Data clouds offer an open, cloud-based data infrastructure that allows users to unify data securely across an enterprise. Data clouds provide on-demand compute, storage, delivery and analytics capabilities. They also aid secure accessibility, faster iteration and time to value, rapid provisioning and flexible integration.
Data clouds are made up of several components, and organizations can build data clouds to meet specific business needs.
Today, most data clouds include:
- Discoverable data: A data cloud combines structured, unstructured or semi-structured data to simplify and reduce complexity. Data clouds should be capable of collecting, ingesting and processing data from multiple on-premises or cloud-based source systems in one place.
- Agile data architecture: A data cloud relies on a data warehouse or a data lake to store all the collected data from source systems. The enterprise's data architecture should be able to leverage other cloud-based data services and integrations, such as cloud database engines, data pipelines and APIs.
- AI and machine learning: Data clouds provide automation and advanced tool kits that help enterprises embed artificial intelligence (AI)/ machine learning (ML) and data science into business processes and context.
- Trusted security foundation: Regardless of the data source, data clouds should be secure and offer advanced compliance, redundancy, recovery and reliability capabilities.
Data cloud uses for enterprises commonly include real-time data processing and insights, data protection and governance, and self-service analytics reporting, dashboards and visualizations.