What Are the Different Methods Used for Data Cleansing?

What is data cleansing and why is it important?

Having an updated database is a vital requirement for organizations these days, given that most of the business comes through marketing. An updated database will ensure that the contacts are correct and you can connect with the customers efficiently while keeping up with the compliance standards. Now, with everyday use by different team members there are high chances of the data getting corrupt (incorrect or jumbled up). It is also a possibility that some contacts may become obsolete over the time and needs replacement.

That is where data cleansing comes to rescue, since with this process you can ensure that you identify any inaccurate data and consequently correct them. Therefore, the data cleansing process aims to maintain clean (steady and correct data) customer database by finding out any inaccurate data (incorrect, obsolete, or incomplete) and remove the dirty data thereby, creating a single record for individual customer with all of their related information. While manual maintenance is usually followed, using the data cleansing tools is also picking up pace these days due to the complexity the database administrator has to face. Before we discuss the different methods used for data cleansing let’s see why cleaning data and maintaining an accurate customer database is so important.

1. Keeping up with the compliance standards that is, Data Protection Act is an important aspect for any organization and so, the data cleansing plays a vital role here.

2. Cleaning data on a regular basis ensures that there’s minimum wastage of information, that is, less wrong emails. This automatically cuts down the mailing costs thus, help your business save some resources.

3. Customer data is crucial for any business and so, you must make sure to maintain a clean database that enables quick repair of customer information thus, lowering the turnaround time.

4. Having all data at one single place not just enhances the quality of service, but also offers improved customer experience.

5. Marketing your business to potential clients is the major survival strategy for all enterprises, and so, a clean customer database will ensure that you have correct client information helping to generate better sales targets and proper management.
That being said, it’s still a tough task to manage a clean customer database.

Customer information keeps changing frequently and so, get obsolete very soon. Moreover, the customer databases in many businesses may have multiple information based on different parameters such as, buying history, list of prospects, or email list. This can create a lot of confusion and mix up since the details of the same customer may appear on different databases with fragments of significant information under each parameter.

So, if you are asking for the methods used to clean data, it’s important for you to know that that it entirely depends on the different software used by the business like, the type of CRM, marketing automation and any other software that you are using. Irrespective of the method you choose, it’s usually a lot challenging to clean data manually as it will consume a lot of time and efforts that directly affects the overall productivity of the company. But, if you are looking for a specific method, you would first need to identify the type of data clean-up you want to perform. The method adopted would totally depend on whether you want to append data, remove duplicate data hk   entries, standardize data, delete non-usable contacts, verify the email list, and so on. And, that’s the

Therefore, your life is much easier when you outsource the data cleaning process and seek help from different data cleansing tools available in the market. Data Ladder is one such efficient tool that’s known for it advanced semantic technology that helps maintain the customer database by removing any unwarranted errors or duplicate entries that might be creating all the confusion. It therefore, reduces the time spent on the entire data cleaning process and cuts down costs considerably thus, helping to improve the productivity of the company.

 

 

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