There are various reasons due to which the data of the organization becomes bad and not usable for effective business processes and decisions. Improper updating an entry within the system and inefficient import of data from outside make the data more corrupted. This bad quality data creates wastage of time and money in the organization. You canâ€™t rely on bad quality data for any of the business decision making. Therefore proper methods of data quality improvement have to be taken in the company to ensure good data flowing in the company. Data cleansing is an important process of the data quality management process in an organization and is the process of detecting corrupt or inaccurate records in the database and correcting it.
Data cleansing includes identification of incomplete, inaccurate, incorrect, irrelevant and other erroneous parts of a data and then replacing, modifying or deleting this data for improving the quality of data in the system. After data cleansing, the data becomes consistent with other similar data sets in the system. Data cleansing software is the software which helps in performing the process of data cleansing automatically in the system and are popular nowadays to make the function easier.
What are the processes expected in a good data cleansing software?
It is much obvious that if you are going to buy a data cleansing software for your company to perform the processes of data cleaning, you need to select the best possible software in the market. Though there are much software available for you to decide, you need to make sure that all the aspects of data cleansing are met when you use the data cleansing software. On the basis of the ideal processes of data cleansing mentioned here, you can decide which cleansing software is the best for you.
Data auditing- A good software audits the data by using statistical and database methods for detecting the anomalies and contradictions. You can specify constraints of various kinds by using this software and then generate code lines which check the data for violation of the specified constraints.
The software then performs the detection and removal of the anomalies with the sequence of operations called workflows. This is performed after the process of data auditing and very important and crucial in achieving an end product of high data quality. The causes of these data anomalies also have to be considered to make a proper workflow.
After this, the workflow is executed when the specification is complete and the correctness verified. The implementation of this workflow should be efficient.
After the cleansing workflow is executed, the results are verified for the correctness. Data which was not corrected can be again corrected through additional workflow to further cleanse the data by automatic processing.