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Businesses shouldn't procrastinate on data quality management

Rachel Wheeler Archive
More than half of U.S. consumers now use smartphones and regularly use them to browse the web or make purchases. Facebook now has more than 900 million users and ecommerce purchases are expected to reach $224 billion by the end of the year, according to eMarketer. Every time consumers participate in these digital channels, clicking links and entering personal information, they are generating data.

An article that was recently released by Castlebridge Associates explains how one customer generates multiple pieces of information through very basic interactions. The company gives the example of a pizza shop, which may keep customers' home addresses, email addresses, home phone numbers, mobile phone numbers and social network accounts in its server. The business stores the phone numbers and addresses in the server to streamline future orders, and adds social media information when customers start following them to show their support or get special promotions.

If businesses take advantage of this vast amount of information by organizing it into actionable insight, it can be used to fine tune their marketing and customer satisfaction efforts.

Some companies tend to neglect data quality

Unfortunately, many companies don't pay enough attention to their data quality, instead waiting for a good opportunity to cleanse their systems in one fell swoop. In a recent IT Business Edge article, blogger Loraine Lawson likens data quality to doing laundry - nobody wants to do it, but letting it pile up is a mistake.

Lawson explains that businesses should develop a routine for maintaining data quality, thus ensuring all of the data they have on file is accurate and up-to-date. Allowing this information to decay renders it useless and will affect the return on a campaign.

To avoid this outcome, companies can schedule data quality cleansing when another project coincides, such as a new CRM system, a master data management initiative or data migration, Lawson adds.

Cleansing during data migration requires tough decisions

When businesses wait until data migration to cleanse their systems, they are often forced to check quality and make changes in environments that pose additional challenges, reports The Data Roundtable. If they decide to wait until the migration is complete, they will have to make changes once the data is already live. Updating information in a staging area between the legacy and target systems could leave gaps in the old information.

If companies do wait for a large-scale migration to encourage the change, they can use data quality tools to aid in the cleansing process.