How do you check data quality?

1 Data quality dimensions

Data quality can be assessed along several dimensions, such as accuracy, completeness, consistency, timeliness, and relevance. Accuracy refers to how well the data reflects the reality or the source of information. Completeness means that the data has no missing values or gaps. Consistency means that the data follows the same standards and formats across different sources and records. Timeliness means that the data is up to date and reflects the current situation. Relevance means that the data is suitable and useful for the intended purpose and audience.

2 Data quality metrics

To measure data quality, you need to define some data quality metrics, which are quantitative indicators of how well the data meets the quality criteria. For example, you can use the percentage of missing values, the number of duplicates, the frequency of errors, or the deviation from the expected values as data quality metrics. You can also use data profiling tools, such as pandas-profiling or pydqc, to generate summary statistics and visualizations of your data quality.

3 Data quality checks

To check data quality, you need to perform some data quality checks, which are tests or validations that compare the data against the quality standards and metrics. For example, you can use data quality rules, which are logical expressions that define the expected or acceptable values or formats of the data. You can also use data quality audits, which are systematic reviews of the data sources, processes, and outputs to identify and document the quality issues. You can use Python libraries, such as pandas or pydantic, to implement data quality checks in your code.

5 Data quality monitoring

To maintain data quality, you need to implement some data quality monitoring practices, which are processes or systems that track and report the quality status and trends of the data over time. For example, you can use data quality dashboards, which are visual displays of the key data quality metrics and indicators. You can also use data quality alerts, which are notifications or warnings that inform you of any significant changes or anomalies in the data quality. You can use Python libraries, such as dash or streamlit, to create data quality dashboards and alerts.

6 Data quality culture

To ensure data quality, you need to foster a data quality culture, which is a mindset or attitude that values and promotes the importance and benefits of data quality. For example, you can use data quality documentation, which is a collection of information and guidelines that describe the data quality standards, metrics, checks, and improvement techniques. You can also use data quality education, which is a set of training and learning opportunities that teach and reinforce the data quality skills and knowledge. You can use Python libraries, such as sphinx or jupyter-book, to produce data quality documentation and education materials.