Data Quality 102: How to fix Bad Data over Time?

In the previous module (https://aimind-ft.com/article/data-quality-101/), we detailed together what was poor data quality and explained what were the six dimensions of it. In addition, we explained the concept of data debt for organisations.

Furthermore, we expounded how the acknowledgement of an existing data debt within organisational processes was itself a positive sign. It means that decision-makers became conscious of the need to deploy human and financial resources to eliminate data debt.

In this module, you will learn how to address data quality’s issues and definitely prevent, in the future, the accumulation of bad data.

So, how to fix bad data over time?

One of the first solutions you can use is to implement validation rules when uploading new data. For instance, you can make certain fields mandatory, so they cannot be left blank. You can also define the expected data type for each field, ensuring that, for example, a postal code contains only numbers, and not a mix of letters and digits.

At the same time, you can audit data quality, so you may find and analyse what exactly causes the accumulation of data debt. Most companies have today, a mature vision of the importance of managing data quality. They create indicators and scores to consistently evaluate, on a common scale, the quality of data that will be implemented into IT systems.

Promoting a data culture is key for an optimised data quality

In your organisation, promoting a data culture is a valuable strategic move to encourage your employees to adhere to quality rules and raise their awareness. This involves raising awareness about the importance of accurate, consistent and reliable data, as well as providing training and clear guidelines on best practices.

By fostering a sense of responsibility and accountability around data handling, employees are more likely to follow established standards and contribute to maintaining high-quality data across the organisation.

Finally, formalise accountability by appointing Data Owners. These are individuals responsible for a specific data domain — ensuring accuracy, maintaining consistency across systems, and enforcing governance standards within their scope.

Key takeaways

1. You don't need to be technical to act Appointing Data Owners, defining validation rules, and running regular audits are concrete.

2. Data quality is a culture, not a project Tools and processes help, but lasting improvement comes from people who understand why quality matters and take responsibility for it.