6 minutes
A data cloud is a data management system that unifies various data sources so they can be used more effectively by organizations.
Most modern enterprises depend on large, complex IT infrastructures that blend cloud service providers (CSPs) with on-premises resources, such as servers and software. Data clouds help unify these various sources, increasing data management efficiencies, improving data integrity and eliminating silos (isolated collections of data that can be difficult for users to access).
When deployed correctly, data clouds help enterprises in industries such as healthcare, financial services, marketing, aerospace and more, speed digital transformation and gain capabilities from new technologies.
A data cloud is made up of three core components: data sources, data architecture and data platforms—known as cloud data platforms. Here’s a look at each of these components and how they function.
Data sources are collections of data in its original form. Some common examples of customer data sources include transactions, email addresses, social media posts and personally identifiable information (PII) (for example, a person’s name, age and physical location). Data clouds must securely collect, integrate, transform, store and manage data from various sources to function.
Data architecture, also known as data warehouse architecture, refers to the design of data repositories and describes how data is managed by an organization from its collection through to transformation, distribution and consumption. Enterprises use a wide range of data architectures and data models depending on their business needs, including data warehouses, data lakes, data pipelines, data mesh and more.
In a data cloud, the data architecture includes specific protocols designed to make the collection and processing of data more efficient in a cloud ecosystem. For example, many modern data clouds use machine learning (ML) to process data more efficiently.
ML helps enable capabilities such as predictive analytics and automated decision-making with cloud architecture, avoiding the cost of building and managing the necessary IT architecture on-premises. ML capabilities are one of the features that make data clouds a highly scalable solution for many enterprises.
Data platforms are technology solutions that enable the collection, storage, analysis and governance of data. In a cloud environment, a data platform is known as a cloud data platform and is designed specifically to help ingest data and move it from on-premises storage into the cloud.
Modern cloud data platforms help organizations govern and analyze data in a cloud or multicloud architecture, optimizing both structured and unstructured datasets.
Data clouds can help organizations in many ways, from identifying new customer insights to automating tasks that previously required human input. Here are some of the most popular benefits of running a data cloud at the enterprise level:
Data clouds enable IT leaders to manage and process data from a single, unified platform, rather than many isolated, interconnected systems. For example, when allocating permissions for data among users in an enterprise, admins on a data cloud can control policies using a single point of control rather than multiple locations, improving data governance and security.
Data clouds allow for data to be controlled and shared beyond physical workspaces, an essential component of a remote workforce. Using a data cloud, users can securely access critical data from anywhere in the world without raising security risks.
They can also seamlessly move data between popular systems such as data lakes and data warehouses for safe and efficient processing, and they can access popular data cloud providers such as Salesforce Data Cloud, Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP) to get the latest data cloud solutions on a highly scalable, software as a service (SaaS) model.
Data clouds rely on modern data-sharing protocols to improve data exchange between cloud storage solutions and optimize the performance of apps that rely on data to function. Using application programming interfaces (APIs), data clouds connect external applications with databases and help apps process data, regardless of its type, format or structure.
Data cloud solutions easily handle the different kinds of business data that apps rely upon, such as transactional and analytical data and even unstructured data such as images and videos. Data cloud metrics help IT managers monitor the effectiveness of their data cloud solution and identify opportunities for further efficiencies and cost savings.
Modern data cloud solutions are equipped with robust security technologies that help protect organizations from costly cyberattacks and data breaches. Last year, one report put the average cost of a data breach at USD 4.88 million, a 10% increase over the previous year and the highest total ever.
Data cloud solutions simplify and streamline data protection through the automation of many security tasks that used to require human input, such as compliance with regulatory and governance rules.
Data clouds improve user access to various kinds of data, streamlining business processes and giving employees across the entire enterprise safe and secure access to the information they need to collaborate effectively, often from a single dashboard.
With a strong, modern data cloud solution, employees can access both structured and unstructured datasets and apply advanced analytics to uncover valuable insights.
While there are many benefits to operating a data cloud, organizations face a few challenges as well. Specifically, organizations seeking to move large volumes of data from an on-premises environment to the cloud face three common obstacles: data ingestion, data integrity and the upskilling of workers who operate the data cloud.
Moving large, diverse datasets to the cloud often means moving data from sources that might be formatted differently and require different environments to be transferred and managed safely, a process known as data ingestion.
In data ingestion, various data files are collected and imported from different sources into a database for cleaning and storage to make them accessible to an organization.
If data from different sources needs to be cleaned or standardized before being transferred to a new storage environment, such as a data lake or warehouse, it can cause delays and even errors in the process.
To maintain data integrity (the accuracy, consistency and completeness of data) enterprises must maintain a high level of precision throughout the data transfer process. One area that is particularly fraught with risk is maintaining data integrity in the face of multiple sets of regulations.
When moving data to the cloud, organizations must comply with regulations regarding data privacy that vary from territory to territory. Data stored on-premises in infrastructure owned and operated by an organization is governed by one set of compliance laws, while when that data is stored in the cloud it will likely be governed by another.
Metadata—information about a dataset’s origin—is particularly vulnerable to bad actors because it often contains sensitive PII, such as names, IP addresses and physical locations of individuals associated with the data.
Moving data from on-premises storage to the cloud requires data management expertise in cloud computing that can force companies to hire new talent or retrain existing IT teams (upskilling), both of which are potentially costly and resource-intensive propositions.
Popular examples of new skills IT teams need to acquire to work in a data cloud environment include dealing with data governance and security, mastering data modeling and workflows for the cloud, and learning the specifics of ingesting and integrating data into a cloud storage environment.
From building new, innovative apps to improving customer experiences, data clouds are helping organizations find new ways to manage and use their most valuable data. Here are some of the most popular and effective use cases for data clouds today.
Cloud computing is at the center of modern app development, enabling developers to streamline their development lifecycles by writing code, deploying and managing databases, and testing app functions—all in the cloud.
Data clouds simplify how developers interact with datasets and integrate them into the apps they're building. Using edge computing and Internet of Things (IoT) capabilities, data clouds help move apps closer to data sources, allowing applications that stream large amounts of real-time data (known as data streams)—such as Twitch and TikTok—to function.
Modern data clouds store both structured and unstructured data, enabling users to analyze both sets, easily and securely, for various analytical purposes. For example, analysts can use data clouds to better understand customer relationship management (CRM) and customer data and create customer profiles to help solve business problems, a process known as identity resolution.
Data clouds are also used extensively in sentiment analysis and the creation of customer data platforms to analyze large volumes of textual data to determine whether it expresses a positive or negative sentiment.
Organizations looking to use artificial intelligence (AI) for business purposes rely on data clouds for a centralized, highly scalable data storage solution that allows massive amounts of data to be processed during the training of AI models. In a modern data cloud, text, images, audio, video, sensorial and other kinds of data can all be safely stored and easily accessed from a secure location.
Data clouds in marketing (known as marketing clouds) help enable cutting-edge AI capabilities such as predictive analytics, natural language processing (NLP) and image recognition to be programmed into advanced applications. For example, Salesforce’s Agentforce is an AI solution that performs data-driven actions across multiple business functions that previously required human intervention.1
Modern data clouds play an important role in business continuity disaster recovery (BCDR) processes, helping businesses return to normal operations when a disaster strikes.
Before data clouds, data had to be moved between storage on different platforms, a process that grew increasingly difficult as the volumes of data companies needed to store became larger.
The data cloud can host mission-critical workloads in one, connected infrastructure, providing fast, secure access and a robust suite of security and recovery options.
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1 Agentforce from Salesforce—Impacts on Enterprise Data, Forbes, 3 September 2024.