DIVISION OF INFORMATION TECHNOLOGY
ENTERPRISE ARCHITECTURE
Information and Data Quality Overview
Purpose: Summary of aspect to consider in the measurement of information and data quality.
Audience: Business Stakeholders, System Custodians, Data Custodians, Business Analyst, Principal Designers,
Architects, Analyst Programmers.
Information and Data Quality
Information quality is the degree to which information and data can be a trusted source of any and/or all required
uses. It is having the right information, at the right time and place, for the right people to use to run the business,
serve customers and achieve organisational goals.
Relates to the fitness of purpose of information and data assets.
Measuring Quality
Information and data assets have many characteristics that influence quality. Data quality dimensions are identified
aspects or features that can be used to define, measure and manage the quality of data and information.
There are a large range of data quality dimensions that can provide a measure of quality therefore it is important to:
 match dimensions against business needs and prioritise
 understand what will or will not be determined from assessing a dimension
 suitable method, mechanisms and resources to effectively measure selected dimensions
Quality Dimensions
A recommended base set of data quality dimensions includes:
Specifications: a measure of the existence, completeness, quality and documentation about the information or data
asset business purpose, data standards, data model/s, business rules, metadata and reference data.
Accurate: A measure of the correctness of the content of the information or data entity is correct and validated for
zero errors.
Current: instances of information or data entity and associated attributes hold data that is deemed current in
accordance with the relevant authoritative business processes, policy and/or regulatory requirements.
Consistent: format and presentation of data is consistent across whole data set relating to the information or data
entity.
Author: Colleen Middleton
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DIVISION OF INFORMATION TECHNOLOGY
ENTERPRISE ARCHITECTURE
Complete: for each record relating to an instance of information or data asset, all required attribute data has been
captured.
Explicitly covers three aspects of information or data entity.
1. Defined to include all attributes desired (width).
2. Attributes must be populated to extent required (density).
3. Must contain the required amount of instances or records (depth).
Available: information or data is accessible to the approved systems and users.
Data Coverage: a measure of the availability and comprehensiveness of data compared to the total population of
interest (as required for business need). For example, population of interest may be a specific area such as Faculties
or whole of organisation view.
Author: Colleen Middleton
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