Data analytics is essential for the success of large-scale businesses. In fact, any company that deals with any type of data set needs data analytics. It helps businesses anticipate risks, identify new opportunities, and reduce costs. Simply put, data analytics allows organizations to make effective decisions.
In the past, companies used to collect, track, and analyze data in on-premises systems. However, as more companies are shifting towards the cloud, cloud analytics is becoming more popular. This is because cloud analytics is more efficient, and it provides excellent processing power. Powerful cloud-based data analytics tools help companies analyze large data sets so that they can improve their business performance.
But, what is data analytics, and what is cloud analytics? In this article, we’ll provide a thorough data analytics definition and cloud analytics definition. This article will also cover other key concepts, such as data analytics tools, data analytics platforms, analytics solutions, analytics services, analytics cloud, cloud analytics architecture, cloud-native analytics, benefits of data analytics, and more.
Before we discuss data analytics in the cloud, let’s first answer the big question: what does analytics mean?
Data Analytics Definition
Data analytics is the process of analyzing raw data to make sense of it and draw out actionable insights. Businesses mainly use four primary types of data analytics:
- Descriptive: allows companies to find answers to questions related to current happenings
- Predictive: helps determine future outcomes
- Diagnostic: helps understand why certain things happen
- Prescriptive: helps businesses understand what needs to be done
What is Cloud Analytics?
Cloud Data Analytics Definition
Cloud analytics refers to storing and analyzing data in the cloud instead of in on-premises systems. In other words, cloud analytics refers to any type of data analytics that is performed on systems hosted in the cloud.
How Cloud Computing and Data Analytics Work
A simple on-premises analytics platform or analytics service can work for analyzing a few thousands of data points, but it can’t handle large datasets of millions. This is where you need cloud-based data analytics tools.
A cloud-based data analytics platform allows businesses to work with big data sets efficiently. For example, predictive analysis requires a lot of data and computing power, which cloud computing< can provide. Hence, cloud analytics tools help businesses identify trends and future outcomes and determine areas for improvement. Additionally, a cloud analytics platform is scalable and more affordable compared to on-premise systems.
Some cloud-based analytics services are designed for only one type of data, such as website traffic or social media usage. However, there are also broad analytics cloud services that you can use for different types of data sets to get a unified view of how your business is performing. A cloud-based analytics service for developing dashboards gives a unified picture of your critical business data, including AI analytics, product analytics, and cybersecurity analytics.
There are tons of cloud analytics applications, such as cloud-based predictive analytics, analyzing financial market data, website traffic, or climate trends based on data, understating data collected from sensors, and more. Another cloud analytics example is cloud-based digital marketing analytics to track the open rate, success, and ROI of marketing campaigns.
Components of a Cloud Analytics Solution
Although different cloud analytics platforms offer different feature sets, the following are the basic components of the best cloud analytics platforms:
- Data sources: These include different sources which your business collects data from, such as social media data, web traffic data, etc. You can link your cloud analytics platform to different data sources and get the latest results with automatic refreshing.
- Data models: These organize elements of data and standardize connections between them. Before choosing a cloud analytics platform, consider whether it provides data models that match your organization’s data sets.
- Analytic models: Some cloud data analytics solutions come with prebuilt analytics models for analyzing complex data sets.
- Computing power: Cloud analytics solutions provide great computing power required to store, process, and analyze large volumes of data.
- Processing applications Cloud analytics solutions involve special applications that help process large data sets efficiently.
- Data storage: Cloud analytics platforms allow quick data sharing so that employees can collaborate efficiently. They also allow companies to scale up or down as needed.
The cloud analytics diagram below will help you understand the cloud analytics architecture:
Types of Cloud-Based Analytics Platforms
There are three main types of cloud-based analytics solutions, namely private cloud analytics, public cloud analytics, and hybrid cloud analytics/hybrid cloud data analytics. There is also multi-cloud analytics, but it isn’t as common as the other three types. The type of cloud data analytics platform you choose depends on the cloud computing environment.
Public cloud is a type of cloud that multiple companies or organizations can use simultaneously. However, the data and information of each organization are isolated. In a public cloud, a third-party cloud service provider hosts and manages computing resources, hardware, and software. The security and maintenance of the servers are also the responsibility of the service provider.
The private cloud is another type of cloud computing environment that only one organization can use and access. This means the computing resources, hardware, servers, and storage are dedicated to a single customer. Hence, a private cloud is a single-tenant cloud environment. There are several types of private cloud, such as virtual private cloud, hosted private cloud, and managed private cloud.
A hybrid cloud refers to a type of cloud that is a combination of on-premise infrastructure, private cloud, and public cloud. Organizations usually use a hybrid cloud to reap the benefits of both private and public clouds.
