Jul
24
2012

How to overcome dirty data

Customers are the life blood of every organization. With customers comes information, and no matter the size or the industry, every organization has some sort of data repository that houses contact details. This information is critical to operations and allows businesses to market and serve their customers and prospects appropriately.

Because of the fluidity of customer relationships, businesses have a hard time keeping contact data clean and up to date. Most organizations try to keep data clean, but there are many that view inaccurate data as a standard part of doing business. While that is true to a certain extent, data can be maintained and cleaned to enable more efficient and cost effective business practices if organizations put certain processes in place.

According to a recent Experian QAS study, 87 percent of organizations manage the accuracy of their contact data in some way, but 92 percent do not completely trust their data in terms of it being completely clean, accurate and up to date.

Data quality should be a standard part of daily operations, rather than a simple clean that happens every quarter. The first step in any data strategy development process is analyzing internal data to find out where errors occur and what types are most prevalent. Once those are identified, an appropriate strategy can be acquired.

Here are some common practices to consider.

  • Staff training – no matter the organization, data quality cannot be achieved without buy in from staff. Train staff members on the importance of data quality and let them know how inaccurate information affects the business.
  • Manual cleansing – implement manual audits to ensure that staff members are following data quality standards. Look for missing or incomplete information and follow up with each individual staff member. While this is an important step, too much manual work will lead to mistakes.
  • Point-of-capture and automated validation – contact data validation tools are the only way to ensure that each new record is entered accurately and standardized.
  • Duplicate removal – Once customer data is correct, organizations can better identify duplicate records. Businesses should review the size of their database and the requirements for duplicate identification in order to determine the most appropriate duplicate removal software tool.

Dirty data is a problem that every organization faces. But data inaccuracies do not need to be a part of standard operations. Review data to find common errors and affected departments. Then identify solutions that will solve those issues and develop a data quality strategy.


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