What components should you include in your data quality assessment framework?

1 Data quality dimensions

The first component of your data quality assessment framework is the definition of the data quality dimensions that are relevant for your data warehouse. Data quality dimensions are the characteristics or attributes that describe the quality of the data, such as accuracy, completeness, consistency, timeliness, validity, and so on. Different data quality dimensions may have different importance and relevance depending on the type, source, and usage of the data. For example, timeliness may be more critical for operational data than for historical data, while accuracy may be more important for financial data than for demographic data. You should identify and prioritize the data quality dimensions that matter most for your data warehouse and define clear and measurable criteria for each dimension.

2 Data quality metrics

The second component of your data quality assessment framework is the selection of the data quality metrics that will allow you to measure and quantify the data quality dimensions. Data quality metrics are the indicators or variables that capture the level or degree of data quality for a given dimension, such as error rate, completeness ratio, consistency index, currency lag, validity percentage, and so on. Data quality metrics should be aligned with the data quality dimensions and the data quality criteria. They should also be feasible, reliable, and comparable. You should choose the data quality metrics that best reflect the data quality dimensions and that can be calculated and monitored using the available data and tools.

3 Data quality rules

The third component of your data quality assessment framework is the specification of the data quality rules that will enable you to validate and verify the data quality metrics. Data quality rules are the logical expressions or statements that define the expected or desired values, ranges, formats, patterns, or relationships of the data elements, such as data type, length, format, domain, uniqueness, referential integrity, business logic, and so on. Data quality rules should be consistent with the data quality dimensions and the data quality metrics. They should also be precise, complete, and actionable. You should define the data quality rules that can effectively detect and prevent data quality issues and that can be implemented and executed using the appropriate data quality tools.

4 Data quality sources

The fourth component of your data quality assessment framework is the identification of the data quality sources that will provide you with the input and output data for the data quality metrics and rules. Data quality sources are the data sets or entities that contain the data elements that are subject to the data quality assessment, such as tables, columns, records, fields, values, and so on. Data quality sources may vary depending on the scope, level, and granularity of the data quality assessment. For example, you may assess the data quality at the source system level, the data warehouse level, or the data mart level. You should determine the data quality sources that are relevant and accessible for your data quality assessment and that can be integrated and compared using the suitable data quality methods.

5 Data quality methods

The fifth component of your data quality assessment framework is the selection of the data quality methods that will allow you to apply and evaluate the data quality metrics and rules on the data quality sources. Data quality methods are the techniques or procedures that perform the data quality assessment, such as data profiling, data cleansing, data matching, data auditing, data scoring, and so on. Data quality methods may differ depending on the type, complexity, and frequency of the data quality assessment. For example, you may use different data quality methods for initial data quality assessment, ongoing data quality monitoring, or ad hoc data quality analysis. You should choose the data quality methods that are suitable and efficient for your data quality assessment and that can be supported and automated by the relevant data quality tools.

6 Data quality reports

The sixth component of your data quality assessment framework is the creation of the data quality reports that will enable you to communicate and visualize the data quality metrics and rules results. Data quality reports are the documents or dashboards that present the data quality findings, such as data quality scores, data quality issues, data quality trends, data quality recommendations, and so on. Data quality reports should be informative, concise, and actionable. They should also be tailored to the needs and expectations of the data quality stakeholders, such as data owners, data users, data managers, or data analysts. You should design and produce the data quality reports that can effectively convey the data quality status and that can facilitate the data quality improvement actions.

7 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?