For example, with the hybrid cloud, companies can store their less sensitive data in the public cloud, whereas confidential data can go in the more secure private cloud. A hybrid cloud also offers other benefits, such as reduced costs, increased security and control, additional resources, and more.
Ridge Cloud is an alternative to the public cloud which enables companies to deploy on any infrastructure and in any location. Ridge is able to provide businesses with a cloud customized for specific throughput, locality, and commercial requirements. The Ridge platform can be integrated into private data centers, on-prem servers, edge micro-centers, and multi-facility hybrid environments. With Ridge Cloud, organizations can reap all the benefits of cloud analytics while maintaining their existing infrastructure.
What Is Edge Analytics?
Edge analytics is a type of data analysis that involves collecting, processing, and analyzing data from the sensors and IoT devices at the point of collection. It involves automated algorithms to process and analyze data before it is sent to some centralized storage system.
Simply put, edge analytics is the collection and analysis of real-time analytics data to draw out actionable insights and make effective decisions quickly.
Edge analytics has become more popular with the rise of the Internet of Things (IoT) and IoT analytics. IoT devices produce lots of data, which can be difficult to manage. Edge analytics solves this issue by analyzing data at the point of collection. However, many businesses also use IoT cloud analytics to store and analyze large IoT data in the cloud.
Edge Analytics Vs. Cloud Analytics
The key difference between edge analytics and cloud analytics is that in edge analytics, the data analysis happens at the device that produces the data, whereas cloud analytics means performing analytics in the cloud. In edge analytics, the data isn’t sent to the cloud for analysis, but may still be stored in the cloud.
Analytics as a Service In Cloud Computing
Analytics as a service (AaaS) provides analytics software and procedures. Cloud analytics as a service delivers efficient business intelligence analytics solutions that help in organizing, analyzing, and presenting big data so that it is easy to understand and interpret. AaaS uses advanced data analytics technologies, such as machine learning algorithms, AI, predictive analytics, data mining, and more, to analyze data and show trends.
Pros and Cons of Cloud Analytics
Advantages of Cloud Analytics
Below are some key benefits of using cloud computing for data analytics:
A big advantage of using a fully integrated cloud-based data analytics platform is that it allows employees to work together from anywhere. Employees can share files and access data quickly, and collaborate in real time.
Fully integrated cloud-based data analytics is more efficient than analyzing data in on-premises systems. Cloud analytics delivers incredible processing power more efficiently compared to on-premise solutions because the cloud provides incredible processing power and a large storage capacity to analyze big data efficiently.
Unified View of Data
When different departments in an organization use different tools for analysis, you need to train the employees on multiple systems, which can waste valuable time. This is where cloud analytics software is helpful. Cloud analytics technologies consolidate an organization’s data from different sources and provide a detailed and unified view of the data.
Scalability and Flexibility
Cloud analytics in cloud computing offers on-demand capabilities with incredible flexibility. With analytics on the cloud, organizations can scale up or scale down, depending on their requirements. For example, organizations can ask cloud analytics companies to increase cloud storage as their business grows and they produce more data. Similarly, companies can add data analysis capacity as needed. Hence, a cloud-based analytics service helps companies to respond to customer demands quickly and efficiently.
On-premises systems involve lots of costs, such as purchasing and maintaining hardware and servers, power consumption costs, replacing the hardware, etc. However, with cloud-based analytics tools, organizations don’t have to worry about such costs as the cloud analytics providers or cloud analytics vendors are responsible for everything.
Do you want to learn the differences between cloud and on-premise in detail? Check out our article on cloud vs. on-premise.
Disadvantages of Data Analytics in the Cloud
Risk of cloud failure: Cloud failure is one of the biggest disadvantages of using cloud-based analytics software. Although rare, cloud failure can occur due to power outages or cyber-attacks. However, if you choose a reliable cloud data analytics company, this won’t be an issue.
Requires internet connection: You need to have internet access in order to access data in the cloud and perform analytics.
Frequently Asked Questions (FAQs)
What is data analytics?
Data and analytics are the two terms that are usually used together, especially in the business world. The data analytics meaning is analyzing raw data and drawing valuable insights from it. Data analytics companies provide different data analytics solutions for different types of analytics, including business analytics, marketing analytics, mobile analytics, and healthcare analytics.
What does data analytics in the cloud mean?
The cloud analytics meaning is using the cloud for analytics. Cloud and data analytics have become essential components of organizations worldwide. Analytics and cloud technology together help businesses draw meaningful insights from raw data. Organizations can access data remotely and efficiently process big data with cloud analytics services.
What are the advantages of a cloud-based data analytics platform?
Data analytics in cloud computing or analytics on the cloud is preferable because it provides several benefits over on-premises analytics tools. Advantages of cloud-based analytics platforms include scalability, cost reductions, flexibility, and high efficiency. Another advantage of a fully integrated cloud-based data analytics platform is that it provides a unified view of an organization’s data.