What is data quality?
Well, the initial answer is straightforward – data quality refers to the quality of data. However, digging deeper, it’s evident data quality has broad-reaching implications for an organisation. If information is inaccurate, business operations and efficiency are affected.
Increasing the quality of our data and information requires us to first understand our data (which can be aided through data modelling), and secondly, ensure data is managed in a way which supports objectives and goals (the Information Lifecycle can help you understand the phases of management).
What is ‘good’ quality data?
There are six dimensions of data quality. Considering these dimensions and the following questions will help improve data quality through creation and retention:
1. Accuracy: How accurately does a piece of information reflect reality?
2. Completeness: Is it comprehensive and are all required fields completed?
3. Consistency: Does information stored in one place match relevant data stored elsewhere?
4. Timeliness: Is it available when you need it? And is it consistently updated for current use?
5. Validity: Is it in the right format and follow business rules?
6. Uniqueness: Is there a single ‘source of truth’, or have you accidentally recorded the same data multiple times?
Why is data quality important?
Data is valuable, but its value is heavily determined by its quality. Quality data provides confidence in inferences, while poor data hampers opportunities to utilise it, impacting the ability to draw conclusions with a high degree of accuracy or confidence for reporting, research or admin purposes. This in turn can lead to unreliable conclusions and could wrongly or negatively influence and affect business decisions. Therefore, the improvement of data quality increases the reliability of data. The flow on effect impacts the rest of the Information Lifecycle and its management, such as the efficiency of data sharing, improving data accessibility, ensures appropriate handling, retention and disposal and allows for the accurate modelling of the data.
How do I improve data quality?
It’s important the six dimensions of data quality (listed above) are considered when creating and maintaining data. In addition, the different types of data roles defined at UQ all have specific data quality responsibilities. For example:
• Information Creators are responsible for accurately capturing information and data (e.g. is it in the right format? Is the required metadata entered correctly?).
• Information Consumers are responsible for checking the quality of data is appropriate for the purpose (e.g. is it complete or accurate?).
• Information Stewards are responsible for implementing strategies for quality improvement and resolving quality issues.
• Information Domain Custodians are responsible for defining the domain-specific procedures and rules to ensure proper quality.
Find out more about the different data roles and responsibilities.
Learn more!
You can learn more about data quality and how it can be improved by:
• Visiting our website.
• Attending our training.
• Learn about data modelling. Managing data is complex. It requires an understanding of its purpose and uses, as well as where and how it is collected and stored. This is where data modelling can help. The team is constantly engaging with different areas to further develop logical data models. If you can’t see the data you’re after, or you’d like further logical data modelling undertaken for your area, please contact the team and they’ll be able to assist.
• Have a curly data-governance question? Want some subject matter expertise advice? The team are always happy to help.
