What are the most effective emerging technologies in data quality frameworks and standards?

1 Data Quality Dimensions

Data quality frameworks and standards typically use a set of dimensions or criteria to assess and compare the quality of data. Some of the common dimensions are accuracy, completeness, consistency, timeliness, validity, and relevance. However, these dimensions may vary depending on the domain, scope, and objectives of the data quality assessment. For example, some frameworks may include additional dimensions such as accessibility, availability, security, or usability. Therefore, data architects need to select and define the appropriate dimensions for their data quality frameworks and standards, and align them with the business and technical requirements of the data users and producers.

2 Data Quality Metrics

Data quality metrics are quantitative or qualitative indicators that measure the performance of data quality dimensions. They can be used to monitor, evaluate, and improve the quality of data over time, and to communicate the results to the data users and producers. Data quality metrics can be derived from various sources, such as data profiling, data validation, data cleansing, data audits, or user feedback. However, data quality metrics can also be challenging to design, implement, and maintain, as they require clear definitions, standards, and methods, as well as adequate data and tools. Therefore, data architects need to use effective and emerging technologies that can automate, integrate, and visualize data quality metrics, and provide actionable insights and recommendations.

3 Data Quality Tools

Data quality tools are software applications or platforms that can assist data architects in implementing and managing data quality frameworks and standards. They can provide various functions, such as data discovery, data profiling, data validation, data cleansing, data enrichment, data integration, data governance, or data analytics. Some of the most effective and emerging technologies that can enhance data quality tools are artificial intelligence (AI), cloud computing, blockchain, and big data. These technologies can enable data quality tools to handle large and complex data sets, to perform advanced and intelligent data quality operations, to ensure data security and traceability, and to deliver data quality insights and solutions in real time and at scale.

4 Data Quality Governance

Data quality governance is the process of establishing and enforcing policies, roles, responsibilities, and procedures for data quality management across the organization. It can help to ensure that data quality frameworks and standards are aligned with the strategic goals and values of the organization, and that they are followed and reviewed regularly. Data quality governance can also facilitate the collaboration and communication among the data users and producers, and foster a culture of data quality awareness and improvement. Some of the most effective and emerging technologies that can support data quality governance are data catalogs, data lineage, data stewardship, and data quality dashboards. These technologies can help data architects to document, track, and share data quality information, to assign and monitor data quality tasks and roles, and to report and visualize data quality status and trends.

5 Data Quality Benefits

Data quality frameworks and standards can provide a range of benefits for data architects and stakeholders. These include improving the reliability, usability, and value of data for decision making, analysis, and innovation. Additionally, it can reduce risks, costs, and errors associated with poor data quality. Moreover, it can enhance the trust, confidence, and satisfaction of the data users and producers. Furthermore, it can enable compliance with regulatory and ethical standards, as well as support continuous improvement and learning of the data quality processes. Ultimately, these frameworks and standards can strengthen the reputation and credibility of the organization.

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?