DAMA UK DQ Dimensions White Paper R37
THE SIX PRIMARY
DIMENSIONS FOR DATA
QUALITY ASSESSMENT
Defining Data Quality Dimensions
Abstract
This paper has been produced by the DAMA UK Working Group on “Data
Quality Dimensions”. It details the six key ‘dimensions’ recommended to be used when assessing or describing data quality.
Defining Data Quality Dimensions
DEFINING DATA QUALITY DIMENSIONS
BACKGROUND
The term data quality dimension has been widely used for a number of years to describe the measure of the quality of data. However, even amongst data quality professionals the key data quality dimensions are not universally agreed.
This state of affairs has led to much confusion within the data quality community and is even more bewildering for those who are new to the discipline and more importantly to business stakeholders.
Socrates said, “The beginning of wisdom is the definition of terms”. Hence, the goal of this whitepaper is to define the key data quality dimensions and provide context so there can be a common understanding for industry professionals and business stakeholders alike.
Sir Karl R. Popper built on this saying "I do not say that definitions may not have a role to play in connection with certain problems, but I do say it is for most problems quite irrelevant whether a term can be defined (or not). All that is necessary is that we make ourselves understood." This certainly reinforces the idea that dimensions are indicators helping us to measure and communicate the quality of data, as opposed to defining what the data itself means or represents.
In May 2012, DAMA UK asked for volunteers to join a working group to consider the issue and produce some best practice advice. The response was overwhelming and demonstrated the need for such a piece of work.
Other data management professional organisations have also been keen to support this initiative and as such we were very pleased to welcome Julian Schwarzenbach, Chair of the BCS Data Management Specialist Group and Gary Palmer, charter member
DIMENSIONS FOR DATA
QUALITY ASSESSMENT
Defining Data Quality Dimensions
Abstract
This paper has been produced by the DAMA UK Working Group on “Data
Quality Dimensions”. It details the six key ‘dimensions’ recommended to be used when assessing or describing data quality.
Defining Data Quality Dimensions
DEFINING DATA QUALITY DIMENSIONS
BACKGROUND
The term data quality dimension has been widely used for a number of years to describe the measure of the quality of data. However, even amongst data quality professionals the key data quality dimensions are not universally agreed.
This state of affairs has led to much confusion within the data quality community and is even more bewildering for those who are new to the discipline and more importantly to business stakeholders.
Socrates said, “The beginning of wisdom is the definition of terms”. Hence, the goal of this whitepaper is to define the key data quality dimensions and provide context so there can be a common understanding for industry professionals and business stakeholders alike.
Sir Karl R. Popper built on this saying "I do not say that definitions may not have a role to play in connection with certain problems, but I do say it is for most problems quite irrelevant whether a term can be defined (or not). All that is necessary is that we make ourselves understood." This certainly reinforces the idea that dimensions are indicators helping us to measure and communicate the quality of data, as opposed to defining what the data itself means or represents.
In May 2012, DAMA UK asked for volunteers to join a working group to consider the issue and produce some best practice advice. The response was overwhelming and demonstrated the need for such a piece of work.
Other data management professional organisations have also been keen to support this initiative and as such we were very pleased to welcome Julian Schwarzenbach, Chair of the BCS Data Management Specialist Group and Gary Palmer, charter member