How Data Cleansing add Value to Customer Data

It is essential for companies to have a simplified database, both for confirming effective contact with their clients and preserving agreement standards. Data cleansing is the method of correcting inaccurate data within the database.

It refers to recognizing the inaccurate data and then modifying it properly. With reference to client database, data cleansing is the process of keeping reliable and precise database through removal of inaccurate data.

Importance Of Data Cleansing

The decisive goal of data cleansing and maintaining a clean database is to duplicating multiple records and creating one with all relevant data. This process of data cleansing and maintaining clean client database offers various benefits to business, including:

  • A clean database can provide more precise prospect information which helps to better sales targeting and management.
  • Data cleansing helps to remove invalid emails and can save on mailing costs.
  • Easily group and filter data for segmenting your database.
  • Improve the efficiency of customer acquisition.
  • Enhance the email deliverability rate

Though, sustaining a clean client database is a difficult task. Further, many companies, based on different criteria (email-list, prospect list, purchase history) have various databases. A few steps that can help in combining the client data maintain clean database are as below:

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Procedure To Cleanse Data:

1. Data Audit:

  • The principal step to data cleansing, is the thorough auditing of all customer databases. The reviewing should be done using statistical and database methods to detect inconsistencies and inaccuracies. The information should be used to infer characteristics and location of inconsistencies, which can lead to root cause of the problem.

2. Merge Data:

  • The process of cleansing the database should not be restricted to just the recognizing and removal of inaccurate data from client database. It should be used as a prospect to combine customer data and other information like email ids, phone numbers or additional contacts should be combined whenever possible.

3. Feedback:

  • The business should launch a control mechanism where any erroneous data gets reported and gets updated into database. For instance, there should be a control and feedback mechanism for emails and any email which is undelivered owing to an incorrect address, should be reported and the invalid email address cleansed from the customer data.

4. Use Various Methods:

  • The process of auditing of a database should not be restricted to analysis through statistical or database methods and further steps like buying external data and comparing it against internal data can be used. Moreover, if a business has restraints of time and employees, it can use the services of external company. However, in this approach, the business needs to be careful with respect to their brand image and the way of working of external company.

Data cleansing is a difficult yet critical process and needs devotion of dedicated time and resources. The processes stated above would certainly help in the making of a clean database which deals several benefits across functions and serves as a critical factor in the growth of business. Hence, businesses should make investment in data cleansing and data management a top priority.

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What is Data Enrichment and What are The Various Benefits?

Data enrichment is a handy tool that can add value in improving the quality of the organization’s data. It is best described as the process of enriching data as a set of method and structures for refining and better value from the data.

One of the roles of enriching the data is to make it a more valuable asset. It means that the data can be used to get more information out of it so that you can make it more useful for the organization without incurring too much cost.

How Does It Work?

Any organization has its goals to add value to the data. But many tools that are used for the purpose are universal in their refinement process. Whether it is ensuring the accuracy of the logs, or adding new data to the tables, or correcting the typographic or spelling errors, these tools can improve the quality of the data on various fronts. So no matter what your plan is you can make use of the right tools to enrich the data.

What are the benefits Of data enrichment?

There are many benefits of the same. Let’s start taking a look at them one by one.

1. It ensures accuracy of algorithms

  • The organizations keep on changing the data-system, it can result in a bad decision making in the long run. A data-enrichment process ensures that the algorithms are more accurate and the decision making is better.

2. Accuracy of data

  • It eliminates the irrelevant and the outdated facts which will help you in making the right decisions which are the very health of the organization.

3. Recurring entries are taken care of

  • It also takes care of the duplicate entries and updates them on the files. When you find these recurring entries, you can well decide whether it will be good to eliminate them or not.

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What are the key steps?

With the help of few simple steps, data enrichment can be implemented within your organization. The first step is to do the quality assessment of all the data with the content management systems. It is important the assessment methodologies cover the essential areas of the integrity, consistency, and completeness. It the #data can’t remove these four areas it might need to be repaired or you can get rid of it altogether. Your strategies go a long way in resolving the issues in your data structure and will make the difference in this initial assessment process.

Once this step is complete, it will be necessary to determine the best way to enrich the unique data in the organization. This process is unique for each organization. Experts suggest that the data enrichment should start by cultivating an original outline for the need of the organization. It must be followed by necessary research to ensure the effective strategy by locating data from various other sources and checking it for the quality before implementation.

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