What are the key steps to accurately assessing data quality?

2 Assess data quality levels

The next step is to assess the current levels of data quality for your data sources, datasets, and attributes, based on the defined dimensions and criteria. There are different methods and tools to assess data quality levels, such as data profiling, data auditing, data cleansing, and data validation. Data profiling is the process of analyzing the structure, content, and metadata of data sources, to identify the characteristics, patterns, and anomalies of data. Data auditing is the process of verifying and evaluating the compliance of data with the defined standards and rules, such as data formats, values, ranges, and constraints. Data cleansing is the process of correcting, removing, or enriching the erroneous, incomplete, or inconsistent data, to improve its quality and accuracy. Data validation is the process of checking and confirming that the data meets the expectations and requirements of the data consumers and stakeholders, such as data formats, values, ranges, and constraints.

4 Implement data quality improvement actions

The fourth step is to implement the appropriate actions and solutions to improve the data quality levels and resolve the data quality issues that you prioritized in the previous step. Data quality improvement actions can include various activities, such as data cleansing, data enrichment, data standardization, data harmonization, data deduplication, data verification, or data enhancement. You should select and apply the most suitable and effective methods and tools for each data quality improvement action, and monitor and document the results and outcomes of each action. You should also communicate and collaborate with the data consumers and stakeholders, to ensure that the data quality improvement actions meet their expectations and requirements, and to solicit their feedback and suggestions.

5 Establish data quality monitoring and control mechanisms

The fifth step is to establish the mechanisms and processes to monitor and control the data quality levels and issues on an ongoing basis, and to prevent or minimize the recurrence of data quality problems. Data quality monitoring and control mechanisms can include various components, such as data quality metrics, data quality dashboards, data quality alerts, data quality reports, data quality audits, or data quality reviews. You should define and implement the appropriate metrics and indicators to measure and track the data quality levels and issues, and to compare them with the defined standards and goals. You should also design and use the suitable dashboards, alerts, reports, audits, or reviews to visualize and communicate the data quality status and performance, and to identify and address any data quality deviations or anomalies.

6 Review and update data quality assessment process

The final step is to review and update the data quality assessment process periodically, to ensure that it remains relevant, effective, and efficient for your data sources, datasets, and attributes, and for your data consumers and stakeholders. Data quality assessment is not a one-time activity, but a continuous and iterative process, that should adapt to the changes and developments in the data environment, such as new data sources, new data types, new data uses, new data requirements, or new data challenges. You should evaluate and document the strengths, weaknesses, opportunities, and threats of your data quality assessment process, and make the necessary adjustments and improvements to enhance its quality and value.