What is cloud analytics?

Cloud analytics is the practice of running data analysis in the cloud rather than on infrastructure an organization owns. A cloud analytics platform is a solution that combines multiple analytic functions, including data acquisition, storage and analysis, offered in the cloud, often as a fully managed service.

Cloud analytic platforms are attractive to businesses because they can be deployed quickly and cost-effectively without the need for in-house expertise. They are also easy to scale as business requirements evolve.

Cloud analytics is primarily a question of deployment model: where the analytics environment runs, who operates it, and how it is paid for. For the broader category of capabilities that make up an analytics environment, see what is an analytics platform?, and for the underlying architecture, see what is a data analytics platform?

What are the advantages of a cloud-based data analytics platform?

Traditionally, analytics environments are deployed on-premises and have multiple expensive components. Organizations would typically purchase separate enterprise software for functions such as data integration, data warehousing and business intelligence. Each component typically ran on its own server and storage infrastructure that was expensive to acquire and required specialized skills to deploy and maintain.

Managing storage infrastructure is a particular challenge for organizations deploying analytic environments. Aside from the data center management challenges, on-premises infrastructure is inflexible. Sourcing, acquiring and installing infrastructure could take months, so organizations would typically purchase more infrastructure than they required, anticipating future growth. Businesses tend to make capital acquisitions and depreciate IT assets over multiple years, making it all but impossible to react quickly to changing business needs.

Cloud-based data analytics platforms deliver the data analytics capabilities and functionality businesses are looking to implement, include operational maintenance and can scale to support future business growth. In addition, cloud-based data analytics platforms offer predictability of cost and can be ideal for organizations where IT resources are limited.

Cloud analytics vs. on-premises analytics

The two deployment models differ less in what they analyze than in how the environment is acquired and run:

• Infrastructure: On-premises analytics runs on servers and storage the organization buys and houses. Cloud analytics runs on a provider's infrastructure and is accessed over the internet.
• Cost model: On-premises environments are capital expenses depreciated over several years. Cloud analytics is typically an operating expense, billed by subscription or by the resources consumed.
• Scalability: Adding on-premises capacity means procuring hardware. Cloud resources can be scaled up or down as workloads change.
• Operations: On-premises teams handle patching, upgrades and capacity planning. With a managed service, much of that work shifts to the provider.

Deployment models for cloud analytics

Not every cloud analytics environment looks the same. Common options include:

• Software-as-a-service (SaaS): The provider runs the full analytics application. Users sign in and work with data, with little or no infrastructure to manage.
• Fully managed platform: The organization configures its own data models, pipelines and content, while the provider operates the underlying infrastructure.
• Self-managed in public cloud: The organization runs analytics software on cloud infrastructure it administers itself.
• Hybrid: Some data and workloads remain on-premises while others run in the cloud, connected so that users can analyze both.

Why move to a cloud-based analytics platform?

Over the past decade, many enterprise applications have shifted to the cloud. Many organizations now run critical business applications such as sales force automation and enterprise resource planning entirely in the cloud using software-as-a-service (SaaS) offerings from Salesforce, Oracle, SAP and others. As core applications move to the cloud, it increasingly makes sense to host analytic platforms there as well. Compute and storage resources in the cloud have been highly cost-effective. Enterprise users pay for only the resources they need.

The shift to the cloud provides organizations with an excellent opportunity to modernize their analytics infrastructure. Rather than simply replicating costly and complex on-premises data warehouse and ETL environments in the cloud, organizations can take advantage of more capable cloud analytics platforms. These cloud-native environments utilize the latest technologies, including optimized columnar file formats and in-memory query engines to deliver excellent performance and self-service data.

Common use cases for cloud analytics

• Analytics on cloud applications: Reporting on data from SaaS ERP, CRM and HR systems without moving it back on-premises.
• Financial analytics: Close, consolidation and variance analysis that can scale up during period-end peaks.
• Supply chain analytics: Combining data from suppliers, logistics and inventory systems across locations.
• Operational analytics: Giving distributed teams access to current operational data from anywhere.
• AI and data science: Using elastic compute for model training and agentic AI workloads that would be costly to provision on-premises.

Challenges and considerations

Moving analytics to the cloud solves many problems, but it introduces others that should be planned for:

• Security and compliance: Organizations remain responsible for access controls, encryption settings and meeting regulatory requirements for where data is stored and processed. It is important to understand which responsibilities belong to the provider and which remain with the organization.
• Cost management: Unmonitored pay-as-you-go workloads can drive costs higher than expected.
• Data movement: Moving large volumes of on-premises data takes time. Reliable data connectors reduce the effort.

Best practices for migrating analytics to the cloud

1. Start with business priorities. Identify the analyses and users that will benefit most, rather than migrating everything at once.
2. Inventory data sources. Document which systems feed analytics, where they run, and how often data must be refreshed. Data mapping can support this step.
3. Define security and governance up front. Establish roles, access policies and data lineage before users arrive.
4. Plan for hybrid. Some sources may stay on-premises for a while; choose a platform that can work with data in both places.
5. Monitor usage and cost. Track consumption from the start so that scaling decisions are deliberate.

Incorta cloud analytics platform

Incorta Cloud is a state-of-the-art cloud analytics platform that enables users to spin up a world-class data analysis platform in a matter of minutes without assistance from IT. It provides a complete, end-to-end platform that can interface with virtually any data source on premises or in the cloud. Using Incorta Cloud, organizations can reduce cost, simplify their environments, and significantly improve the effectiveness of their analytics and easily scale up and down as requirements change for better business results.