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 example.
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 unique.
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.
People generally think of data quality as a remedial exercise. During the ongoing course of business, for a variety of reasons, companies find themselves with incorrect data. The goal of a data quality program is to identify the incorrect data and fix it.
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 example.
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 unique.
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.
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