9 Data Cleansing Activities Organizations Should Perform

 

It is very essential for organizations to maintain an updated database regularly through using effective data cleansing activities and know its benefits for business. This helps in ensuring that the contact with your customer is efficient and compliance standards are well maintained. The process of identifying the errors in the database and then rectifying those mistakes in the dataset is known as data cleansing or data scrubbing. Inaccurate data while cleaning the data means the data which is incomplete, incorrect, out of date or in a wrong format.

 

The goal of cleaning the data is to maintain good quality and clean database by creating a single customer view, which means that there should be only one record present in the database for a single customer containing all the required data.

 

However, since data is highly dynamic and gets old rather quickly, maintaining a clean database can prove to be a difficult task if you fail to do so regularly. Also, it may so happen that many businesses might have a different database which leads to storing data of a single person in multiple databases.

 

Therefore to reap the benefits of data cleansing, organizations should perform certain data cleansing activities explained below.

1. Import data

To the first start with your data cleansing activities, you need to have data at a single place. You need to import your dirty data from all the places that you have stored them in, such as Excel, CSV or text files.

2. Consolidate data

The next task should be to convert and merge the data from differently formatted sources like CSV, SQL, Excel, SAP, Salesforce, etc., into a single, common database.

3. Re-fill missing data

You need to identify the data where values are missing and then recreate the missing information as and when it is possible. These missing information can be anything like state, country, postal code, the area code for the phone, web address, email address, gender, salutation, etc.

4. Standardize data

Check the data that has been separated or updated in the database to make sure that the same type of data is present in each column and then combine the data. Through this task, you can make sure that the first name, last name, phone number, email address, etc., of your contact, are all present in their respective, pre-defined columns.

 

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5. De-duplicate data

Duplicate data not only downgrade the quality of your database but it can also harm your reputation if you contact the same customer again and again because his entry was present more than once in the dataset. Therefore, you need to recognize potential duplicates and seek high matches for accuracy with tolerance for missing values, spelling mistakes or different address orders. For data that is critical for a mission, such results should be reviewed manually and then updated in the database accordingly.

6. Normalize data

This means that the data that is similar, for example, the salutation Miss, Ms., ms, should all be converted into Ms. Or the word street, st., St. should be converted into St. You should also convert telephone numbers into their standard Telstra format or any other way, as required. You should also check and change the web and email addresses wherever it has been provided and then reformat them as necessary.

7. Verify data

You should then verify to enrich the data. Validation of data against external as well as internal data sources for appending the information for adding values, that is, business contact can be verified against the yellow pages for verifying their current phone number and address. The same rule goes for several other fields including key contacts, geo-cords, profit, employee size, credit ratings, time zones, revenue, etc. which needs to be fetched for each company.

 

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8. Export data

After you are done with cleaning the data you need to export it back to different formats like XML, PDF, SQL database, Excel, or other databases as required.

9. Feedback

Every organization should build a mechanism that controls the places from where incorrect information is reported and then updated into the database. For example, you can establish a feedback mechanism for emails that go out but remains undelivered due to an incorrect address so that it gets reported and the invalid email address is cleaned from the customer data.

 

These data cleansing activities, if carried out practically and regularly can let you turn your raw, dirty data into a fruitful one. Although the process is a difficult one it is beneficial for the organization which is why the business should not skip this core data management function. All the data cleansing activities that are mentioned above will provide you with cleaner customer data which will act as a critical part for contributing to the business growth.

 

Since cleansing the data not only gives you good quality data but it also brings uniformity in the data sets that are merged from different sources. Also, your job of maintaining and storing good quality data doesn’t simply end with cleaning the data. You need to take utmost care of the incoming data so that they are consistent with the similar data sets that are used by the organization.

 

Data cleansing regularly can help you in using the data on time so that the benefits can be extracted before the data decays naturally. There may be many reasons for the same therefore the data scrubbing activities should be vigilant.

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Kartik Jha

Author Kartik Jha

Digital Marketing Manager of Only-B2B.com with 4.5+ years of experience in Internet Marketing.

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