What is Data Cleansing and How Does It Work?

Data cleansing is a process that involves the uncovering and correction of inconsistencies and errors in data that are gathered from various sources. It is a process also known by the name of data scrubbing. Data that is incomplete or has mistakes within it, can have several causes. These include accidental deletion, user error, and outdated information. This type of data is known as coarse data. It qualifies when different parts of it are incomplete, inaccurate, irrelevant, or inconsistent. Once data that is coarse has been identified, the process of data cleansing includes correcting, deleting, updating, and modifying every entry as is necessary.

Large companies die or live based upon their data. Because of this, one might think that the marketing data would be carefully recorded, strictly controlled, and maintained. However, this is not the case in every circumstance. In non-tech companies, in fact, this is seldom the case. Maintenance of databases is not anyone’s top priority. Combing through contacts in the thousands is a task that is daunting, but it can pay rich dividends.

When it comes to finding data is a complete mess, the problems can be varied and widespread. Some common problems when it comes to gathering the data are misspelled names, incorrect addresses, wrong contact information, and much more. These problems cannot be avoided and the information that is being gathered is crucial in the lead gathering process. It is essential to have good data entry employees who can find and correct these mistakes easily.

The first step to solving some of the common data cleansing problems as discussed before, is that it can be time consuming to sort through all of the incorrect data. It can take full days, even weeks, to accomplish. Phase one of data cleansing is fixing issues with formatting and case. This is the most common issue in data inconsistency. Phase two is removing unwanted characters and whitespace. Phase three is consolidating and standardizing for the improvement of filtering.

Roughly 30% of databases go bad each year. About 10%-25% across databases contain errors that prevent someone from doing their job effectively. This is potentially a major problem for CMO’s and marketers. They invest a great deal of time, resources, and fund on a program for marketing automation only to discover errors in the database that prevents them from effectively targeting audiences.

The solution to some of these common problems is to surround yourself with capable professionals who know that these problems exist and can correct them quickly. Data cleansing is a timely business. Be open with your clients about your process and ensure that the data you are retrieving is valuable to them and their goals.

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