What are the latest trends in data integration quality and governance?

2 Data governance automation

Another trend in data integration governance is the automation of data governance tasks and processes. Data governance is the set of policies, standards, roles, and responsibilities that ensure the proper management and use of data in an organization. Data governance automation is the use of artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) to automate and simplify data governance activities, such as data discovery, data cataloging, data lineage, data classification, data stewardship, and data compliance. Data governance automation can help you reduce manual effort, increase data visibility, and enhance data trust and value.

3 Data integration hubs

A data integration hub is a centralized platform that connects and orchestrates data flows between different sources and targets. It is a modern and flexible approach to data integration that can support various data integration patterns, such as batch, real-time, streaming, event-driven, and API-based. A data integration hub can also enable data quality and governance features, such as data validation, data transformation, data profiling, data masking, data auditing, and data lineage. A data integration hub can help you achieve faster, easier, and more scalable data integration, while ensuring data consistency and reliability.

4 Data fabric

A data fabric is a unified and comprehensive data platform that provides data integration, data management, data analytics, and data services across the entire data lifecycle. It is a holistic and agile solution that can handle any type of data, from any source, at any speed, and for any purpose. A data fabric can also incorporate data quality and governance capabilities, such as data quality assessment, data quality improvement, data governance framework, data governance policies, and data governance metrics. A data fabric can help you create a seamless and secure data environment that can support your business goals and needs.

5 Data mesh

A data mesh is a decentralized and distributed architecture that treats data as a product. It is a radical and innovative way to address the challenges of data integration in large and complex organizations. A data mesh shifts the focus from centralized data warehouses and lakes to domain-specific data platforms that are owned and operated by the data producers and consumers. A data mesh also applies data quality and governance principles, such as data quality standards, data quality measurement, data governance roles, data governance rules, and data governance enforcement. A data mesh can help you achieve more autonomous, collaborative, and scalable data integration, while ensuring data quality and governance.