An Introduction to the Basics of Cloud Analytics

Deepshikha Verma 13 Feb, 2023 • 7 min read

Introduction

According to Wikipedia, Cloud analytics constitute a set of technological and analytical tools and techniques housed in a cloud that are specifically designed to help users derive information, carry out analysis from massive data, and make it available on the internet to derive actionable business insights.

However, to begin with, we must know what CLOUD is or what cloud computing is, forming the basis of cloud analytics. The term ‘CLOUD’ in the computer world is a term used for clusters of computers that combine and share their computing powers that make up the internet infrastructure. Cloud computing, thus, refers to the services delivered by these clusters of computers through the internet. These can include services like networking, servers, storage or databases, software, and other high-level like Artificial Intelligence, Business Intelligence, CRM solutions, and machine learning systems.

Cloud Analytics

With our everyday life getting increasingly online, the internet has become a storehouse of massive information. Storing, maintaining, and using this information can be a big challenge, especially for businesses. Cloud computing steps in here and allows its users to rent or purchase computer services from a cloud service provider on an on-demand basis. The cloud service provider is equipped to store and maintain such an enormous inflow of data and reduce the costs of the infrastructure along with computing.

Cloud Analytics

Learning Objectives

  1. Understand what is could analytics and how it works.
  2. Learn about the components of cloud computing.
  3.  Understand how the use of cloud computing helps us.

This article was published as a part of the Data Science Blogathon.

Table of Contents

What is CloudAnalytics?

As the name suggests, Cloud analysis is storing and analyzing data to generate solutions like charting, categorization, dashboards, and pivot tables after querying the data hosted in the data warehouse in the data centers. Just like on-premises data analysis, cloud analytics applies algorithms to large data collections to identify patterns, predict results, and produce actionable business insights while adding efficiency to traditional on-premises analysis. With cloud analytics, businesses do not need to purchase, house, or maintain expensive data centers that increase multifold costs. Instead, it can be shared with the help of cloud service providers while scaling it to levels that are otherwise difficult to match at the same cost. The subscriber can subscribe or unsubscribe from the services depending on the requirements.

For example, a company could subscribe to a data warehouse and BI tool like SaaS BI that can deliver different types of reports or charts after querying the data hosted in the cloud.

How does Cloud Analytics Work?

Cloud analytics systems, in most cases, run on state-of-the-art data centers hosted on an internet platform. Users can subscribe to a suitable cloud service provider for processing power and storage space to analyze volumes of data related to them. The cloud analytics system can then clean, organize, process, and analyze the data using proprietary algorithms, and results following the analysis can be shared in the form of categorizations, reports, and business insights through different data visualizations and other intuitive formats through.

HOW DOES IT WORK?

Basic Components of Cloud Analytics

According to Gartner, each cloud analytics solution can have its unique set of features, but all solutions usually have some common features. All solutions contain data sources, data models, computing power, analytic models, processing applications, and sharing or storage of results. The solutions can be provided under a subscription-based or utility (pay-per-use) pricing model. Subscribers can opt for one or more services.

Data Sources: Organizations may be collecting data from different sources. Together, they constitute data sources for the cloud which can be further analyzed. Examples include social media data and data from CRM and ERP systems.

Data Models: As different types of data sources constitute the feed for the cloud, ways to retrieve them, too, need to be different. A data model is a blueprint that decides how the data will be retrieved from a particular resource. It retrieves data and standardizes the points of data that are co-related for the analysis of different data sources. Data sources in the cloud can be a single column of a spreadsheet or involve several multidimensional triggers and parameters.

Processing Applications: A cloud can host multiple processing applications to serve different client requirements. It can house simple applications to generate reports, charts, or categorizations and even full-fledged solutions like Artificial Intelligence, Business Intelligence, CRM solutions, and machine learning systems together under one cloud.

Computing Power: The cloud hosts computing power close to that of server/s. Operations like input, cleaning, structuring, and finally, analyzing large volumes of data queried by the client are thus easily empowered by this system.

Analytic Models: These are mathematical models that can be used to analyze complex data sets to establish business insights for the future.

