What is Cloud Analytics?
Cloud analytics is a service model and delivery model that allows for cloud computing to be used in the hosting. It enables the analysis or computation of business data through cloud technologies. These technologies manage the storage and/or processing of data. Cloud analytics can be used to include any combination of data analytics or business intelligence, provided it is done in the cloud. A hybrid cloud analytics approach is used by some companies. Some functions are reserved for cloud-hosted environments, while others use on-premises servers. Some companies have begun to move full-time to the cloud to expand their analytics program to meet business growth. It also eliminates the need to perform traditional analytics on-premises.
This can prove costly for an entire organization. Cloud analytics is becoming more popular and a major concern for modern businesses. Cloud migration is rising to the top of many organizations’ to-do lists due to its promise of improved productivity and lower operating costs. Chances are, you’ll soon start your cloud migration journey.
The cloud revolution has forever changed the world of big data analytics and business intelligence ( BI). The cloud revolution has made it possible to gather and analyze data from many digital sources in one place. This data can then be shared across the entire organization. Everyone can now access the same trusted data from all the different data sources today.
Also read: Top 17 Applications of Cloud Computing
Key Elements of Cloud Analytics
Any combination of these elements can be used in cloud analytics. The majority of cloud analytic strategies that have been successful include all the elements listed above, which are supported by most cloud computing vendors today, such as Amazon Web Services (AWS), Google Cloud, and Microsoft Azure.
Three Types of Cloud Analytics
Three options are available for cloud computing and analytics. These are described below:
- Public Cloud: A public cloud is a form of cloud computing in which services are made available to the public through third parties. This includes storage capabilities, applications and virtual machines. These services are usually free, but users may have to pay for their use or consumption. This public cloud allows IT systems to be shared, while data is kept private. It also reduces costs for organizations.
- Private cloud: A private cloud is not available to all users. This cloud offers the same benefits as public clouds, including scalability, democratized access, and democratized scaling, but it is located in a data center that belongs to a single company or hosting service. This can offer greater data security and privacy, but it can also be more costly.
- Hybrid Cloud: Hybrid Cloud is the third option. It combines both of these options. A hybrid cloud structure allows organizations to use the public cloud to store non-sensitive data while keeping a smaller private cloud that is only accessible to them.
What are the Benefits of Cloud Analytics?
- Scalability: Cloud analytics allows companies to scale up their analysis and save money on data storage. Cloud computing allows you to add storage capacity and data analysis capacity as required to keep up with business changes. Cloud storage can be easily increased or decreased as the business grows. This is much easier than buying hardware. This allows businesses to respond to changing demands and adjust their analytics quickly to meet consumer requirements and take advantage of opportunities.
- Streamlined Operation: Cloud analytics is a great way for businesses to improve their business intelligence and uncover more insights that will help them make better business decisions. Users can ask data questions on-demand and with more flexibility thanks to agile processes. This allows key decision-makers to explore data and collaborate across teams. It also helps them strategically leverage data to gain a competitive advantage.
- Collaboration and Easy Access: Without cloud analysis, it can be hard to get a complete view of a company’s data. This limits the opportunities to collaborate from different locations. Cloud analytics can consolidate company data from different sources, and models can be easily updated in real time. Cloud-based workflows allow teams to collaborate more efficiently and easily, which is great news for multinational companies. Employees can collaborate and share files from anywhere with easier access to consolidated data.
- Security & Governance: Cloud analytics offers security and governance advantages. Cloud analytics gives you more control over data access and auditing capabilities. It also provides a single source of truth when it comes down to understanding the company’s data. Cloud storage of analytics can be useful for protecting information during natural disasters or other emergencies.
- Reduced costs: While in-house or on-premises solutions can quickly become expensive, cloud analytics doesn’t require any hardware, equipment, or continuous upgrades. This can lead to significant savings and allow you to have a more flexible budget through simple subscription models.
Also read: Top 10 Cloud Security Solutions for Businesses
Considerations for Deploying Cloud Analytics
Computing Power: The first step in implementing cloud analytics is to ensure that you need to have the necessary computing power to ingest, structure, and analyze large amounts of data.
- Data Sources: An effective cloud analytics solution must capture data from a variety of data sources including company websites, ERPs and CRMs, social media platforms as well as the Internet of Things, mobile applications, and many more.
- Data models: Data models can be used to transfer data from on-premises to the cloud. This will help to minimize disruption to the business. Cloud-based data modeling standardizes how data elements relate to each other and determines the data structure.
- Processing Applications: It is necessary to have applications that can handle large data sets from many systems. A data processing framework is required by organizations for cloud-based environments. These applications can be developed using Apache Spark, Google BigQuery, and Hadoop.
- Analytic Models: These models can be used to predict the future and provide advanced analytics functions that can run in the cloud.
- Data sharing: Cloud analytics solutions should allow for easy data storage and sharing through modern cloud architectures.
Consider these questions when you’re considering data analysis in the Cloud.
How do you use your cloud environment? You can use your current usage pattern to help you decide how to create a cloud environment that is scalable. You can scale up or down depending on your needs.
- What are your plans regarding data privacy and security Consider the security model that you will need for each layer of your network, including data storage, OS, and network.
- Are you able to plan for disaster recovery and high availability? What will your strategy look like?
- What location do you wish to house your solution?