Implementing a Data Quality Model
Data quality is likely the single most important reason why companies tackle MDM. Trusted data delivered in a timely manner is any company's ultimate objective.
However, there are many aspects of data quality, including, among other things, the source of bad data as well as the actual definition of what bad data really is and its associated representation. Let's take a look at a couple examples to illustrate this further:
- There is no contention about what the two acceptable denominations are for the attribute gender. However, one system may represent it as M/F, another with 1/0, another with Male/Female/MALE/FEMALE, another without any validation whatsoever, with a multitude of manually entered values that could be missing, correct, or incorrect. Furthermore, it is possible to have a correct gender value improperly assigned to a given person. In the end, this information can be critical to companies selling gender-specific products. Others, however, may not be impacted so much if a direct mail letter is incorrectly labeled Mr. instead of Mrs.
- Some data elements may not even have an obvious definition, or its definition is dependent on another element. An expiration date, for example, has to be a valid date in the calendar as well as later than an effective date. In another scenario, some customers are eligible for a certain service discount only if they have a gold account.
The previous examples show just one facet of data quality or lack of ...