Data quality issues framework

19 Nov 2025 01:32 PM

Who this framework is for?

This framework is for anyone with an interest in, or responsibility for, the quality of data within a business area or for a specific data asset. It defines a data quality issue, and explains how to identify issues when they occur and assign a priority to the issue.

If you’re a data practitioner in a public sector organisation, you should use this framework to help create a data quality action plan (DQAP). It will be especially useful for steps 3 to 5 of a DQAP:

• assess current data quality
• prioritise improvements and set goals
• identify root cause and take action

The framework includes a working example based on the preceding sections, and a glossary of some of the terms used in this guidance, such as data item.

What data quality rules and issues are

To maintain high-quality data in your organisation, for each of your data assets you need to:

• understand what the purpose of the data is – including its importance and impact
• create data quality rules
• set targets and performance bands against each data quality rule

Data quality rules are checks that help make sure your data meets a required standard. They define what ‘good’ data looks like and help you spot errors or inconsistencies.

For example, you might set:

• a completeness rule that states a field cannot be left blank (ie no null values)
• an accuracy rule that states a value in a field must be within a valid range – such as dates not being set in the future

You can assign different levels of priority or criticality to each rule, and it may or may not be acceptable for a data asset to contain errors.

Once you’ve defined the rules, you can then measure the performance of the data asset. If its performance is below the target, this is a data quality issue.

Determine the importance of purpose

Each data quality rule should document a purpose – like in Table 1 which refers to a data set that was used to book resources for training courses.

Table 1: resources for training courses

Purpose	Part of data	Requirement	Relevant aspect	Measurement	Metric
To allocate the right resources for training	Trainers’ details	Trainers’ names should be complete	Completeness	Trainer name should not be null	Percentage of records with names filled in

Based on its importance to your organisation or the wider public sector, allocate the purpose one of the following importance categories:

1. Low
2. Medium
3. High
4. Critical

Low

If the purpose fails or is disrupted, it might:

• incur minor unnecessary losses of costs, time or other resources 
• cause minor inconvenience to a person in the exercise of their rights and freedoms

Medium

If the purpose fails or is disrupted, there is a high risk of:

• incurring excessive unnecessary losses of costs, time or other resources 
• causing unnecessary inconvenience to a person in the exercise of their rights and freedoms

High

If the purpose fails or is disrupted, there is a very strong risk that:

• you will not achieve your goals without significant unnecessary losses of costs, time or other resources
• your organisation will be unable to meet regulatory requirements
• a person will be able to exercise their rights and freedoms, and do so without unnecessary significant inconvenience

Critical

If the purpose fails or is disrupted, there is a major risk that:

• there will be a serious negative impact on you achieving your goals
• your organisation or the government will break the law
• there will be loss of life or personal injury
• there will be loss or destruction of property (including financial loss)

Determine the impact of non-conforming data

Consider how severe the impact of a data item in the data asset not meeting the rule condition would be on the ability to fulfil the purpose.

Allocate the data an impact category and associated score, referring to Table 2.

Click here to read on and access the full framework on the GDS website