (e.g. "no fire resistance classification")
A missing classification does not mean that a product lacks the required performance. Check manufacturer documentation or another authoritative source; never invent a classification without evidence.
3. Control
After data cleaning, it is important to check the result:
Does the cleaned data meet the quality expectations?
Can the remaining problems be easily spotted with the help of data visualization?
Is the new dataset consistent?
4. Documentation and Reporting
Detailed documentation of the cleaning process and the operations performed is essential:
Which data fields have we modified?
What rules did we apply?
Differences between the original and the cleaned dataset
Any remaining quality issues or limitations
This helps in reproducibility of the process and similar processing of other datasets.
The Benefits of Data Cleansing for Construction AI Applications
Clean, consistent and precisely structured data provides many advantages for AI-based solutions in the construction industry:
1. More Accurate Forecasts and Estimates
AI models based on clean data can provide much more accurate estimates of:
For the duration of the project
Costs and their changes
For material needs
For resource demand
According to an international research, AI models based on more accurate data can reduce cost estimation errors by up to 25%, which can mean significant savings.
2. More effective decision support
Clean data allows you to:
Early detection of problems
A more accurate assessment of risks
Faster decision-making based on facts
3. Automated Reporting and Documentation
Standardized, cleaned data enables:
Automatic reporting
Speeding up documentation processes
Reduction of human errors
4. Better Project Implementation
Indirect benefits of data cleaning in project implementation:
Fewer delays and redesigns
More efficient resource allocation
Better communication between project teams
Summary: Clean Data Is More Valuable Than We Think
Data cleaning is not just a technical issue, but a strategic investment. Clean, reliable data:
They enable better decisions
They reduce risks and errors
They lay the foundation for the introduction of successful AI projects
They provide a competitive advantage during the digital transformation
For construction SMEs, data cleansing can be the first step towards digitization - a step that brings immediate benefits while paving the way for more advanced technologies like AI.
As mentioned earlier, AI is not magic, it's a tool - and like any tool, it's only as good as the quality of the raw materials used. In the construction industry, these raw materials are nothing more than data.
How to proceed?
If you want to start your business towards data-driven operations and the use of AI, here are some specific suggestions:
Create a data inventory: Collect all available project data and assess its quality.
Identify the most valuable data: What data would help you make the most decisions? Which ones do you experience the most problems with?
Start a pilot data cleaning project: Select a small but valuable data set and clean it using the methods described above.
Measure results: Document how processes and decisions have improved using cleaned data.
Build incrementally: After initial successes, expand data cleansing to additional areas as you implement data quality assurance practices.
Data cleansing may not be the most exciting part of digital transformation, but it's definitely one of the most useful - and it creates the foundations on which you can build lasting success.
Sources:
Lexunit: "This is why most AI projects fall by the wayside" - Comprehensive article on the importance of data cleaning in the success of AI projects. Lexunit (Downloaded on April 10, 2024)
Bim Corner: "Data cleaning - all you have to know" - Detailed guide on the data cleaning process in the construction industry. Bim Corner (Downloaded on April 10, 2025)
Astera Software: "A Comprehensive Guide to Data Cleansing" - A comprehensive introduction to the techniques and benefits of data cleansing. Astera Software (Downloaded on April 10, 2025)
Alation: "What Is Data Quality and Why Is It Important?" - Detailed analysis of the business benefits of data quality. Alation (Downloaded on April 10, 2025)
Forbes: "Building The Future: How AI Is Revolutionizing Construction" - A comprehensive analysis of the application and benefits of AI in the construction industry. Forbes (Retrieved: April 10, 2025)
Bolpagni, M. & Bartoletti, I. (2021): "Artificial intelligence in the construction industry: adoption, benefits and risks" - Scientific study on the benefits and risks of introducing AI in the construction industry. ResearchGate (Downloaded on April 10, 2025)
Sources
https://lexunit.hu/blog/miert-lenne-fontos-az-adattisztitas/
https://bimcorner.com/data-cleaning-all-you-have-to-know/
https://www.astera.com/type/blog/data-cleansing/
https://www.alation.com/blog/what-is-data-quality-why-is-it-important/
