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What is a data cloud?

A data cloud provides an open, cloud-based data infrastructure that enables the availability, integration, portability, availability, and security of enterprise data. It offers a comprehensive and proven approach to the cloud, offering on-demand compute, storage, delivery, and advanced analytics capabilities that enable organizations to harness their data to drive transformation and value creation. 

Data transformation is made all the more difficult by siloed systems that require a lot of effort and resources to maintain and manage. Many companies struggle to ingest data fast enough to achieve data intelligence. Others may find it difficult to unify data for new insights or make data accessible and shareable to those who need it. 

Even with modern data tools, organizations may be unable to integrate them with existing systems easily and get bogged down with scaling and managing legacy IT infrastructure. Teams spend most of their time getting the right data in the right format to the right place at the right time—leaving little or no time for meaningful data analysis. 

Digital innovators are now building data clouds to eliminate data fragmentation and leverage the full power of their data. Data clouds make it easier to unify data, connect to it, and make it available, providing resilient, reliable databases, analytics, and machine learning systems to drive innovation, better experiences, and accelerate time to value. 

How do data clouds work?

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. 

Data cloud uses and examples

Some common data cloud uses at organizations include:

  • 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. 

Benefits of using a data cloud

Faster time to value

Data clouds offer fully managed cloud databases and analytics services which can free up time, shifting focus from maintenance and management to higher-value-adding activities. 

Secure accessibility

Data clouds provide faster, easier access to data and insights without compromising security. They ensure data is trusted, secure, and governed according to both regulations and internal policy. 

Flexible integration

If your data cloud is built on open protocols and uses standard interfaces, it’s easier to integrate data architecture components, whether they are developed internally or by a third-party vendor. Open platforms also ensure portability and extensibility to prevent vendor lock-in.

Faster iteration

Data clouds not only drive higher productivity rates for predictable workloads but also give teams the resources and elasticity to iterate faster on unpredictable and data-hungry ones. 

Rapid provisioning

With a data cloud, data engineers can quickly provision new data management resources as needed for both developers and business users. 

Better business outcomes

The benefits of a data cloud extend far beyond accelerating and streamlining data work. Data clouds have been shown to improve other areas, such as profitability, cost savings, resilience, and risk management. 

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