Cloud Analytics

Cloud Analytics Definition

Cloud analytics encompasses any kind of business intelligence or data analytics performed on a cloud in conjunction with a service provider. The data processing is done on a private or public cloud to avoid the expense and maintenance of on-premises data storage and compute. Cloud-based analytics is also called a Software as a Service model or Cloud Analytics as a Service model. Some companies use a hybrid model that keeps some functions on-premise while moving others to a cloud. Data warehouses and on-demand business intelligence are among the services most commonly offered by cloud analytics.

Image depicts cloud analytics: business intelligence or data analytics performed on a cloud in conjunction with a service provider.


What Is Cloud Analytics?

Cloud analytics is a service that runs data analysis and business intelligence operations in a public or private cloud. Cloud analytics companies help enterprises scale quickly by reducing the costs and administrative burden of on-premises hardware.

Cloud for analytics types:

  • Public cloud — Storage and data processing is publicly accessible on multi-tenant architecture that shares IT systems but not data.
  • Private cloud — Accessible only to one company and acts as an extension of the company’s IT infrastructure. Used when data privacy and security is paramount.
  • Hybrid cloud — A combination of public and private clouds and most effective when only a small amount of sensitive data needs to be in a private cloud.

Why Use Cloud Analytics?

Cloud analytics offer a number of benefits that include:

When companies have rapidly changing needs, the business intelligence tools of cloud analytics offer quick access to real-time data. This allows for faster and more accurate decisions. Cloud-based analytics services also make it easier to scale when enterprises aren’t tied to expensive and less flexible on-premises solutions. A hybrid analytics solution lets companies test a new projects before committing to on-premises investments.

Data consolidation
As the Internet of Things generates massive amounts of data at constantly increasing rates, the cloud has become a repository of many different kinds of data sources. Cloud computing data analytics allows enterprises to consolidate data and better understand the information it possesses. A cloud-based data warehouse makes data accessible to the people who need it. Consolidation also helps with data mining for creating real-time prediction models.

Easier access and scalability
Cloud-based analytics solutions allow enterprises to only use services when they are needed. This makes it easier to scale as a company grows. The data can also be accessed by the people who need it, wherever they are. Global companies benefit from increased sharing and collaboration in real time, which leads to a culture of data discovery.

How Cloud Analytics Works

Cloud analytics work through a software system that is hosted on an Internet platform. The systems run on servers in a data center.

Companies like Google and Amazon have giant data centers for cloud services. Amazon Web Services (AWS) and Microsoft’s Azure are the most popular computing platforms for cloud analytics. The centers house many powerful servers that support cloud-based data analytics tools.

The data collected by cloud-based analytics software is stored on the cloud where it can be accessed anywhere. The data can be retrieved quickly and will remain safe if any physical locations of a company experience a local disaster.

Eventually, cloud-based analytics tools will be able to learn on their own with the advent of machine learning algorithms. This will make it easier to predict future behavior based on past data and allow for more efficiency.

According to Gartner, a cloud analytics example encompasses any implementation of the following six elements in the cloud:

  • Data sources — Original data sources that can include social media analytics or website usage data.
  • Data models — Created with structured data types to make sense of and standardize how data points are related to each other.
  • Processing applications — Large volumes of big data are processed in a data warehouse.
  • Computing power — Raw computing power to ingest, structure and analyze data at scale.
  • Analytic models — Closed function mathematical models for predicting outcomes. They require strong computing power.
  • Data sharing and storage — Data warehouses as a service that let organizations quickly and easily scale.

What Are Cloud Analytics Tools?

Cloud analytics tools have features that include data ingestion, storage, analysis and reporting. A number of cloud-based analytics tools are available in the market. Some of the most popular cloud analytics vendors include:

Power BI — A business analytics service provided by Microsoft that consolidates data analytics into one place. Utilizes interactive graphic visualizations. Includes features like quick measures, forecasting, grouping and clustering. Supported by Salesforce, Excel and Sharepoint.

Domo — Specializes in business intelligence tools and data visualization. Founded in 2010. All functions can be managed from a dashboard in real-time. Data for multiple businesses can be viewed. A workbench imports large amounts of data from XML, ODBC and Excel. The analyzer application shows results users are most interested and hides the rest.

IBM Cognos Analytics — A business intelligence platform that uses AI and machine learning. Cognos recommends the best chart types for visualization. The analytics are self-service, using a natural language-powered AI assistant to discover patterns of information.

Geospatial Cloud Analytics

Geospatial cloud analytics is the process of looking at geospatial data. One example is a program created by the Defense Advanced Research Projects Agency (DARPA) called Geospatial Cloud Analytics (GCA). The program provides instant access to images anywhere in the world along with the tools to analyze the images. GCA virtually aggregates vast amounts of commercial and open-source satellite data available. A cloud-based repository offers automated tools for curating the data.

The data comes from multiple sources, which include synthetic aperture radar (SAR) and radio frequency (RF). The data can help the U.S. military better understand global situational awareness, event detection and tracking capabilities of forces around the world.

GCA will also offer analytical services for commercial entities in a competitive marketplace.

Does HEAVY.AI Offer Cloud Analytics?

Yes. HEAVY.AI equips IT operations analytics teams with real-time, accelerated cloud analytics tools for collaborative operational data visualizations to make business critical decisions at the speed of modern business.