What is Data Cleansing?
Your business is only as good as the data you collect. When you are sitting on top of unusable data, the ensuing analysis or algorithms are likely skewed. If your company relies on the information that it collects to make informed decisions for the future, then data cleansing or data cleaning is an essential step in ensuring the quality of those future decisions.
Data Cleansing is the Removal of Junk Data
It really is that simple. This can apply to data that is:
- Incorrectly formatted,
Over the lifetime of the data set, it’s only natural for data that fits these descriptions to seep into the dataset. It can easily impact any outcomes or algorithms that rely on the information at hand. Data cleaning isn’t a one-size-fits-all type activity. It’s something that will vary by need, and by the dataset in question. But it’s important to understand what needs to be done when the situation arises.
The Data Cleansing Process
While the data cleansing process may look different from company to company, or even dataset to dataset, there are some basic steps that can be applied to the process.
Remove Duplicate or Irrelevant Data
When you combine data sets, or scrape data, or receive data from multiple inputs, the potential for duplicate data increases immensely. Removing this data goes a long way in decluttering the data set and improving the quality of your algorithms and outputs.
The other type of data to keep in mind in these initial stages is irrelevant data. This is when you have information that doesn’t strongly pertain to the problem that you are collecting this data to solve. Sometimes businesses become overambitious in the data they collect, or the scope of what they collected may just be a little too wide.
Fixing these issues will go a long way in making your data more targeted and manageable. It’s an excellent initial step in the data cleansing process.
Correcting Structural Errors
Data collection is exacting. Structural errors can occur with strange naming conventions, typos, or inconsistent capitalization. When this happens, you can end up with inconsistencies, such as duplicate categories or classes that mean the same thing.
Address Your Unwanted Outliers
Data outliers happen. Sometimes they may be mistakes and sometimes the very existence of the outlier can prove whatever it is that you are working on. Evaluating your outliers with human eyes can help you determine their validity and how to proceed. If it is irrelevant or a mistake, you may want to remove them.
Missing data can throw off your algorithms. There are ways to address this issue, however. You could delete the observations that contain missing values, but this option may result in lost information. Another option might be to input missing values based on other observations. But this could jeopardize the data quality because you’re now operating from assumption, rather than observation. A third option might be to alter the way that you use data to navigate null values.
Once the Data is Cleaned
After the data is cleaned, you should have a data set that makes sense, and follows the appropriate rules. You should hopefully be able to follow trends to help you make sense of the data that’s been collected. Hopefully, these steps should help you in correcting any data quality issue.
Keeping more consistent data allows you to increase your overall productivity and make better decisions. It allows for more efficient business practices and happier employees. Data that is consistently cleansed helps you keep a smooth and efficient business.
If your outdated data becomes corrupted, contact We Recover Data to help.
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