By Elliot King
And while data errors inevitably do occur, an essential element of a data
quality program is putting technology and processes in place that will ensure as
much as possible that the data captured initially is correct. It stands to
reason that the higher the quality of data at the front end, the less extensive
the remediation will have to be later on.
Precise and comprehensive business rules can play an important role in
protecting data quality both as data is captured and as data is used. Broadly
speaking, in building a database, data quality business rules can be classified
into four categories–rules that describe how a business object is identified;
rules that describe the specific attributes of a business object; rules that
control the various relationships among business objects; and rules that define
the validity of the data. A business object can be thought of as a collection of
data points that form a complete unit of information–a customer record for
Each of those broad categories contains different possibilities that must be
defined. For example, each business object must have a unique identifier. That
record can be a newly generated number such as a purchase order number or a
customer identification number or it can consist of a number of data points in a
record such as name and telephone number. The key is for the identifier to be
The relationships between business objects must be set. For example, a
professional baseball player can be associated with only one team at a time, but
a team can be associated with many players. Valid values for data have to be
determined. Is the “year” value in a date two digits or four digits. Y2K is an
excellent example of how significant that rule can be.
Carefully constructing the business rules for your data will ensure that you
know the information you have and assist you in applying it correctly.
Unfortunately, too often the development of business rules is a black-box
operation implemented by software. When people do not know the business rules
defining their data, mistakes happen.