Validating your data: This is the final step of the process.Dealing with missing data: If there are gaps in your data, what effect will this have? You might choose to remove associated entries, guess missing values, or simply flag them so you can measure their impact later on.For instance, are number fields properly labeled as numerical data? Resolving type conversion and syntax errors: This involves things like removing whitespace, checking for spelling mistakes, or simply ensuring data is categorized correctly.Fixing cross-set data errors: Data rarely comes from a single source ensuring that different data sources don’t contradict each other is vital.You’ll need to make a judgment call about which outliers to keep and which to remove. Removing unwanted outliers: Outliers can be useful, but if they’re erroneous they’ll skew the results of your analysis.Standardizing your data: This involves things like ensuring the numerical observations in your dataset use the same unit of measurement.Unifying the data structure: You’ll need to ensure data from different sources is consistent by mapping it to a unified underlying structure.Getting rid of unwanted observations: Removing observations that aren’t relevant to the problem you’re trying to solve.The main tasks you’ll have to carry out when cleaning data include: What is data cleaning and how is it done? What are some of the most popular data cleaning tools?.Want to skip straight to the tools? Just use the clickable menu. But before we get there, let’s set some context by briefly recapping what data cleaning involves. In this post, we highlight some popular data cleaning tools that data analysts use every day. Better yet, a lot of these have been designed specifically for areas like customer data, which plays a huge part in 21st-century business. Luckily, there are many industry tools available to streamline the process, which can be especially helpful for beginners. If you’re starting as a data analyst, one of the first things you’ll learn is the importance of effective data cleaning.