What is a data cloud?

A data cloud is not something you buy off of the shelf. Instead, data clouds are composed of several components and capabilities to provide flexible, scalable data solutions and data integration. You can build data clouds to meet specific requirements and needs to help you achieve your business objectives. 

Typically, most data clouds include the following: 

• Discoverable data: An organization’s data needs to be easy to find and access so various groups of users can interpret and act on it. A data cloud unifies structured, unstructured, or semi-structured data to reduce complexity and simplify discovering data. Therefore, data clouds should be capable of collecting, ingesting, and processing data from multiple on-premises or cloud-based source systems and serving it to one place.
• Agile data architecture for data: Data clouds rely on a data warehouse, a data lake, or even a data lake in some cases to store all the data collected from source systems. The data architecture you choose will largely depend on your unique requirements, but you should be able to leverage other cloud-based data services and integrations, such as cloud database engines, data pipelines, and APIs.
• Built-in AI and machine learning: Intelligent capabilities, such as self-service analytics and AI and machine learning, help organizations save time and effort and support innovation. Data clouds provide automation and advanced tool kits that help you embed AI/ML and data science into business processes and context. 
• Open data platform: The data platform orchestrates the ingestion and scaling of data sources and the data architecture itself. This component creates a unified source of truth that can be reused for many different purposes across the organization. Open data platforms allow organizations to manage data and applications across multiple multicloud and hybrid cloud environments.
• Trusted security foundation: Data needs to be trusted—up-to-date, accurate, and always protected—to streamline data collection and maximize data usage. Data clouds should be secure by default and offer advanced compliance, redundancy, recovery, and reliability capabilities, regardless of the data source. 

• Real-time data processing and insights to drive product and service innovation and enhance employee and customer experiences
• Data protection and governance throughout the entire data life cycle management process
• Self-service analytics reporting, dashboards, and visualizations
• AI-driven analytics and automation, including data and ML models, to streamline processes, increase efficiency, and deliver better productivity
• Automating data quality to improve data consistency without data movement or duplication

Overall, data cloud uses are far ranging and can yield impressive results across industries. Retail brands have been able to gain better visibility into inventory and help employees locate goods within physical store locations. Healthcare organizations are driving better patient outcomes using AI to analyze samples faster and transform unstructured clinical notes into structured formats. Logistics companies have reduced fuel consumption through more efficient routing while financial services and banks have found they can increase processing speeds.