It is necessary for organizations to have an updated database, both for ensuring efficient contact with their customers and maintaining compliance standards. Data Cleansing or data scrubbing is the process of identifying and correcting inaccurate data from a data set. With reference to customer data, data cleansing is the process of maintaining consistent and accurate (clean) customer database through identification & removal of inaccurate (dirty) data. Here, inaccurate data stands for any data that is incorrect, incomplete, out-of-date, or wrongly formatted.
The ultimate goal of data cleansing and maintaining a clean customer database is to create a “single customer view” meaning that there is only one record for each customer that contains all their relevant data. This process of data cleansing and maintaining clean customer database offers multiple benefits to business, including:
- Data cleansing is a critical tool that helps in maintaining compliance with the Data Protection Act
- Data cleansing helps to reduce wastage in the form of incorrect emails and saves on mailing costs.
- Maintaining a clean database allows for swift location of relevant customer data and reduces service response time.
- It also improves the service quality as all relevant data is located at same place and results in better customer experience.
- Clean customer data can provide more accurate prospect information leading to better sales targeting and management.
However, maintaining a clean customer database is a difficult task. Customer data is highly dynamic and tends to go out-of-date quickly. Further, many businesses, based on different criteria (purchase history, email-list, prospect list) have multiple databases. This leads to the same customer being present on multiple databases with bits of relevant information under each criterion. A few steps that can help in consolidating the customer data maintain clean database are as follows:
Procedure to Cleanse Customer Data:
Table of Contents
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Data Auditing:
The first step towards data cleansing, is the complete auditing of all customer databases. The auditing should be done using statistical and database methods to detect anomalies and inaccuracies. The information should be used to infer characteristics and location of anomalies, which can lead to root cause of the problem.
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Use Multiple Methods:
The process of auditing of a database should not be limited to analysis through statistical or database methods and additional steps like buying external data and comparing it against internal data can be used. Additionally, if an organization has constraints of time and staff, it can use the services of external telemarketing company. However, in this approach, the organization needs to be cautious with respect to their brand image and the way of working of external company.
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Consolidate Data:
The process of cleansing the database should not be limited to just the identification and removal of dirty (inaccurate) data from customer database. It should be used as an opportunity to consolidate customer data and additional information like email addresses, phone numbers or additional contacts should be incorporated whenever possible.
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Feedback:
The organization should establish a control mechanism where any inaccurate information gets reported and gets updated into database. For example, 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.
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Repeat:
People’s lives are increasingly becoming dynamic and so associated details like addresses, telephone number, company email-id, change frequently. Thus, the process of data cleansing should not be thought of as a one-time process; instead, it should made a part of the regular workflow. Regular weeding of inaccurate information and updating customer database is the only route towards ensuring clean customer database.
Data cleansing is a difficult yet critical process and requires dedication of committed time and resources. The procedures mentioned above would certainly help in the creation of a clean customer database which offers multiple 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.
| Sl.No | Article |
| 1 | 6 Reasons Why Data Management Leads To Business Success |
| 2 | Three Major Causes of Enterprise Data Quality Problems |
| 3 | 5 Best Practices of Effective Data Quality Management |
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