Characteristics of Quality Data

People depend on the systems they use to contain high‐quality data. If

they find the data to be wrong, outdated, or incomplete, they begin to

distrust the system and will likely stop using it. If the data in the system is

personally important to the individual, such as the data in payroll or

medical systems, then there is a strong need to have it corrected as

quickly as possible.

What are the characteristics of good‐quality data? There are a variety of

characteristics, but we will focus on six. Let's look at the data that may be

in a payroll system and how each of the characteristics of quality data are

important, and consider an example of each.

• Accuracy – Is the information correct? For example, is the annual

salary correct?

• Completeness – Is all the information there? For example, if overtime

was worked, is it included?

• Timeliness – Is the information current and pertaining to a specific,

identified time period? Does the payroll data pertain to the current

pay period? For example, is old, outdated data used, which could

change the amount paid to the employee?

• Uniqueness – Does each record have its own individual identifier

(often referred to as a unique identifier)? Does the payroll record

apply to a specific individual? For example, does a specific payroll

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record pertain to a specific employee?

• Validity – Is the information appropriate for defined parameters? Is

the data in the payroll record based on the acceptable ranges? For

example, is the hourly rate within the accepted range and is the

number of hours worked reasonable?

• Consistency – Does the data correctly align with other data in the

system? For example, if the employee is a salaried employee does the

payroll record reflect that vs. hourly employee data?

It is important that the data in information systems is of high quality. As

systems are developed, the testing should include ensuring that the

quality of data is maintained throughout the system, from its source to

the final output. Therefore, data needs to have these characteristics when

it is entered into the system. The data entry process should include

validation that it meets these quality attributes, and then it needs to be

protected as it resides in and flows through the system. If any of these

characteristics are missing, the system must be analyzed to discover

where the problem lies. The correction may be as simple as fixing an

individual record; or, if it is not clear where the problem lies, the system

may be considered unreliable overall and may need to be taken offline

until corrections are applied. When migrating data to a new system, it is

also important to maintain accuracy and integrity. Inconsistency or

redundancy in data will reduce the acceptance of a new system by users.

Part of a system implementation plan should include specifics about how

data will be transferred, entered, and verified to ensure a high degree of

accuracy (often referenced as a data migration plan).

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