Five signs of a good data quality culture

A good data quality culture in your organisation brings lasting benefits. Recognising good practice and building this into the way your organisation works can give you the confidence to make decisions knowing that you are backed by high quality data.

We all want to know that our data is fit for purpose. In this article, we’ll look at five ways to be sure that the quality of your data is right for your needs and your users’ needs.

The risks of bad data quality can occur in many places. From design, data collection, and data entry to use, publication, and archiving of data. Everyone in your organisation should understand their responsibility to data quality and have the skills to play their part. This means you can get things right from the start.

You should recognise and share where good data quality practices occur in your organisation. Define roles and responsibilities for data quality in your organisation and ensure that your people can develop the skills they need. These can be important steps in building a good data quality culture.

Data quality cannot exist in a vacuum; you cannot consider it in isolation or only react to poor data quality after it has caused a problem. Business impacts of good or bad data quality intersect with so many areas of an organisation that it must be integral to your working.

Consider your business outcomes and the risks that poor data quality can pose to these. Mitigating risks early and involving the right people throughout your organisation will allow you build a culture where data quality is systemic in your working.

IT solutions can help to improve your data quality, but they do not create a good data quality culture. Take time to develop your people’s skills and implement simple, repeatable practices that help ensure your data is fit for your purposes. Review the quality of your important data regularly and update your practices as needed.

No two organisations are the same. Your unique priorities, needs and challenges mean that there is no single, correct way to manage data quality. Recognising whether your data is fit for purpose means that you understand what is and isn’t critical in your context. This will include knowing your most important datasets, how they are used, and understanding what your minimum requirements for data quality in each dataset are.

The way that your organisation monitors data quality and implements improvements will depend on your own structure. Organisations with a strong data quality culture identify the departments and roles where responsibility and accountability for data quality sit. Make these responsibilities and accountabilities visible across your organisation, so that your people can see how these connect to their own responsibilities.