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:
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.