Data analytics is the most comprehensive way to gain a competitive advantage in business today. It is the data that you can cultivate to continue to climb in the business, and that’s where the importance of data. As the volume of data increases, the cloud becomes an obvious part of it. Be it for storage or analysis, and the cloud is in the rage. Cloud-based analytics or cloud analytics has gained importance in this context, and the market is expected to reach $57.055 million by 2023. The only reason for it is that most businesses acknowledge that the cloud is the best place to run enterprise-scale analytics.

Cloud analytics – the term itself defines it. It is any data analytics or business intelligence performed on the cloud with a service provider’s help. The cloud model could be public or private, eliminating the cost of on-premises data storage and its maintenance and computation. It can also be termed as Cloud analytics as a service model similar to software as a service model.

The most common service offered by cloud analytics is on-demand business intelligence. In cloud analytics, analytic algorithms are applied in the cloud against data in a public or private cloud to deliver the desired result. It also involves the deployment of scalable cloud computing with analytic software that helps to identify patterns in data and extract new insights.

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Types of Cloud Analytics

In Cloud analytics, all the data analysis-related work like data storage, ingestion, aggregation, reporting, etc., happens in the cloud. It could be partial or full. Now, depending on the cloud environment, cloud analytics can be of three types –

Analytics in the public cloud

This is the most economical way to conduct cloud analysis since the infrastructure cost is divided among the cloud tenants. In this multi-tenant architecture, data storage, and processing publicly accessible. Besides, IT systems are shared in this architecture but not the data.

Analytics in the private cloud

If your goal is to maintain enhanced control over the IT infrastructure and data compliance and data security, then cloud analytics in the private cloud is the best option for you. In this scenario, either the private cloud is physically located at the user’s own data center or the cloud provider’s site. In contrast, hardware and software are dedicated to the user. However, this option is secured enough but expensive too.

Analytics in the hybrid cloud

If data analysis in the private cloud is expensive for you, but you still need to meet the data regulatory requirements like GDPR, HIPAA, GLBA, etc., then a hybrid cloud is the best option for your data analysis. In this case, you can divide a load of data analysis partially to the public cloud and remaining in the private cloud. This type of architecture significantly reduces the data analysis cost; at the same time, it helps you stay compliant with the data regulation.

However, irrespective of the cloud analytics type, each has its own advantages like less hardware related expense, better scalability, and high fault tolerance. Hence, depending on your budget, you can select the cloud analytics model.

Cloud Analytics Elements

As mentioned in the beginning, cloud analytics works on an internet platform through a software system. Furthermore, these systems run on servers that remain in the data center. Companies like Amazon, Microsoft, and Google have large data centers, and these are some of the popular cloud analytics computing platforms. The powerful servers of these data centers support cloud analytics tools. The data collected by these tools are stored in the cloud from where it can be retrieved fast and remain safe.

According to Gartner, cloud analytics can encompass any of the following elements in the cloud:

Data sources — In cloud analytics, data sources are off-premises and stored in the cloud. Not only stored but also it is delivered by the cloud. Moreover, you can access data in real-time. Another advantage of cloud-based data is that they are not slow, and there is no chance of the data being outdated.

Besides, with cloud models, you can pull multiple and different data plots, no matter whether it is business analytics or web. At the same time, you can thread them all in real-time. This puts tremendous value on data insights. To explain more, cloud analytics delivers data as soon as it is available in the data sources.

Data models — In cloud analytics, data models are created with structured data types. These data models standardize and make sense of how data points are related to each other. Cloud data models learn depending on the constantly updating data pools. Furthermore, a cloud solution comes with progressive models, uploads, applications, and more. As a result, you won’t have to spend any time building out your own uses. Also, you can add the models yourself.

Processing applications — In the usual data analysis process, large volumes of big data are processed in a data warehouse. Furthermore, for analysis, data needs to be prepared properly before use. On the contrary, in cloud analytics, your processing application is up in the cloud and often managed and maintained by the third-party vendor. This means you can process data faster than an on-premise system.

Hence, it is more readily available too. In this case, the processing application is always on and ready to crunch data for analysis. Furthermore, the computing power in cloud analytics is high, which is not the case for an on-site solution since you cannot consistently access data. As a result, data processing is slow.

Computing power — For data analysis, raw computing power is a big factor needed for data ingestion, structure, and analyzing data at scale. Moreover, when it comes to cloud analytics solutions, computing power is a serious matter to consider. To process massive data in a reasonable amount of time, we need a decent hardware stack. Without the right hardware, it often becomes time-consuming for data to be processed and parsed. This often results in upgrading the hardware, which eats up both time and money.

