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:
- Aggregate multiple disparate data sources
- Create access to data for users and stakeholders
- Provide collaboration and insight sharing features
- Eliminate costs associated with managing and owning data analytics infrastructure
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.
- Data sources — Original sources of data to be aggregated. The range of data is wide, including both internal sources, ERPs, CRMs, and external sources, social media, website usage, and third-party data vendors.
- Data models — Data models reconcile the disparate data sources through standardization to a structured data type. Data models are essential in making sense of the relationships between data points.
- Computing power — Compute and data storage capacity at scale is essential. This is a defining characteristic of cloud environments, and the primary reason for operating analytics in the cloud.
- Processing applications — Using huge processing power, applications like Hadoop have the capabilities to ingest and process the volumes of big data that flows into cloud analytics systems.
- Analytics models — Mathematical models used in predicting future probabilities based on large data sets. These models may leverage machine learning, artificial intelligence, and deep learning methods.
- Data sharing and storage — It's not enough to process data, it then must be made available to those who can use it most effectively. Data warehouses and data marts are used to help disseminate analytics insights relevant to specific groups within the organization.
Cloud computing refers to the on-demand availability of compute, storage, and networking resources, usually provided by a third-party cloud vendor or cloud service provider (CSP), and following a cloud service model. The cloud service model is a set of IT-related services offered by CSPs, and come in three major forms: infrastructure-as-a-service (IaaS), platform-as-a-service (Paas), and software-as-a-service (SaaS). Various other service models exist with more or less features, like database-as-a-service (DaaS), which is a form of software specialization.
Cloud based analytics is considered a Software-as-a-Service (SaaS) model, and sometimes called Cloud Analytics-as-a-Service (CAaaS), and functions as a service or possible extension to an organization's IT. Other common cloud analytics configurations include bridging a SaaS model with on-premise or private cloud infrastructure. This hybrid configuration allows companies to control where sensitive data resides, typically secured on on-premise storage, while other data resides on cheap and scalable infrastructure in the cloud.
Cloud analytics has several key benefits.
- Update business software — Businesses that move to cloud analytics from outdated legacy systems leapfrog those that continue to hold on to antiquated models. Once the decision is made to migrate to the cloud, organizations can build and own their own configurations, integrating cloud into their on-premise ERP and other data systems. Or, they can choose to perform a full cloud migration. Cloud analytics can be as versatile in updating legacy systems as they need to be.
- Improve productivity — Cloud analytics provide advanced tools, like automation, that improve productivity by eliminating the need for people in many mundane and error prone tasks. As well, many of these tools, like natural language search for data discovery, provide superior performance.
- Automations emancipate time — Automations help to emancipate time spent administering data operations, and return it to staff who now can spend it improving system performance.
- Reduced error — Many data analytics tools help to collect, clean and prepare data for processing. Tools like data mapping and deduplication perform tasks simply time consuming for humans.
- Consolidated data — Today, data is produced in staggering quantities, and organizations may expect to synthesize insights from hundreds, even thousands of data sources. Cloud data analytics platforms are specifically designed for this type of data ingestion.
- Improved workflows and processes — Analytics platforms standardize data workflows to ensure that data is accurate and consistent, but also allow significant customizability to fit the needs of users. Together, organizations are able to enforce improved workflows and rely on proven processes.
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.
