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.
1. Everyone is involved
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.
2. Data quality is a commitment, not a task
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.
3. You know what works for your organisation
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.
When you know what makes sense in your context, you can build robust data quality practices that will support a good data quality culture.
4. You know why quality matters
Organisations with a good data quality culture have a clear understanding of the link between their outcomes and the quality of their data. This enables leaders to commit to improving the right data quality practices and to upskilling the right people.
When you understand why good data quality matters to your outcomes, you can measure the risk of bad data quality to your important outcomes. A culture where data quality is done right first time, as early in the data lifecycle as possible, can reduce the impact of bad quality data on your outcomes.
Documenting your data quality practices and how they link to your outcomes can help everyone to understand why they matter. This can also make it clear and simple to update practices and responsibilities when your priorities change.
5. You are proactive not reactive
Proactively managing your data quality is an important step towards a good data quality culture. Long-term solutions to improving data quality involve identifying and fixing poor quality before it impacts on your important outcomes. Reactive fixes to problems that threaten outcomes can become embedded in an organisation’s work. Applying superficial fixes after poor data quality creates a problem is inefficient and leaves your outcomes open to more risks in the future.
When low quality data has a negative impact on your outcomes, you can turn data problems into growth opportunities. Build a culture where mistakes can be talked about in the open, and your people have space to share stories of their data quality problems and solutions. Take time to reflect on them and identify opportunities to proactively mitigate these problems in the future. By openly learning from past mistakes, you can reach a culture where good data quality practices are built into your working from the start, not bolted on afterwards
Organisations with proactive practices for good data quality can mitigate risks to their outcomes more effectively. In the long term, using proactive data quality practices to mitigate these risks can reduce your costs. Effective root cause analysis finds the cause of data quality problems so that you can focus your resources most effectively. A good data quality culture moves away from inefficient, superficial fixes by removing the source of the problem.
Committing to a good data quality culture is a continual process. Put data quality at the heart of your work so that problems are identified and removed proactively. Understand your unique challenges and involve the right people, so you can prevent bad quality data before it damages your work.
The Government Data Quality Hub (DQHub) is developing tools, guidance, and training to help you with your data quality initiatives. You can find the Government Data Quality Framework, tools and case studies on the DQHub site.