Introduction to Data Quality

Data Quality involves ensuring the accuracy, timeliness, completeness, and consistency of the data used by an organization so that the data is fit for use. Spectrum Technology Platform supports data quality initiatives by providing the following capabilities.


Parsing is the process of analyzing a sequence of input characters in a field and breaking it up into multiple fields. For example, you might have a field called Name which contains the value "John A. Smith" and through parsing, you can break it up so that you have a FirstName field containing "John", a MiddleName field containing "A" and a LastName field containing "Smith."


Standardization takes data of the same type and puts it in the same format. Some types of data that may be standardized include telephone numbers, dates, names, addresses, and identification numbers. For example, telephone numbers can be formatted to eliminate non-numeric characters such as parentheses, periods, or dashes.

You should standardize your data before performing matching or deduplication activities since standardized data will be more accurately matched than data that is inconsistently formatted.


Matching is the process of identifying records that are related to each other in some way that is significant for your purposes. For example, if you are trying to eliminate redundant information from your customer data, you may want to identify duplicate records for the same customer; or, if you are trying to eliminate duplicate marketing pieces going to the same address, you may want to identify records of customers that live in the same household.


Deduplication identifies records that represent one entity but for one reason or another were entered into the system multiple times, sometimes with slightly different data. For example, your system may contain vendor information from different departments in your organization, with each department using a different vendor ID for the same vendor. Using Spectrum Technology Platform you can consolidate these records into a single record for each vendor.

Review of Exception Records

In some cases you may have data that cannot be confidently processed automatically and that must be reviewed by a knowledgeable data steward. Some examples of records that may require manual review include:
  • Address verification failures
  • Geocoding failures
  • Low-confidence matches
  • Merge/consolidation decisions

The Data Stewardship Portal contains features that allow you to identify and resolve exception records.