Optimizing Your Data for Success: Data Cleaning Steps & Process
Optimizing Your Data for Success: Data Cleaning Steps & Process
Whatever type of data analytics you perform, your analysis and any subsequent processes are only as good as the data you start with.
Most raw data, whether text, images, or data stored in spreadsheets, is incorrectly formatted, imperfect, or downright dirty, and must be cleaned and structured before you begin your analysis.
To ensure that your data is properly prepared for analysis, you can use a variety of data cleaning, "data cleansing," or "data scrubbing" techniques.
Data cleaning is the process of repairing or erasing inaccuracies, corruptions, improperly formatted, duplicate, or incomplete data from a dataset. When different data sources are combined, numerous potential for data duplication or mislabeling exist.
Cleaning your data is as simple as following this six-step guide:
Get Rid Of Any Information That Isn't Relevant: The first step is to find out what analyses you'll be doing and what your post-analysis requirements are. When it comes to your issues and problems, what do you want to know?
Check your data and see what you can do with it before making a final decision. Get rid of any data or observations that aren't related to your future goals.
Remove Redundant Information From Your System: With many sources, multiple departments, scraped datasets for analysis and multiple surveys and customer responses to the same question you'll almost certainly end up with data duplication.
A slower analysis and greater storage space are both caused by duplicate records. It's also vital to keep in mind that a machine learning model will likely give more weight to duplicate outcomes if it's trained on a dataset having them. They must be eliminated in order to get a more equitable outcome.
Error-Proof The Structure: Misspellings, inconsistent naming rules, wrong capitalization, and other grammatical inconsistencies are all examples of structural faults. Despite the fact that they may be evident to humans, most machine learning systems would not be able to identify the errors and your analyses would be skewed.
Deal With Data That Has Been Lost: Data that is missing, or that has blank spaces, can be found by scanning it or running it through a cleaning programme. An incomplete database or human error could be at blame for this. You'll need to decide if the entire column or row, a whole survey, or individual cells should be rejected, manually entered, or left as is, depending on the nature of the missing data.
Exclude Data Errors From Your Analysis: Outliers are data points that deviate significantly from the norm and can distort your study in one direction. If you were to average a class's test scores and one student refused to answer any of the questions, his or her 0% would have a significant impact on the entire average. In this instance, you should consider completely eliminating this data point. This could result in outcomes that are significantly closer to the average.
Ensure That Your Data Is Accurate: Data validation is the last step in the data cleaning process, and it ensures that your data is of high quality, consistent, and properly formatted for downstream operations.
Check to see if your data is well-organized and clean enough for your needs. Check that all of the data points are in order and that nothing is missing or incorrect.
Conclusion
If you're going to conduct any kind of data analysis, you're going to have to go through the tedious task of data cleansing. As a result of the steps outlined above, you will soon have data that is ready for further processing.
Don't compromise on data cleaning, and you'll end up with realistic, real-world results that you can act on right away.
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