10 Examples When Data Cleansing Is Required
Data cleansing, as we already know, is the process of detecting errors and inconsistencies in data and correcting them to maintain high data quality. The data cleansing process typically includes both manual steps (like data wrangling) as well as automatic steps (like using well-designed queries to detect the broken data).
Data cleansing in business data is necessary because the customer information changes constantly. A person who is living or working at a certain location can relocate somewhere else. Therefore, these records have to be changed and the updated information should reflect in the data.
Here are 10 examples of dirty data that were corrected on time using data cleansing procedure. If you face similar issues, you can follow the same techniques.
1. Corrupt Data
Data gets corrupted when you have kept it for a longer period of time without cleaning it. Data corruption due to data rot can be avoided by correcting it using the historical backup.
Every business collects its data from more than one source to have a decent database. Since different sources use different format of retrieving information from a user, there might be a field or two which is less or more in these sources. These inconsistencies make your data bad and you can lose your time, money and customers due to this. Data cleansing identifies the inconsistencies in the data set, corrects them manually and then migrates the clean data into the master data management tool.
The root cause of such data is the same – data collection from multiple sources. There are a lot of records in your database that isn’t relevant to your business or vice versa, like students’ information will be irrelevant to you if you deal have been selling apartments/office space. If you have irrelevant data, it will result in loss of money, time and resources. Removing irrelevant information during data cleansing is the only solution in this case.
Removing irrelevant data doesn’t guarantee accuracy. You can have relevant information of a customer which can be a turning point for your business but the data turns out to be inaccurate. Maybe the person that you were targeting moved out of the city and you realize that your huge profit hopes have come crashing down. Had you cleaned your data, you would have been targeted the person before he moved out of the city or you might have simply jumped to your next target. One more strong reason to use data cleansing in your business.
5. Typography errors
Typography errors are unavoidable and it happens to the best of us. However, if you let these typography errors be while sending out emails, your campaign might not be successful. Data cleansing helps you correct your typographical errors so that the emails are delivered and doesn’t bounce back because of the wrong email address.
Not standardizing the data can lead to the business listing the same entry as a different entity of the data. For example, you have Street and St., both mentioned in your data and instead of correcting it, you let it be. This only leads to confusion and thereby gives out a bad impression. Data cleansing inculcates standardization process.
7. Dirty Data
Businesses import their data from multiple sites where a majority of such data sets are present for a long time. This gives you dirty data which gets difficult and costly to clean but it is also very necessary. You need to get the dirty data cleaned before using it for your business as it will not get the desired results.
8. Incomplete Data
Another example of when your data needs some thorough cleansing. Imagine you want to reach to someone on an urgent basis but you don’t have his email address and he cannot be reached over a call. This happens because while collecting the data you didn’t feel it necessary to get all the details that are required to get in touch with him. Data cleansing finds out alternatives for completing the missing information, that helps solve this problem.
9. Use of Special Characters
Special characters are confusing. Moreover, some sensitive parsers are not able to handle special characters owing to which a script fails. Data cleansing allows you to identify such problems and clean it so that it can be used without any complication or confusion.
10. Referential Degradation
While migrating the data the main aim is to migrate historical sales order to a new platform. However, it can sometimes happen that the foreign key constraint is broken during the migration process. This breaks the structure of the data as well. Data cleansing fixes the data structure so that you can use the data successfully.
There may be more data cleansing examples that make you understand the importance of it. However, these are the most common problems that businesses face and therefore we have listed it here for you.
Data cleansing examples show the ‘before-after’ effect of the process on dirty data. If you face any of the problems mentioned above, you now know what you need to do. Keep in mind the data cleansing best practices that you need to follow while scrubbing the data.
We have already stressed on fact of how important data cleansing is for your business, both for revenue growth as well as reputation, in our previous blogs.