What metrics and standards are needed to manage unstructured data quality?

1 Data quality dimensions

When measuring and improving unstructured data quality, one of the first steps is to define the relevant data quality dimensions for your business goals and use cases. These aspects or characteristics of data determine its fitness for purpose. Accuracy, completeness, consistency, relevance, and timeliness are some of the common data quality dimensions for unstructured data. Accuracy measures how correctly the data represents the reality or source. Completeness assesses if the data has all the required or expected attributes and values. Consistency looks at how coherent and compatible the data is across different sources, formats, and systems. Relevance determines if the data is suitable and useful for the intended analysis and decision making. Lastly, timeliness evaluates if the data is available and updated within the required or expected time frame.

3 Data quality standards

When managing unstructured data quality, it is essential to establish and enforce data quality standards that can guide and regulate data quality processes and practices. Data quality standards are the rules or policies that define the data quality requirements, expectations, and responsibilities for data producers, consumers, and stewards. Examples of these standards include accuracy, completeness, consistency, relevance, and timeliness. Accuracy refers to the acceptable margin of error or deviation from the source or reference data. Completeness involves mandatory or optional attributes and values that the data must have. Consistency is about the format, schema, or standard that the data must follow. Relevance describes the criteria or specifications that the data must meet. Finally, timeliness is about the time frame or frequency that the data must be available or updated.

4 Data quality tools and techniques

In order to implement and maintain the data quality metrics and standards, you need to use data quality tools and techniques to collect, analyze, validate, and improve the quality of unstructured data. These methods or applications can perform a variety of data quality tasks and functions. For example, data quality assessment is the process of measuring and evaluating the current level of data quality using metrics and standards. Additionally, data quality cleansing is the process of correcting and resolving errors using rules and algorithms. Furthermore, data quality enrichment involves adding or augmenting the data with additional information. Finally, data quality monitoring tracks and reports the data quality level using dashboards and alerts. All these techniques help to ensure the compliance and improvement of data quality.

5 Data quality best practices

To ensure the success and sustainability of your unstructured data quality management, certain data quality best practices should be followed. These principles or guidelines can help foster a data quality culture and mindset. Examples of data quality best practices for unstructured data include defining and documenting data quality goals and objectives that are aligned with business goals and use cases, involving and communicating with data stakeholders to understand their needs and expectations, establishing and assigning clear data roles and responsibilities, implementing and automating data quality processes and workflows that integrate with the data lifecycle and pipeline, as well as reviewing and updating data quality metrics and standards to fit the changing environment.

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?