Data cleaning is the process of modifying or removing inappropriate data entities to prepare it for correct analysis. Data clusters can have irrelevant or redundant information that can destroy the analysis and can lead to inaccurate results.
Data analysis is an imperative aspect of data sciences and if it is not done properly, the whole meaning of data sciences would be lost and a complete wastage of time and cost would occur. To avoid this situation, data cleaning is employed to improve data quality with overall productivity.
Importance Of Data Cleaning
Data quality is fortune making for those enterprises and industries that improve their business functions by relying on customer profile and feedback. For example, data quality would be extremely important for any bank who wants to notify all of its customers about the new scheme affiliated with the savings account. Similarly, if you implement an omnichannel for your brand, you would have collected tons of data that can be irrelevant, there data cleaning can play a key role to improve customer experience. Here are some more advantages of data cleaning.
Saves Money And Time
Data scientists are employed not only to analyse data but also to provide optimal cost solutions. And they first put their hands on data cleaning to make the dataset appropriate and relevant. Otherwise, plenty of time and cost would be gone wasted on the processing of inappropriate data.
Data cleaning protects the reputation of a company because it will give accurate results and an accurate strategy would be devised after analysis. This ensures a happy customer and a reputable company.
Boosts Results And Revenue
It boosts revenue by providing efficient results. If you use online tools for data cleaning, everyone would get the optimized results efficiently, this would raise the work pace and eventually skyrocket your revenue.
Here is something you can read to know the importance of data cleaning.
6 Steps For Data Cleaning
Following steps can be useful in cleaning the data and boosting sales.
Remove Unnecessary Observations
The foremost step of data cleaning is to remove all the unnecessary data. The unwanted information can be irrelevant or it may be redundant. To avoid any inaccuracy in results removal of these data entries must be ensured. An unwanted data entry is called Bad Data, data cleaning removes it and causes frequent increase in data quality.
Deal With Missing Information
There are several strategies available to deal with missing values inside the dataset. You can plot a graph to identify the rows with maximum missing values. The graph below represents the percentage of missing values for each element.
Here are some possible changes you can make in your dataset.
- You can add the Isnull feature that will indicate any empty rows.
- You can replace the empty space with mode or median values of the column.
- A constant can be placed inside the empty space too that is outside the fixed value range.
To get more details about the above steps, read this blog.
Fix Structural Errors
Errors that rise due to measurement and transaction of data are called structural errors. Some common cases of structural errors:
- Irregular capitalization.
- Same attributes with different names
- Mislabeling of classes.
These tiny problems can lead to big research gaps for drawing results.
Manage Unwanted Outliers
Outliers can cause problems with many algorithms. If you don’t have a valid reason to remove the outlier, don’t remove it. For example, if you are having a really big numeral in the data that is disturbing your graphical view, try to depress it. But if it is too big to be manageable then you can take measures to exclude it.
Standardize The Process
At the point of entry, a standard process must be employed. This will minimize any wrong data entry inside the dataset and will help you move further from data cleaning to next steps.
Validate The Accuracy
Validate the accuracy of results after data cleaning. Try to employ AI or machine learning to detect and remove any data error in real time.
The blog is written to elevate the importance of data cleaning. According to research , about 60% time is consumed on data cleaning. Try to be proactive and take measures to avoid any anomalies and if it occurs you can use all the above steps to make your analysis accurate.