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Data cleansing is the process of identifying and removing errors, inaccuracies, and duplications from data. It is a crucial step in data preparation, which can make or break the success of your data analysis.
There are many benefits to keeping your data clean, including:
- improved accuracy and quality of your analysis
- reduced time and cost associated with data preparation
- fewer errors in your final results
- improved decision-making based on accurate data
There are several steps involved in data cleansing, including:
- identifying errors, inaccuracies, and duplications
- correcting or removing errors, inaccuracies, and duplications
- standardising data format
- filling in missing values
- verifying that the data is clean
Let's take a closer look at each of these steps.
Identifying errors, inaccuracies, and duplications
The first step in data cleansing is to identify errors, inaccuracies, and duplications in your data. This can be a challenge, depending on the size and complexity of your data.
There are several ways to identify errors, inaccuracies, and duplications in your data:
- Review your data manually: This is often the most time-consuming option, but it can be the most effective way to identify errors, inaccuracies, and duplications.
- Use a software tool: There are many software tools available that can help you. These tools can save you time and improve the accuracy of your data cleansing process.
- Use a validation rule: A validation rule is a statement that defines what constitutes a valid record. For example, a validation rule for an email address might state that all email addresses must contain a '@' character. (Note: email address validation is far more complex than that!)
- Use a check digit: A check digit is a character that is used to validate the accuracy of a sequence of characters. For example, the last digit of a credit card number is often a check digit.
Corrections
Once you have identified errors, inaccuracies, and duplications in your data, you need to decide how to deal with them. There are several options available:
- Correct the error: This option is only available if you know what the correct value should be. For example, if you spot an error in a customer's name, you can correct it by entering the correct name.
- Remove the error: This option is available if you don't know what the correct value should be or if correcting the error would introduce new errors. For example, if you spot an error in a customer's address, you can remove it from your data.
- Ignore the error: This option is only available if the error is not critical and will not impact the accuracy of your analysis. For example, if you spot an error in a customer's phone number, you can ignore it since it will not impact your analysis of customer behavior.
Standardising data format
Once you have corrected or removed errors, inaccuracies, and duplications from your data, you need to standardize the format of your data. This step is important because it will make your data more consistent and easier to work with.
There are several ways to standardize the format of your data:
- Use consistent field names: Make sure that all fields in your data have consistent names. This will make it easier to reference specific fields when working with your data.
- Use consistent field order: Make sure that all fields in your data are ordered in the same way. This will make it easier to work with your data when importing it into a software tool.
- Use consistent field types: Make sure that all fields in your data have consistent types. For example, all dates should be formatted in the same way. This will make it easier to work with your data when performing calculations or comparisons.
- Use consistent value formats: Make sure that all values in your data are formatted in the same way. For example, all prices should use the same currency symbol. This will make it easier to work with your data when performing calculations or comparisons.
- Use consistent character case: Make sure that all characters in your data are consistently lowercase or uppercase. This will make it easier to work with your data when performing searches or comparisons.
- Use consistent date formats: Make sure that all dates in your data are formatted in the same way. This will make it easier to work with your date values when performing calculations or comparisons.
- Use consistent number formats: Make sure that all numbers in your data are formatted in the same way. This will make it easier to work with your numeric values when performing calculations or comparisons.
Filling in missing values
Another common issue that can occur with data is missing values. Missing values can occur for a variety of reasons, including incorrect data entry, missing information, and formatting issues.
When dealing with missing values, you have two options:
- Remove records with missing values: This option is only available if missing values are not critical for your analysis. For example, if you're analyzing customer behavior and only have customer names and email addresses, you can remove records with missing values for phone numbers and addresses.
- Fill in missing values: This option is available if missing values are critical for your analysis or if you have enough information to fill in the missing values accurately. There are several ways to fill in missing values:
- Use a default value: This option is only available if you can assign a default value that is accurate for all records with missing values. For example, you could assign a default value of 'Unknown' for records with missing customer names.
- Use data discovery techniques to find and fill the real value. For instance, you might use a service or tool to find the Twitter handle for leads when you have their name, company and email address.
- Use the mean/median/mode: This option is only available if you're dealing with numerical values (e.g. prices) and if there are no outliers in your data set. For example, you could fill in missing customer ages by calculating the mean age of all customers. Do this with extreme caution.
- Use linear interpolation: This option is only available if you're dealing with numerical values (e.g. prices) and there is a linear relationship between the values in your data set. For example, you could fill in missing stock prices by linearly interpolating between known prices.
- Use multiple imputation: This option is available if you're dealing with categorical values (e.g. customer names) and if there are no outliers in your data set. Multiple imputation involves randomly selecting values from known records to fill in missing values. For example, you could fill in missing customer names by randomly selecting names from known records.
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