Bad data leads to bad decisions. Whether you're preparing a quarterly report, running a marketing campaign, or making operational improvements, the quality of your data directly impacts your results. Unfortunately, most datasets are messier than they first appear.
Here are seven data cleaning tips that will help you catch problems early and trust your analysis.
Before you analyze anything, find out what's missing. Blank cells, NULL values, and placeholder entries like "N/A" or "-" can skew averages and break formulas. Start by counting how many empty cells exist in each column. If a column has more than 20% missing values, you need to decide whether to fill them with defaults, use averages, or exclude that column entirely.
Duplicates are surprisingly common, especially when data comes from multiple sources or form submissions. A single customer appearing twice can throw off revenue calculations and segmentation. Look for exact duplicates first, then check for near-duplicates where names or emails might have slight variations like extra spaces or different capitalization.
Inconsistent text formatting is a silent data killer. "New York," "new york," and "NY" might all represent the same location, but a computer treats them as three different values. Decide on a standard format early and apply it across the board. Pay special attention to phone numbers, zip codes, and address fields.
Numbers stored as text won't calculate properly. Dates stored in different formats can't be sorted correctly. Check that numeric columns actually contain numbers and that dates follow a consistent format. A common red flag is when you can't sort a column numerically or when SUM formulas return unexpected results.
Outliers aren't always errors. A massive one-time purchase might be legitimate, or it might be a data entry mistake. When you spot values that seem unusually high or low, investigate before removing them. Sometimes outliers reveal important patterns, like fraud or system errors that need separate handling.
Every dataset has constraints. Ages shouldn't be negative. Email addresses should contain an "@" symbol. Dates shouldn't be in the future. Create a quick validation checklist for your key columns and run through it. This catches typos and entry errors that other checks might miss.
Cleaning data without documentation is a recipe for confusion later. Keep a simple log of what you changed, why you changed it, and which rows were affected. This makes your analysis reproducible and helps teammates understand the decisions behind the cleaned dataset.
Data cleaning doesn't have to be tedious. Many of these checks can be automated, and tools like getqueryly can help you spot issues and clean up your dataset using natural language queries. Ask questions like "How many rows have missing values?" or "Find duplicate entries" and get answers instantly.
Clean data is the foundation of every good analysis. Invest the time upfront and your insights will be far more reliable.
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