How can you identify the root causes of poor data quality?

1 Data quality dimensions

Before you can identify the root causes of poor data quality, you need to define what data quality means for your specific context and goals. Data quality can be measured by different dimensions, such as completeness, validity, consistency, accuracy, timeliness, and uniqueness. Each dimension has its own criteria and standards, depending on the type, source, and purpose of your data. For example, completeness refers to whether your data has all the required fields and values, validity refers to whether your data conforms to the expected format and rules, and accuracy refers to whether your data reflects the true state of reality.

2 Data quality assessment

To assess your data quality, you need to perform some data quality checks and audits, using various methods and tools. Data profiling is a common technique that involves analyzing the structure, content, and metadata of your data - such as the number of records, columns, values, data types, formats, ranges, distributions, and patterns - in order to identify any missing, invalid, or inconsistent data. Data cleansing is another method that involves correcting, removing, or replacing erroneous or incomplete data. This can help improve the validity, accuracy, and consistency of your data. Finally, data validation is used to verify the quality of your data against predefined rules or standards. It can help ensure the completeness, accuracy, and timeliness of your data while detecting any violations or discrepancies.

3 Data quality root causes

To identify the root causes of poor data quality, you need to trace the origin and flow of your data from source to destination, as well as understand the context and environment in which it is generated, collected, stored, processed, and used. Common root causes include data source issues, such as incorrect or outdated data or insufficient security; data collection issues, like human errors or incompatible formats; data transformation issues, like integration or filtering errors; and data usage issues related to misinterpretation or misalignment with objectives.

4 Data quality improvement

To improve your data quality, you need to address the root causes of poor data quality and implement some data quality best practices and strategies. This includes establishing and enforcing data quality policies and standards, measuring and tracking data quality indicators, and implementing data quality improvement projects. Additionally, fostering a data quality culture within your organization is important for valuing, recognizing, and rewarding data quality. This can be done by promoting data quality awareness, education, and collaboration.

5 Data quality visualization

Using appropriate data visualization techniques and tools, such as charts, graphs, dashboards, or reports, can help you visualize your data quality. Doing so can enable you to explore and understand data quality dimensions, indicators, and metrics. Additionally, it can assist in communicating and sharing data quality assessment, improvement, and management results and insights. Furthermore, it can motivate and engage data quality stakeholders, as well as demonstrate the value of data quality on business performance or outcomes. Data quality visualization also helps improve data visualization quality by ensuring that the data is reliable, relevant, and consistent for analysis or presentation purposes.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?