Understanding The Process Of Data Cleansing

Global corporations generate and archive large volumes of data on a daily basis. It is necessary to record and safeguard this data in an efficient manner so that it can be referred to and relied upon in the future. However, keeping data reliable and updated is not an easy task. Sometimes it demands huge manual effort and also man-hours that can jeopardize the other supplementary operations of an organization. Reliable and consistent data ensures that the organization is able to make sound judgments and accurate appraisal of business performance.

It is here that the role of data cleansing takes center stage. Data cleaning, also referred to as data scrubbing, is a process that enhances data quality by eliminating redundancies, erroneous details etc.

Simply put, data cleansing improves data worthiness significantly. It is a must for organizations. A well-devised data cleansing strategy will help an organization to achieve the consistency in intensive data that has to be used in multiple operations significantly.

Factors that make data error prone or redundant:

1. Manual errors at the time of data entry:

Processes that involve manual intervention are highly prone to mistakes. When a person performs a task repeatedly on a regular basis, there are chances that he or she might make mistakes inadvertently or due to a lack of focus. Such mistakes can reduce the data quality and make it less reliable for further use.

2. Lack of a standard data entry system:

A data entry system which lacks proper controls - requiring users to feed in data with correct form and structure - can create several incorrect and redundant records.

3. Inadequate system controls that permits entry of incorrect or redundant data or duplicate data:

The system should be configured in a manner that does not permit users to enter data with incorrect, redundant data or duplicate data. The system should ask for entering complete information in a standardized format. Besides, this will help prevent any possibility of slowing down decision making due to duplicate entries.

4. Errors when two or more databases with varying metrics are merged:

Data errors and redundancy largely happens when two or more databases are merged. This could be controlled only by achieving a common number of fields and parameters that will reduce the possibility of data becoming unusable.

How data quality can be improved using data cleansing?

A data cleansing strategy will help organizations put in place protocols about how data should be created and archived. The process of data cleansing can be automated with the help of item data management tools and data scrubbing software. These software analyze data for common errors such as unavailability of data, non-standardized entries, duplicate records, etc. They purge duplicate entries and remove non-standard entries after confirmation from all related users.

Data cleansing tools provide the immense benefit of restoring the use worthiness of past records. They remove unnecessary spaces, special characters, or other errors that may make the data unworthy for use. With better quality of data, an organization will be able to use its resource more efficiently.

As companies grow their data volumes, requirements also scale exponentially. It is necessary to have multiple data cleansing tools that will improve its reliability and accuracy significantly. Databases will be able to provide better results with less responsive times and with enhanced quality.

The author is an expert in the field of master data management. The writer has flair for writing and he keeps on writing various articles and blogs related to the industry.