By Mery Ramirez
Data Scrubbing is one of the most common techniques used since the introduction of data sciences and has been used for various purposes for quite some time. Also known as Data Cleansing, this process is primarily defined as the cleaning and removing of various types of completely inconsistent data that does not have any meaning or needed usage. Industries such as insurance companies, banking systems, telecommunications, and many others use data for various purposes. For this reason, having completely refined data that does not require many changes affecting system performance is important.
Normally, data cleaning processes are hectic and require a lot of effort. This is made much easier by using various software solutions that can completely clean and scrub the data in no time. Though this is one of the best ways to get work done, however, there are also many other struggles that need to be looked at. While both data scrubbing and data cleansing are used simultaneously, there is still a minor difference between these two. Deciding on taking the challenges one may have to face? Let us have a look at the process in more detail.
Steps to Perform Data Scrubbing
There are several steps to perform while doing data scrubbing. Here, we have confined them to a few that are important to give a rough idea of what is going to happen and how one can sort out all the essential things easier.
Inspection and Audit
The very first thing is to identify all the irregularities and inconsistencies that are present in the data. This can be done in two ways. Either by reading and pointing out the whole errors scheme manually or by using a data-scrubbing tool. By doing a complete audit, one can easily get an idea of what is wrong with the given data and what needs to be fixed. With this, one can now move on to the next step as the parts that need change have been identified.
Data Cleaning
Here comes the real deal. This is where one’s skills will start counting. In data cleaning, begin by removing the general errors that can be seen. This includes all inconsistencies and all irregularities in the data that affect the flow. This also involves irregular words that do not appear to be needed. In short, this is the general error removal of typo errors and more.
Verification of Data Cleanliness
After removing all the general errors, one needs to make sure that all the necessary data issues that were in the data have been removed. One can also do this by using the same data-scrubbing tool that has been used to first identify the errors. This can also be done by reviewing the whole thing manually and getting others to give feedback on what needs to be rectified.
Report
Typing a report is also valuable, as this will document the effort on things that have changed. Writing a report is important and good practice, especially in a great firm. This is needed to get the whole thing under analysis and noting the parts changes were made. All the outcomes are then converted into a written format and submitted.
Common Errors
Speaking of things to rectify, let us get an idea of what kind of errors may need to be fixed. This is important so one can easily identify errors and make sure things do not go south. Following are some of the common data errors one can find while rectifying working data.
Duplicate Data Errors
This typo error involves the repetition of words in your data. It can be a single word or an entire block. Duplicate data increases the size of the data in no time and makes things difficult for many people.
Inconsistent Data Errors
This is an irregular flow of the whole data and can make a poor impression. The data flow is corrected and neatly set to ensure that it does not affect the natural look and elegance of the whole data. It is removed either using a scrubbing tool or manually.
General Errors
These include all the typing errors such as typo errors, punctuation, smaller letters, and much more. This can be rectified by simply using document editing software. However, it is important to take care of all errors so there are no issues left behind.
Data Scrubbing, as easy as it seems, can become a real hectic task if not done correctly. Just make use of the proper tools and knowledge to cleanse data from all kinds of errors. Choose from the options one can avail, and we are good to go.