When you move for cloud-based software with analytics embedded, you can achieve cloud-based analytics along with cloud-based computational power. This is a significant achievement in terms of raw computational power. Furthermore, it can burden off you with budgetary concerns.

Analytic models — Analytics models are based on functional mathematical models that predict outcomes. These analytics models are the backbone of business analytics and pull abstract plots of data into more understandable and cohesive terms. However, such models need strong computing power. Interestingly, cloud analytics always has access to the latest models created by the vendors, which, in other terms, is the greatest advantage.

Moreover, most cloud analytics software supports models that can cover up to 95 percent of business needs. As a result, you don’t need to come up with your own complex data models.

Data sharing and storage — Data warehouses let organizations scale quickly and easily. However, all that data has to end up somewhere. Hence, if you plan to keep data around for long-term analysis, it is a considerable fact of how and where that data will be stored. Even though you can get cheap data storage, retrieving data isn’t always cheap or efficient. Furthermore, long term storage might become yet another burden for the IT department. Cloud answers this storage issue in an optimum way by storing petabytes of data that costs very reasonable and with no retrieval issue.

Cloud analytics and AI

Cloud analytics is often associated with artificial intelligence (AI) technologies like machine learning (ML) and deep learning (DL). It is commonly used in scientific research in genomics, industry applications like oil and gas fields, business intelligence, the Internet of Things (IoT), security, and many others.

AI and other analytics approaches leverage organizations of all sizes to quickly make data-driven decisions to gain efficiencies in their products and services. No doubt, the cloud is an indispensable platform that enables a user to experiment with ideas through proofs of concept (POCs) while providing a rich software ecosystem.

What Are Cloud Analytics Tools?

Cloud analytics tools include features like data ingestion, storage, analysis, and reporting. Various cloud-based analytics tools are in use in the industry. Here are some of the most popular cloud analytics vendors:

Oracle Analytics Cloud (OAC)

Features

• Powerful and compelling data visualization with the power of augmented analytics and NLP
• Intelligent insights with built-in machine learning capability
• Automated data preparation with machine learning help augment your data and suggest smart enrichment options before you even start.
• Instantly understand data.
• Fast and one-click predictive analytics with the help of machine learning.
• Mobile analytics with the power of NLP.
• Powerful collaboration and publishing capability
• Embedded analytics

IBM Cognos Analytics

Cognos Analytics is an AI-fueled business intelligence platform that supports the entire analytics cycle, from discovery to operationalization. This model has three major services – IBM Cognos Framework Manager, IBM Cognos Cube Designer, IBM Cognos Transformer.

Features

• Built-in data management and data governance
• automated data modeling
• natural language-powered AI.
• Compelling and interactive report dashboard.
• Uncover the pattern hidden in the data
• Easy collaboration
• Clean and combine data in minutes

Qlik

There are three services available for analytics – Qlik Sense, QlikView, Qlik Analytics Platform

Features:

• Turn raw data into remarkable outcomes with end-to-end, multi-cloud data integration and analytics solutions
• Built-in data preparation and integration
• drag-and-drop visualizations
• smart search feature
• real-time analytics and reporting
• data storytelling functionality
• secure real-time collaboration

Domo 

Domo is the only cloud-native platform that combines iPaaS capabilities for data integration, visualizations for real-time and predictive insights, and a foundation for building your own apps to take immediate action on those insights.

Features:

• Specializes in business intelligence tools and data visualization.
• 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 in and hides the rest.

Benefits of Cloud Analytics

Cloud Analytics enables users to increase complex data analysis easily, rapidly, and accessible on extensive data sets. It is particularly a feasible solution for several reasons:

• The amount of data used for analysis is growing at staggering rates, and solving the storage problem much of it is being pooled in the cloud or on edge.
• Cloud services are much easier to deploy as these are automated services that don’t require deployment and maintenance of physical hardware.
• The cloud business model allows users to turn services on and off as needed. This is a cost-saving approach too.
• The cloud allows dynamic resizing of resources. As a result, users can easily apply to compute, and storage as per business requirement and scale them as needs change.
• Cloud analytics has very effective use in testing genomic data.
• As many vendors offer cloud analytics service that helps to identify patterns in speech, images, and videos, this, in turn, helps to improve customer service.
• With the help of cloud analytics, you can identify patterns of disease reporting, which helps to improve the availability of medicine and vaccines

Final thought

Cloud analytics that cobble two terms together also maintains some substantial differences between on-premise data analysis and cloud-analytics solutions. No doubt, you can do the same analysis on-premises, but the power of the cloud can give your access to data quickly and with little maintenance. Well, what other unexpected benefits do you think the cloud could bring to business analytics?