Data Prep & Cleansing
What is Data Cleansing?
Techopedia describes data cleansing as, “The process of altering data in a given storage resource to make sure that it is accurate and correct. There are many ways to pursue data cleansing in various software and data storage architectures; most of them center on the careful review of data sets and the protocols associated with any particular data storage technology. Data cleansing is sometimes compared to data purging, where old or useless data will be deleted from a data set. Although data cleansing can involve deleting old, incomplete or duplicated data, data cleansing is different from data purging in that data purging usually focuses on clearing space for new data, whereas data cleansing focuses on maximizing the accuracy of data in a system. A data cleansing method may use parsing or other methods to get rid of syntax errors, typographical errors or fragments of records. Careful analysis of a data set can show how merging multiple sets led to duplication, in which case data cleansing may be used to fix the problem.”
Why do I need to cleanse my data?
TechRepublic explains, “Poor data quality adversely affects your organization in three key ways:
Poor data quality causes inefficiencies in those business processes which depend on data—reports, ordering products, voter registration, and just about everything else for which facts are required. These inefficiencies result in very expensive rework efforts to “fix” the data in order to meet the requirements of various processes.
Poor data quality gives rise to poor decisions. A decision can be no better than the information upon which it’s based, and critical decisions based on poor-quality data can have very serious consequences. This is another reason why you should make sure that your data actually represents reality.
Poor data quality creates mistrust. Poor data quality can reflect adversely on your organization, lowering customer confidence. If the data’s wrong, time, money, and reputations can be lost.
As the usefulness of data extends beyond ordinary business transactions to the support of business-intelligence initiatives, data-quality issues are going to become increasingly evident.”
Why choose Competitive Analytics to clean my data?