What is a fully integrated cloud-based data analytics platform?

What is a fully integrated cloud-based data analytics platform?

A fully integrated cloud-based data analytics platform manages all aspects of the analytics process -- including data collection, transformation, analysis, storage, visualization and collaboration -- through a unified, cloud-hosted system.

Unlike tools that cover only certain parts of data analysis, a fully integrated platform offers a comprehensive, all-in-one process.

For example, a cloud-based data warehouse stores data but lacks built-in data collection, analytics, reporting or visualization capabilities. Similarly, software that identifies trends or insights isn't a full data analytics platform because it only handles the analytics step, not the processes that need to occur before analytics can happen or those that occur afterward to help interpret analytics results.

Fully integrated cloud-based data analytics platforms also differ from on-premises end-to-end analytics systems. Although the on-premises platforms might address all aspects of the data analytics process, they are often harder to scale than cloud-based alternatives. Their processing capabilities are constrained by local infrastructure. Expanding that infrastructure is a much more complex and time-consuming process than provisioning additional infrastructure in a cloud environment.

Key capabilities of fully integrated cloud-based data analytics

A fully integrated cloud-based data analytics platform should provide the following capabilities.

Data collection

The platform must be able to collect or ingest data from multiple sources and move it to a centralized location. This is crucial because businesses often analyze data originating in disparate systems. The analytics platforms must be able to pull data from those sources to ensure it is available for the rest of the analytics process.

Data transformation

After ingesting data, analytics platforms often need to transform it to prepare it for analysis. Transformation can involve removing redundant entries, converting data formats or standardizing data structures.

The transformation process is important because feeding raw, unprocessed data into an analytics engine results in inaccurate or incomplete results. The system can't reliably analyze all the raw data. Additionally, untransformed data might take longer or require more compute resources to analyze, leading to inefficiencies.

Data storage

Some analytics workflows process data immediately after transformation, especially in real-time streaming scenarios. But many data analytics use cases require temporary or long-term storage. For instance, in a batch-processing approach to data analytics -- meaning you run analytics operations in batches instead of performing them continuously -- incoming data is stored until you have acquired enough to analyze the next batch.

Businesses might also need to retain data after analysis for compliance purposes; regulations often require retaining certain types of information for a fixed period of time.

To support these needs, fully integrated cloud-based data analytics platforms provide scalable storage resources.

Data analysis

Data analysis is the process of examining data to derive relevant insights. Teams define questions they want to answer beforehand and then deploy automated data analytics tools that can parse data sets to answer those questions.

If a business wants to predict the next quarter's sales for a specific product, it could feed historical sales data into a predictive data analytics engine that is part of its cloud-based analytics platform. By identifying patterns in historical data and extrapolating them into the future, the analytics engine generates a forecast.

Data visualization

To interpret analytics results, visualization tools generate graphs and charts that highlight key trends or takeaways. Fully integrated cloud-based analytics platforms address these needs through built-in visualization tools that allow users to produce visual interpretations of data.

Collaboration

Collaboration is the process of sharing data with other stakeholders. The individual or team responsible for running analytics is not the only party that benefits from the insights. For example, a sales forecast might be useful for sales and marketing because the information can help shape upcoming campaigns.

To facilitate collaboration, fully integrated cloud-based analytics platforms include data reporting tools that summarize key takeaways and might offer built-in communication features that allow teams to share and discuss reports within the data platform.

Advantages of cloud-based data analytics

Cloud-based data analytics offers distinct advantages over traditional on-premises systems, making data processing more efficient and adaptable. Scalable infrastructure supports growing workloads, while built-in tools simplify management and security.

• Scalability. Cloud platforms provide virtually infinite compute, memory and storage resources, making it easy to scale analytics operations up or down quickly. On-premises infrastructure, by contrast, requires adding or removing servers or disk arrays instantaneously in response to changes in infrastructure requirements.
• Real-time data processing and analysis. The massive scalability of the cloud helps enable real-time data processing and analysis by ensuring that compute, memory and storage resources are always available. On-premises environments can experience resource shortages during peak demand, forcing delays in data processing.
• Cost-effectiveness. Fully integrated cloud-based analytics can deliver higher ROI for two main reasons. First, purchasing an all-in-one analytics platform is typically more cost-effective than acquiring and integrating individual tools. Second, cloud infrastructure scales dynamically, allowing organizations to scale infrastructure down when they don't need as much to avoid paying for unused capacity.
• Improved data security. While on-premises analytics ensure that sensitive data remains within an organization's infrastructure, modern cloud-based analytics platforms often include more rigorous security controls than many businesses can implement themselves on-premises. Additionally, a fully integrated analytics platform eliminates the need to move data between disparate tools or secure each tool separately, which can increase the risk of oversights that lead to the exposure of sensitive information.
• Ease-of-use. Fully integrated cloud-based data analytics system simplifies deployment, management and maintenance. Instead of acquiring and configuring infrastructure, installing data management software or integrating software, organizations can just create an account, connect the platform to data sources, configure it based on your business needs and priorities and then start the analytics process with minimal setup.

That being said, a fully integrated cloud-based data analytics platform is not required if you want to analyze data. Businesses with in-house expertise might prefer to implement a custom data analytics pipeline using individual tools -- such as data collectors for ingestion, data processing tools for transformation and a data warehouse for storage -- and integrate them into a comprehensive system. Another option is to deploy an integrated, all-in-one data analytics platform on-premises instead of in the cloud. Still, these approaches might lack the key advantages of fully integrated cloud-based data analytics.

Chris Tozzi is a freelance writer, research adviser, and professor of IT and society. He has previously worked as a journalist and Linux systems administrator.