Practice Director - Digital & Analytics Practice at HCL Technologies
Real User
Top 20
2022-10-13T04:26:18Z
Oct 13, 2022
Data mesh is a concept. You need to develop data products and make them discoverable and interoperable to extend the usage of data for business benefits. This happens at its best when it is developed by domain/business units that are experts in the specific areas, due to the simple fact that they know the business and are supporting the data more than anyone else in the organization. This requires many components to come together - domain knowledge, responsible business/data stewards, technology experts, a paradigm shift in ways of working & change management, governance processes & policies, a technology platform, etc. Data fabric is important to make data mesh practical - that is the technology backbone that makes it possible to provide context to data, make data discoverable, enable self-service of data, manage data through stewardship workflows, and more.
What is Metadata Management? Metadata management is the administration of metadata (data that describes other data) across your organization. The management of metadata involves establishing processes and policies to ensure that information can be best accessed, integrated, shared, maintained, and analyzed across an organization.
Data mesh is a concept. You need to develop data products and make them discoverable and interoperable to extend the usage of data for business benefits. This happens at its best when it is developed by domain/business units that are experts in the specific areas, due to the simple fact that they know the business and are supporting the data more than anyone else in the organization. This requires many components to come together - domain knowledge, responsible business/data stewards, technology experts, a paradigm shift in ways of working & change management, governance processes & policies, a technology platform, etc. Data fabric is important to make data mesh practical - that is the technology backbone that makes it possible to provide context to data, make data discoverable, enable self-service of data, manage data through stewardship workflows, and more.