4 Tips to Clean Up Your Estimating Data

A Gartner report from 2021 states that dirty data can “costs businesses an average of $12.9 million annually.” Data is moved around, touched by a lot of team members, duplicated and duplicated and duplicated, forgotten about, and left to rot in the corners of the database until it is the next project’s problem. 

Keeping your estimating data squeaky clean ensures it's always ready to go when your team needs it. Not only does this make your cost database neat and tidy, but it also makes it super easy for your team to quickly find what they need. And let's face it, there are several good reasons why keeping your preconstruction data clean is crucial:

  • Accuracy: Estimating data that is inaccurate or outdated can lead to errors in cost estimates, material quantities, and project timelines. Clean data ensures that your estimates and budgets are accurate and that your project stays on track. 
  • Efficiency: Clean estimating data allows project teams to work more efficiently. When data is organized, up-to-date, and easily accessible, teams can make informed decisions quickly and avoid delays in the construction process. 
  • Cost savings: By keeping estimating data clean, you can avoid the costs associated with rework, mistakes, and delays. Accurate data can help you identify cost-saving opportunities, such as more efficient materials or processes. 
  • Risk mitigation: Inaccurate preconstruction data can lead to increased risk, including safety hazards and legal disputes. By keeping your data clean, you can mitigate these risks and ensure a safer, more successful construction project. 

Overall, keeping preconstruction data clean is essential for ensuring the success of a construction project. It helps project teams make informed decisions, stay on budget, and avoid costly mistakes and delays. Now that we have a good understanding of why it is important to maintain clean estimating data let’s get into how to do it. 

plan iconStart with a Plan

Before diving into cleaning, make a plan of attack. Decide which areas of your database need the most attention and what tasks you want to tackle first. By chunking up the all-encompassing “estimating data” you can set realistic and attainable goals. It also means you can delegate to other team members a portion of the data to review and clean. We recommend the following schedule: 

  • Database maintenance is done quarterly based on chunks. For example:
  • First quarter = Division 01-05, 31-33 
  • Second quarter = Division 06-09 
  • Third quarter = Division 10-13 
  • Fourth quarter = Division 21-25 
  • Estimate data maintenance is done monthly. 

Carve out time on your calendar to focus on the tasks. And be realistic about the amount of effort it is going to require. It may take you five hours to complete cleaning one portion of your preconstruction data, but you may not have five hours in one single day. Set the time on your calendar each day and treat it as a highly important meeting. Don’t let other people schedule over it and don’t let yourself get sidetracked by other tasks. 


Decluttering preconstruction data means getting rid of unnecessary or irrelevant information and organizing the remaining data in a clearer and more manageable way. It's like cleaning up a messy room and putting things in their proper places. This could include outdated cost data, duplicate entries, or data that doesn't directly impact your estimates. 

In construction estimating, you deal with a lot of data related to costs, quantities, materials, and other project details. Sometimes, this data can become overwhelming and difficult to work with. Decluttering helps simplify and streamline the data so that it's easier to understand and use. 

Next, you can organize the remaining data in a logical and structured manner. This can involve categorizing the data based on different aspects of the project, such as cost categories (like labor, materials, equipment), project phases (like site preparation, foundation, finishing), or even specific work items (like flooring, plumbing, electrical). By grouping similar data together, you can quickly access and analyze the information you need. 

Additionally, you can use software specifically designed for construction estimating to help you declutter and organize your data more efficiently, like DESTINI Estimator. Our platform provides features like data filtering, sorting, and visualizations, which can make it easier to work with large amounts of information.

Scrub the Data

Data cleaning and organization are important steps in working with your preconstruction data. Here are some key tasks involved in scrubbing your data: 

  1. Diminish Duplicates: Duplicates in data occur when information is gathered from different sources or when team members provide redundant information. Removing duplicates helps save storage space and makes analysis more efficient. 
  2. Trash Irrelevant Observations: Data that is not relevant to the problem being solved can slow down processing time. It's important to exclude such irrelevant data from analytic reports. However, practice caution when deleting data as it may be connected to another part of your estimating process. 
  3. Uncover Incomplete Data: Missing values in your database can cause bias and errors in your analysis efforts. Determine if the missing values are plausible or due to missing information. Dropping incomplete data is one option, but alternatives include replacing null values with substitutes or marking the missing data. 
  4. Pay Attention to Outliers: Outliers are data points that are significantly different from the rest, and they can distort the overall understanding of the data. Using tools like PowerBI, you can scan estimates for outliers, missing properties, and other issues. DESTINI Estimator integrates with PowerBI so you can easily connect your data to deep dives into your data.
  5. Fix Structural Errors: Correcting errors and inconsistencies in data, such as typos, capitalization, or formatting, is crucial. Ensuring correct data types and standardizing fields helps maintain consistency and removes unwanted characters. We have some suggestions in our virtual community for naming conventions here.
  6. Verify Your Efforts: Validate the process of ensuring data accuracy, completeness, consistency, and uniformity. This may involve running checks and tests throughout your database cleaning process, keeping track of the tools you are using to get the job done, and what techniques you used to do the deep cleaning. 

Don’t Forget Your Team Members

Keeping your peers updated on why the cleaning is important, what has been changed, and looking for ways to keep it maintained will benefit you (and your sanity) as well as your company for future cleaning efforts. Getting other team members involved in the data cleaning is highly recommended. You can send a filtered report to each estimator on your team to have them fix the issues. This delegation will help them improve their everyday workflows.  

Keeping preconstruction estimating data clean is crucial for the success of your business. Whether you are tackling this cleaning in the Spring or Winter or whenever you carve out the time to do it doesn’t matter. The fact that you are making this a priority is the most important part of this effort. 

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