Cloud analytics is the application of analytics to big data sets within cloud environments. While cloud environment gives the term cloud analytics its name, cloud analytics is essentially data analytics scaled in the cloud. Data analytics aims at extracting meaningful operational and business insights by discovering patterns within internal and external data sources.
Data analytics has moved to the scalable cloud as data increasingly becomes more voluminous and therefore more valuable when properly analyzed. Artificial Intelligence (AI), machine learning (ML), and deep learning (DL) are associated with cloud analytics because they are leveraged to make rapid data-driven decisions against such large data sets.
In practice, cloud analytics applications are found in industry and manufacturing, scientific research, business, security, Internet of Things (IoT), and business sectors that rely on data analysis to gain competitive advantage.
Cloud analytics solutions provide enterprise the following capabilities:
Scale data support to match business needs
Cloud analytics has certain infrastructure and software requirements. In many cases, it is easier for organizations to seek out trusted cloud vendors instead of assuming the risk and responsibility for deploying and operating their own private cloud analytics systems.
Gartner defines an analytics framework comprising six components for deploying data analytics in the cloud. The framework includes: data sources, data models, processing applications, computing power, analytic models, sharing and/or storage of data.
Cloud analytics has several key benefits.
Cloud analytics, or business intelligence (BI) platforms, support companies' ability to gain greater insight into their unstructured and structured data. Typical cloud analytics tools include data integration, cleaning, blending, discovery, and analysis tools. While these tools are powerful enough to drill deeply into data, and may require special skills, many analytics platforms also provide user-friendly and customizable data visualization dashboards for business users.
Analytics platforms come in two broad categories: all-in-one solutions, and point solutions. Two sub-categories stand out in the all-in-one solutions category, self-service analytics platforms, and embedded platforms.
Self-service platforms are designed for business users, and provide easy interfaces for non-coders. Users can rely on drag-and-drop interactions using pre-built templates, dashboards, and automatic tools, like NLP, for data discovery.
Embedded analytics software integrates into existing business systems, adding data analysis functionalities, and data sharing capabilities for end-users. Furthermore, these systems may include self-services features to support decision making for end-users.