Data Sharing and Storage: Data warehousing is one of the services by cloud analysis solutions to promote effortless scalability of the business/es. Data-warehouse is an electronic storehouse of data from different data sources related to a subscriber that can be used for analysis and reporting etc.

Reporting: Cloud analysis solutions usually host tools for reporting as well. Backed by detailed data, it can create accurate, detailed reports that you can confidently bring to the directors, lenders, or investors and derive actionable business insights.

In addition to these features, AI (Artificial Intelligence) is increasingly becoming an integral part of cloud analytics. Machine learning algorithms support cloud analytics systems to self-learn and predict future trends more accurately.

What are the Benefits of Cloud Analytics?

Apart from the many benefits that cloud analysis has, here are a few benefits with the biggest impact on business.

BENEFITS

Data Consolidation

A business today can make its presence felt with or without physical boundaries. Powered by the internet, it can carry business and generate data from multiple locations across the world, no matter the size. Cloud analytics can bring all of a company’s data sources together to give a more wholesome view to all concerned, irrespective of their physical location (or the data’s location). They can, thus, easily access this data from one place, gain more accurate insights and make better business decisions in real-time.

Sharing and Collaboration

Data can be siloed even in departments within the organization like Finance, IT, Marketing, Sales, Human Resources, etc.. Each could be maintaining its database or using its analysis tools. Integrating data in such a scenario to achieve common goals and make wholesome decisions can be very challenging and affect the whole business. A cloud analytics solution can better integrate the data from different parts of the organization — subject to configurable role-based access controls — leading to better sharing and collaboration between the departments. This will save the need for staff training on different systems, thus saving man hours of the company.

Scalability

With a growing business, it’s natural to have increasing infrastructure demands to accommodate its operations. Meeting such demands can be overwhelming for the company both in terms of planning and expenditure. Also, if demand falls in the future, it will lead to overprovisioning. Cloud platforms provide on-demand capabilities that can be scaled up or down by subscribing or unsubscribing services according to the user’s requirements.

Cost Reduction

Setting up new infrastructure to meet the company’s growing demand, frequent upgrades, migrations, etc. can increase the cost of operations exorbitantly in an on-premises analysis model. More so, this can lead to system downtime affecting business continuity. More so, employing skilled staff to handle this big setup might not be easy to afford in-house. To save all this expenditure, one can opt for cloud analysis, which will both reduce the costs of infrastructure and maintenance, and also has in-house experts available for the subscribers.

Security

Most cloud analytic solutions back data regularly to servers in multiple locations, ensuring protection during a fire or natural disaster. Unlike the IT staff in an on-premises platform, security is the full-time focus for a cloud host. The sheer enormity of data on the cloud necessitates cloud analytics providers to use robust encryption to ensure data security while it is transmitted over networks. No information is shared through insecure methods like email or flash drives. In addition, all data is password protected, with strong access permissions in place, to avoid unauthorized access. Audit logs are maintained to provide visibility into the user accessing the data and what was done with it.

However, the biggest security advantage of the cloud may be that the data is stored offsite. According to a study, around 34 percent of all security breaches result from insider threats. Since no information is stored locally, there are no local hard drives to steal and no passwords to compromise.

With compelling advantages, cloud analytics is increasingly becoming a favorite among organizations.

Conclusion

So, in the end, we can safely take home the following:

• With businesses making their presence felt increasingly across the world using different future technologies, it’s time to bring in a futuristic technology to manage ever-increasing data processing demands, connectivity, and making real-time business decisions.

• It adds to the features of the on-premises model of analysis and makes it available to all concerned in real-time without compromising on security

• It can be an answer to the growing demands of the business with reduced cost than that of the on-premises model of analysis, along with the flexibility of scaling up or down the resources or services as and when required by the subscriber.

• For optimum efficiency, though, one must carefully match the demands of the enterprise with the features made available by the cloud analytic system. Cloud service providers are usually quite flexible and allow you to choose services that makeup almost a tailor-made solution for your enterprise.

It’s time to harness the power of the Cloud to scale new heights for your business.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

Deepshikha Verma 13 Feb 2023

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