Cloud Analytics is a service and delivery model that involves the deployment of cloud computing with analytics software to extract actionable insights by identifying the patterns in data. Cloud analytics is associated with Artificial Intelligence, Machine Learning, and Deep Learning. It is most commonly used across key industry applications to analyze and make sense of your business data. Whether you want to monitor production efficiency, analyze website traffic, track supply chain operations, review financial market figures, or capture real-time data from IoT sensors, cloud analytics has got you covered.
Let’s discover in detail what cloud data analytics is and why it matters. We will also explore how cloud-based data analytics work, the advantages of cloud analytics, and valuable tips on selecting the right platform in this insightful blogpost. Uncover the power of leveraging your data to extract valuable insights, make accurate predictions, and take proactive actions that were once mere aspirations.
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Data analytics has been around for a while, evolving with emerging technologies. As analytics moved to the cloud, it became Cloud Data Analytics, offering easier, more accessible, and capable data analysis on large datasets.
By 2028, the cloud analytics market is predicted to reach a staggering USD 86.15 Billion, growing at a compound annual growth rate (CAGR) of 20.9% during 2021-2028.
Vendors manage data analytics and business intelligence (BI) processes in cloud-based data analytics instead of relying on your company’s on-premise servers. This arrangement is often referred to as “analytics as a service.” The analytics vendor assumes responsibility for setup and maintenance, simplifying the process of extracting deep data insights. This approach offers scalability, improved performance, reliability, and cost savings.
Let’s break down the diagram above to help you understand how data is processed by cloud analytics into actionable insights and application events.
This wide range of analytics capabilities enables pattern recognition and the development of actionable insights, which can lead to increased efficiency, productivity, and profits. Additionally, top-tier tools offer seamless integration with other applications to initiate automated, data-driven events.
Cloud based analytics involves conducting data analysis and related processes (data integration, aggregation, storage, and reporting) fully or partially in the cloud. There are three distinct types of cloud analytics. Your choice among them will depend on your budget, business requirements, and compliance needs.
Public cloud in this category is referred to as application as a service, including virtual machines, storage, and data processing. These services are available to the general public and operate on a multi-tenant architecture, where IT systems are shared, but data remains isolated. This setup allows companies to reduce costs and streamline IT management.
A private cloud is a proprietary cloud that exclusively extends your organization’s existing IT infrastructure. Private clouds are implemented when data privacy and security are top priorities, although they typically have higher implementation costs.
Hybrid cloud combines the elements of both public and private clouds. These implementations enable organizations to enjoy the benefits of on-demand IT infrastructure for non-sensitive data while maintaining sensitive data within a private cloud environment.
We can help you build, deploy, scale, and maintain your analytics apps on the cloud to accelerate your time to value.
Any industry can benefit from data analytics to enhance performance and create value. Leveraging AI and analytics allows organizations of all sizes to make data-driven decisions quickly. The cloud facilitates experimentation and offers a rich software ecosystem for building AI applications and training deep learning models. It is the future of data analysis, offering businesses numerous benefits, including the following.
Here’s a list of top cloud-based analytics tools with various features and capabilities that make them suitable for a range of business needs:
From data warehousing and visualization to advanced analytics and machine learning – these modern cloud analytics tools come with a wide range of capabilities. You can consider the following options to find the one that best suits your business requirements for data analysis in the cloud.
When it comes to cloud based analytics tools, there are several popular types, each serving specific purposes as listed below:
These categories cover a wide spectrum of modern cloud analytics platforms, each tailored to meet different data analysis needs. Let’s understand how to choose the analytics platform that best aligns with your goals and data requirements.
Cloud-based data analytics platforms are designed to handle and analyze vast datasets, making them essential for businesses dealing with extensive datasets. These platforms typically employ machine learning algorithms to automate data analysis while also allowing users to apply their algorithms manually. Here are key factors to narrow down your choice for sustainable success:
While cloud analytics tools are often associated with larger companies with extensive databases, even smaller businesses can benefit significantly. Leveraging cloud analytics eliminates the need for in-house IT personnel and other staff members tasked with manual data analysis, freeing up time and resources for more critical business activities.
While cloud analytics solution providers often tout their solutions and services’ reliability, features, and capabilities, businesses remain vulnerable to challenges. Listed below are a few common pitfalls with their practical solutions to help you make the most of cloud analytics while minimizing potential disruptions.
Cloud analytics can face downtimes, so you must consider vendors with high uptime rates.
Solution: To ensure uninterrupted operations, consider a hybrid cloud strategy that combines on-premises and cloud analytics.
Data loss and leakage are concerns with cloud adoption.
Solution: Enhance data security by investing in employee training and certifications, reducing vulnerabilities.
Hiring and training for cloud analytics can be challenging due to ever-evolving technology.
Solution: Reduce skill gaps using automation tools and invest in skill development initiatives.
Cloud analytics costs can spiral if not monitored carefully.
Solution: Avoid cost overruns by implementing cost monitoring strategies and automation. Regularly review expenses for optimization.
Migrating outdated systems to the cloud is tedious, time-consuming, and risky.
Solution: Minimize migration risks by planning thoroughly, ensuring secure data backups, and seeking the guidance of a technology partner when needed.
Being an industry leader in data analytics services, our expertise lies in converting your data into BI dashboards and connecting any business data source to minimize reliance on manual data feeds. We place the utmost importance on your reporting needs by meticulously defining data sources, data flows, and the precise functions of each component.
Our team has extensive experience with popular BI platforms such as Power BI and Tableau. We can assist you in migrating your existing reports from Excel & Tableau to Power BI. Our profound understanding of cloud service platforms like AWS (Amazon Web Services) & Microsoft Azure allows us to harness the power of cloud analytics for data visualization. With us, you can be confident that your data analytics project is in capable hands.
Leverage our result-oriented expertise in AWS and Azure to implement data analytics in cloud and tap the full potential of your business data.
A: Cloud Analytics refers to a type of data analysis that shifts the elements of data analytics, such as data processing and storage operations, to a public or private cloud. Like on-premises data analytics, cloud analytics solutions help you identify patterns, make predictions, and derive actionable insights. It extends those capabilities to enable you to work with massive amounts of complex business data using algorithms and cloud technologies.
A: Data analytics relies on IT infrastructures to store, process, and model incoming data streams swiftly, while a cloud platform provides scalable storage, computing, and network bandwidth for big data apps. So, cloud and data analytics are two interconnected fields that complement each other. The key difference between them is that data analytics depends on cloud computing for data extraction, whereas cloud computing doesn’t depend on data analytics for anything.
A: Cloud Analytics Tool is a platform based in the cloud that allows the user to view analytics data. You can monitor data sources, data models, processing applications, computing resources, analytics models, and storage resources. These tools carry out a variety of data analytics use cases, such as streaming analytics, predictive analytics, cloud reporting, and dashboards into other applications and systems.