Data Cleansing

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by: Gwyn | Total views: 5 | Word Count: 630 | View PDF | Print View
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In every business, data is very important to keep the business growing. Just like humans that need food to survive and healthy foods are much needed of the body. Bad foods will just deteriorate our body. Just as data is everywhere in business and marketing and learning from one's own company data is the ideal way to learn, change and grow. So data can be healthy or bad also. Realizing its importance, irrelevant data thus needs to be cleaned. So how clean is your data? To get information or knowledge out of it, first it should be pure and not polluted.

To eliminate those problems, we have what we call Data Cleansing. Others termed it as data scrubbing. It is the act of detecting and correcting or removing corrupt or inaccurate records from a record set, table or database. Usually used in databases in identifying incomplete, incorrect, inaccurate, irrelevant parts of the data and then replacing, modifying or deleting this dirty data. Dirty data could be data that is misleading, incorrect or without generalized formatting, contains spelling or punctuation errors or data that is input in a wrong field or duplicate data. Any of these problems can reduce the effectiveness and business value of customer and prospect data. A database with accurate and consistent records equates to improved response rates that could result in increased profit.

Successful marketing on one end depends on understanding who your best customers are and knowing how to reach them. How you will be able to deal with the headaches, cost and resources of cleaning dirty databases loaded with misspelled addresses, human error, and inconsistent, inaccurate data is essential.

Dirty data caused also by people moving so frequently and businesses coming and going, then marketers begin to lose contact with their customers. In a year, approximately 6 million people move addresses and half a million addresses are affected by postal address changes. In a year also your data stored can easily be outdated. Your response rates may fall and audience reach becomes limited. You could be wasting a lot of money on marketing campaigns on invalid and outdated information.

How you maintain data as well as creation rules are very important for it is a critical factor for the success of enterprise intelligence initiatives. If the information shared across the organization is contradictory, inconsistent or inaccurate, then interactions with customers, suppliers and others will be based on inaccurate information, resulting in higher costs, reduced credibility and lost business. That is why the goal of data cleansing is not just to clean up the data in a database but also to bring consistency to different sets of data that have been merged from separate databases.

Bad data on one system can easily and rapidly propagate to other systems. Data cleansing is important to the efficiency of any data dependent business. If some of the clients within a database do not have accurate phone numbers and if your clients' email addresses are not formatted correctly, an automated email system would be unable to send out the latest coupons and special deals or anything new about your business. Data cleansing job is to ensure that the data within a system is correct, so that the system is able to use the data.

Data Cleansing may have been mistaken as data validation. These two differs in a way that 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 postal code or correcting records that partially match existing known records. Data cleaning softwares are also created to make the task easy and fast.
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