The ultimate guide to data quality management

Data quality management can be complex, especially if you don’t know the extent of the data you hold or fully understand all of its applications. 

Proper data governance is essential to your organisation’s ongoing success: as the quality, relevance and accuracy of data deteriorate over time, the effectiveness of your decision-making will also decrease unless you have an appropriate management strategy. 

When your data is inaccurate: 

  • Any data-driven decisions you make will inevitably be based on false assumptions. 
  • Your marketing campaigns will have poor response rates owing to out-of-date contact details, so you will miss sales opportunities. 
  • You will not be able to demonstrate your compliance with the DPA (Data Protection Act) 2018 and GDPR (General Data Protection Regulation)’s personal data processing requirements. 

In this blog, we help you understand how to effectively manage your data and explain the risk of inaccurate or low-quality information assets.

What is data quality management?

Data quality management refers to the ways an organisation evaluates the usefulness of the information it collects.

The process is often associated with data analysis and decision making, and as such, it’s essential that the information being used is accurate.

That doesn’t just mean that records are up to date or that they don’t contain errors. It could also refer to the elimination of duplicate files, for example.

Why do you need data quality management?

Data quality is often considered in terms of its ‘health’ or ‘fitness’. This is important to understand, because the process isn’t simply about correcting inaccurate data for the sake of it.

Rather, you are trying to ensure good habits are maintained and keep business functions operating as effectively as possible. One of the main functions this relates to is CRM (customer relationship management).

How do you measure data quality?

You can measure data quality using six criteria:

  1. Accuracy: Is the information you use correct and precise? If there are any mistakes in the data, it could mean processes aren’t completed correctly.
  2. Timeliness: Is the information up to date? You should review information at appropriate intervals to ensure that it is correct and give customers the opportunity to amend data if it’s no longer correct.
  3. Completeness: Do you have all the necessary information you need for each of your records? A few incomplete records are inevitable and may occur when there is no way to access the information or data subjects don’t provide it. However, if you have consistent gaps in your records, it will cause problems.
  4. Uniformity: Is information presented in a consistent manner across your system? Common issues include information being entered in the incorrect field or ratings systems being presented on an inconsistent scale.
  5. Uniqueness: Do you have any duplicate records? If so, this could skew data analysis or result in customers being contacted multiple times.
  6. Security: Do you have adequate controls to prevent unauthorised individuals compromising the information? This includes both cyber criminals breaking into your systems as well as malicious insiders.

The 5 pillars of data quality management

Now that we’ve taken a look at what data quality management is and how you measure it, let’s turn our attention to the ways you can ensure you can implement it.

1. Data profiling

Data profiling is an essential component of any DQM lifecycle. It requires organisations to conduct an in-depth review of the information they have processed and assess how they can improve its quality.

This could include comparing the information to its metadata, running statistical models and completing reports.

2. Defining data quality

Before you can improve the quality of your data, you need to assess what factors are most important for your business requirements. You can do this by creating data quality rules.

3. Data reporting

Data reporting involves removing and recording all low-quality data. If you record where the compromised data has come from, you should see patterns that identify why the information is low quality.

4. Data repair

Data repair requires you to determine the best way to remediate low-quality data and the most efficient way you can implement that change.

5. People

We have so far focused on technological solutions, but it’s worth remembering that technology is only as effective as the people who use it.

As such, you may benefit from appointing someone (or creating a team) who are responsible for data quality management. This could include a DQM programme manager or a business/data analyst.

If you don’t have the means to appoint dedicated employees, you should ensure that anyone responsible for creating data records is given appropriate training on the important of data quality management.

Create high-quality data with DQM GRC

If you’re looking for help developing your data management project, our Data Quality Assurance may be the perfect solution.

Whether your organisation sells data commercially or you have a large database of customers, we can help you be confident in the quality of your data.

DQM GRC is long established as a leading provider of data privacy, security and assurance services to clients of all types and sizes, from family-run enterprises to multinational corporations. 

Our data professionals have many years’ experience in data analysis and can verify, measure and report against your data benchmarks to ensure quality and accuracy.


  • Luke Irwin

    Luke Irwin is a former writer for DQM GRC. He has a master's degree in Critical Theory and Cultural Studies, specialising in aesthetics and technology.

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