Data cleansing is an essential process to ensure that your business makes the right decisions based on accurate and up to date information. WinPure is your ideal partner and has been involved in cleaning data since 2003 and has helped more than 3000 businesses worldwide to clean, correct and deduplicate their lists and databases, ensuring they save time and money, time and time again
What is Data Cleansing?
Data cleansing (also known as “data cleaning” ) is a method used to identify and correct, duplicated or inaccurate records within a list or database. It involves identifying all inaccurate information on the database and either deleting them or replacing them with accurate information. It can be done through different techniques such as data transformation, data deduplication and the use of statistical methods.
Business benefits of Data Cleansing
Cleansing of data is of great importance to any business. It is advisable for any business to perform a data cleansing process regularly in order to make proper decisions in relation to the performance of the business, save on time and maintain the good reputation of the system. Benefits include:
Reducing Marketing Costs: It is estimated that 37% of an average sized business database will decay every year. Improving the integrity of business data by identifying and removing those businesses that have moved address or closed, and ensuring that individuals/organisations are not mailed more than once with the same offer, will reduce wasted mailing costs associated with inaccurate information.
Accuracy: A database with accurate, onsistent and duplicate free records — Accurate addresses equates to improved response rates that could result in increased revenue
Compliance: B2B organisations have a legal responsibility to remove individuals who have registered with the Fax Preference Service and the Corporate Telephone Preference Service. Ensuring compliance with these data regulations not only removes the risk of incurring financial penalties, but also protects the organisation from complaints and negative publicity.
Examples of Data Cleansing
An example of data cleansing can be seen below. Here we have a list (“Dirty Data”) that contains un-standardized records, some missing and misspelled data and contains duplications. By performing a data cleansing excercise we can successfully convert this to “Clean Data” which is now correctly standardized, is populated and correct, and contains no duplicated records.