How to customize your data quality solution for better results  

Data quality tools are designed to meet the needs of a wide range of users and include tools to process common elements including names, addresses, contact information, and other personal data. As powerful as they are in their default settings, customizing your data quality tool to meet the needs of your business and application will maximize your return on investment by eliminating manual review, keeping data clean, and providing users with data that meets their unique needs.


Data quality solutions use reference data to identify and parse data, correct misspellings, and remove unwanted characters. While these dictionaries can contain millions of words and phrases, they should also include industry-specific terms, regional spellings, and proprietary information specific to your business for even better performance. Adding your own definitions to the reference database makes data processing faster and more effective by eliminating the need to manually review words that are unique to your industry or location.   


One of the most powerful functions of a data quality solution is finding links between records that identify individuals and companies, allowing the solution to remove duplicate records. Your preferences for what constitutes a match and the threshold between an automatic match and a manual review may differ significantly from the default. By fine-tuning your parameters, you can reduce under-matches that require manual review and over-matches that reduce the accuracy of your data.  


Householding is the process of bringing together multiple records within a single address, which has obvious benefits for sales and marketing, including reduced mailing costs and identifying cross-sell opportunities. Your definition of what constitutes a household will vary depending on your industry, location, and even how your product or solution is purchased and distributed. Customizing the definition of a household ensures that you are not missing potential sales opportunities.   


Data quality solutions have default maps for how fields are imported into the solution and exported to other applications. These maps work well if fields are consistent, but data can vary significantly in quantity, layout, and format, creating data quality issues and errors that require additional review and manual processing. By customizing the map for each data field, you can correct differences in format, character length, validation, and other parameters so that data transfers seamlessly. You can also customize data to fit different users’ needs so that each department within your organization receives only the data they need in the format that best suits their application.

By customizing your data quality solution to accommodate the unique needs of your industry and application, you reduce the costs associated with manual review and error correction while delivering added value to those who rely on quality data to be successful. So be sure to dig into the settings of your data quality solution or ask your provider for assistance in customizing the tool to suit your needs.