A business process involves lots of operations with respect to the storehouse of information and database. Information relevant to the business, clients and customers has to be stored in a database so that it can be utilized as and when needed. Handling bulk data, however, is not going to be an easy task. Also, it may be increasingly difficult to find data when needed. Having tons and tons of data, however, is relevant to the business needs as it is needed for planning and operations of a business. In addition, such data also becomes a part of decision making. Handling large volume of data is definitely cumbersome. This becomes compounded when inconsistency prevails throughout the dataset. Incomplete and inconsistent data is the cause of dirty data. Datacleaning should be initiated to clean the data in order to make it useful.
Step#1: Identifying Error
Data cleansing begins with the process of identifying errors. There may be errors or inconsistencies in files, which makes the entire set of information incomplete or irrelevant. Data bits can also be corrupt. It is very important to initiate the process of cleansing such data in order to make a consistent or concise database. Furthermore, the database should include all information and data intact, which is absolutely necessary. Generally, the process of data identification seeks to identify the errors as well as classify the data as non-critical (in which errors do not lead to operational problems) or critical (in which errors have to be fixed).
Step#2: Reporting Error
Whether a manual or automated system is used, you have to perform the process of error verification in order to cleanse data. As humans, it is needed to look into the information before making the final judgement call – whether there is truly any error or not- whether the error is non-critical or critical- such things have to be assessed. This step will involve report generation and getting it checked all over again to assess accuracy.
Step#3: Data Cleaning
Data stored in the database is normally cleaned through an automated system. However, it is also likely to be carried out manually. All the data errors that have been identified and verified during the process of reporting error have to be fixed as early as possible. It will be an extremely crucial step as the critical errors have to be identified and addressed. Non-circtial errors, however, can be ignored.
Step#4: Checking Error
After the process of cleaning is over, error audit has to be done for all the data. This particular step has been designed to ensure that all data errors that were identified have been corrected. Additionally, it will be able to spot any new error or inconsistency that has developed. If any such errors are identified, the process of reporting and cleaning should be repeated.
Step#5: Data Merging
The process of cleaning data cannot be initiated on the main working data, rather, a second identical data has to be generated and cleaned; this prevents any risk of irreparable damage. Once the process of data audit is run successfully and verified, the cleaned data is ready to be used. It will replace the actual data. For more Information visit this Data cleansing tools