What is Data Cleansing?
Data cleansing, also known as data scrubbing, is the process of ensuring that a set of data is correct and accurate. During data cleansing, records are checked for accuracy and consistency, and either corrected, or deleted as necessary. Data cleansing can occur within a single set of records, or between multiple sets of data which need to be merged, or which will work together. At its most simple form, data cleansing involves a person or persons reading through a set of records and verifying their accuracy. Typos and spelling errors are corrected, mislabeled data is properly labeled and filed, and incomplete or missing entries are completed. Data cleansing operations often purge out of date or unrecoverable records, so that they do not take up space and cause inefficient operations. In more complex operations, data cleansing can be performed by computer programs. These data cleansing programs can check the data with a variety of rules and procedures decided upon by the user. A data cle
Data cleansing or data scrubbing is the act of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database and Data validation. Data cleansing, also known as database cleansing or data scrubbing, is the process of ensuring that a set of data is correct and accurate. Nowadays companies heavily rely on computerized data in their normal operations so data cleaning is a very important task. During the data cleansing process, different tools are used to check records for accuracy and consistency, and either corrected, or deleted as necessary. Data cleansing process uses different software and tools and can occur within a single combination of database records, or between multiple sets of data which need to be merged, or which will work together. Database cleansing at its simplest level, involves a person or persons reading through a set of data records and verifying their accuracy. Typos and spelling mistakes are corrected, mislabeled database
The elevator pitch: “Data cleansing ensures that undecipherable data does not enter the data warehouse. Undecipherable data will affect reports generated from the data warehouse via OLAP, Data Mining and KPI’s.” A very simple example of where data cleansing would be utilized is how dates are stored in separate applications. Example: 11th March 2007 can be stored as ’03/11/07′ or ’11/03/07′ among other formats. A data warehousing project would require the different date formats to be transformed to a uniform standard before being entered in the data warehouse. // //]]> // //]]> Why Extract, Transform and Load (ETL)? Extract, Transform and Load (ETL) refers to a category of tools that can assist in ensuring that data is cleansed, i.e. conforms to a standard, before being entered into the data warehouse. Vendor supplied ETL tools are considerably more easy to utilized for managing data cleansing on an ongoing basis. ETL sits in front of the data warehouse, listening for incoming data. If
Data cleansing involves detecting inaccuracies in records and correcting them so the data set remains consistent. What causes these errors that result in data cleansing? An inaccuracy could be caused for any number of reasons including a data entry mistake, corruption in transmission or similar entities replacing originals. One market that data cleansing is common in is in the direct mail marketing industry. Problems that result from inaccurate mailing lists contribute to an annual loss of 600 billion dollars by major companies worldwide. That sounds like a problem that needs data cleansing address solutions! Data cleansing may involve removing typos and correcting data on mailing lists after validation. In order for quality data cleansing to be effective, mailing lists have to be updated regularly. When data cleansing software is utilized, companies who print mailing lists usually report improved response rates for their direct mail marketing campaigns. Residents are more likely to re
Data cleansing (or ‘data scrubbing’) is detecting and then correcting or removing corrupt or inaccurate records from a record set. After cleansing, a data set will be consistent with other similar data sets in the system. The inconsistencies detected or removed may have been caused by different data dictionary definitions of similar entities in different stores, or caused by user entry errors or data which was corrupted in transmission or storage. Preprocessing the data will also guarantee that it is unambiguous, correct, and complete. The actual process of data cleansing may involve removing typos or validating and correcting values against a known list of entities. The validation may be strict such as rejecting any address that does not have a valid ZIP code or fuzzy such as correcting records that partially match existing, known records. Data cleansing differs from data validation in that validation almost invariably means data is rejected from the system at entry and is performed a
