What Is Cloud Based Analytics? How It Works + Benefits & Examples

Key Takeaways

• Cloud based analytics processes and analyzes data using remote servers instead of local hardware, offering scalable computing power and storage accessible from anywhere 
• Benefits of cloud analytics include significant cost savings, improved collaboration, automatic scaling, and faster deployment compared to on-premises solutions 
• Modern cloud analytics tools like Google Cloud, Amazon Web Services, and specialized platforms provide actionable insights for better business decisions

Building client reporting capabilities in-house seems logical until you realize the hidden costs of development time, maintenance, security headaches, and frustrated clients tired of calling support every time they want a new chart.

Cloud based analytics solves this chaos by moving data processing and analysis to remote servers. You get scalable, accessible analytics that work from anywhere, while your clients get self-service dashboards they can use. 

This guide shows you how cloud analytics platforms work, what features matter most, and how to choose solutions that morph frustrated customers into self-sufficient power users who actually enjoy working with their data.

What is Cloud-Based Analytics?

Cloud-based analytics uses remote servers and cloud computing resources to process, analyze, and visualize data instead of relying on local hardware and software.

Unlike traditional on-premises systems where you own and maintain physical servers, cloud analytics leverages the infrastructure of cloud service providers like Amazon Web Services, Google Cloud, or Microsoft Azure. 

How Cloud Analytics Works

Cloud analytics platforms operate through a distributed architecture that separates data storage, processing, and visualization across multiple cloud environments.

The process starts when you connect your disparate data sources e.g social media platforms, CRM systems, IoT devices to the cloud platform. The system ingests this real-time data into a centralized cloud data platform designed for analytics processes.

Modern cloud analytics architecture has evolved significantly. David Abramson, CTO at Qrvey, explains this shift:

“One of the most significant changes is an architectural shift from AWS serverless functions to a containerized (Kubernetes) model. This gives customers unparalleled control, flexibility, and portability in how they deploy our platform.”

Cloud Analytics Architecture Components:

• Data ingestion layer: Collects information from hundreds of sources automatically 
• Storage layer: Uses cloud storage optimized for analytics operations
• Processing layer: Applies machine learning and artificial intelligence for analysis 
• Visualization layer: Presents real-time insights through interactive dashboards 
• Security layer: Implements access controls and governance controls across all levels

The containerized approach means your analytics platform runs consistently across different cloud environments, giving you the freedom to deploy on AWS, Azure, or hybrid cloud analytics models without being locked into a single cloud provider.

Key Aspects of Cloud-Based Analytics

What makes cloud analytics different from traditional business intelligence approaches? Several critical aspects define modern cloud-based analytics solutions.

Smart Data Storage & Fast Processing

Cloud analytics platforms use distributed storage systems that can handle massive datasets while maintaining query performance. Unlike on-premises data centers with fixed capacity, cloud storage scales automatically based on your needs.

Accessible from Anywhere

Business users access reports during client meetings on their phones, check key performance indicators while traveling, and collaborate on dashboards from home offices. This flexibility increases productivity.

Real-Time Insights Capability

Modern cloud analytics tools process streaming data as it arrives, enabling immediate responses to changing customer behavior or business trends. 

Say a customer abandons their shopping cart, cloud analytics tools instantly detect this behavior and trigger automated email campaigns within minutes. Instead of discovering lost sales days later through weekly reports, your system responds immediately to recover revenue while the customer’s intent is still fresh.

Scales with Your Business

You never worry about running out of raw computing power or storage space. This also applies even if you’re analyzing thousands or millions of records. Cloud computing automatically allocates more computing power during peak usage. 

Security and Compliance Framework

Cloud analytics providers implement enterprise-grade security with levels of security often exceeding what most companies can achieve with on-premises solutions. 

Regular automatic updates ensure you stay protected against emerging threats, while white-label analytics solutions maintain your brand identity throughout the entire user experience.

Types of Cloud Analytics

Cloud analytics comprise several distinct approaches, each serving different business use cases and analytical operations.

Descriptive Analytics

This foundational type answers “what happened” by analyzing historical data patterns. Customer-facing dashboards show past performance, conversion rates, and satisfaction metrics that help users understand their business trends through self-service tools.

Predictive Analytics

Using machine learning algorithms, predictive analytics forecasts future outcomes based on current data trends. SaaS applications embed these capabilities so customers can predict churn risk, sales performance, or resource needs without requiring cloud-native expertise.

Prescriptive Analytics

The most advanced type recommends specific actions by combining artificial intelligence with mathematical models. These analytics models don’t just predict what might happen but suggest what customers should do, making enterprise-grade insights accessible to everyday users.

Real-Time Analytics

Processes data as it streams in, enabling immediate responses to changing conditions. Unlike traditional embedded solutions, modern real-time analytics provides instant alerts and live dashboards that keep customers informed of critical business changes as they happen.

Examples of Cloud Analytics Architecture

Real-world cloud analytics architecture varies significantly based on organizational needs and existing technology infrastructure.

• A typical e-commerce company might connect their website data, customer experience platforms, and inventory systems to Google BigQuery for centralized analysis. 

The cloud data analytics pipeline automatically processes transaction data, customer insights, and supply chain information to generate actionable insights.

• Financial services teams often use hybrid analytics solutions, storing sensitive data in private clouds and handling non-sensitive tasks in public clouds. 

With Qrvey’s cloud-native solution deployed directly to AWS, you get built-in security, automatic scaling, and powerful analytics without the hassle of managing servers or making big upfront investments.

• Manufacturing companies leverage cloud analytics systems to monitor equipment performance, predict maintenance needs, and optimize production schedules. 
• Real-time data from sensors flows into cloud analytics platforms that use deep learning models to identify potential issues before they cause daily operations disruptions.
• SaaS companies face unique challenges when building customer-facing analytics. 

Instead of creating complex data sets and visualization tools from scratch, many turn to embedded cloud-native analytics platforms that provide ready-made solutions for multi-tenant environments.

This eliminates months of development work while ensuring scalable performance.

Features and Capabilities of Cloud-Based Analytics

Cloud-based analytics tools offer comprehensive capabilities that would be expensive and time-consuming to build internally.

Multi-Tenant Analytics Support

Cloud analytics platforms designed for SaaS applications provide tenant-level data isolation and customization. 

Each customer sees only their data while sharing the underlying infrastructure, creating an efficient alternative to building separate systems for each client.

Advanced Analytics and Machine Learning

Built-in AI-powered analytics capabilities include natural language query processing, automated insight generation, and predictive modeling. 

Business users can ask questions in plain English rather than writing complex queries.

Comprehensive Data Integration

Cloud-based analytics solutions connect to disparate data sources through hundreds of sources including SQL databases, NoSQL stores, cloud data warehouses, and real-time streams. 

This integration capability eliminates the manual processes traditionally required for data preparation.

Self-Service Dashboard Creation

Companies using enterprise analytics platforms quickly realize the power of self-service. With Qrvey’s intuitive drag-and-drop dashboard creator, business users can mix data sources, apply formulas and trends, and securely access tenant-specific data without IT. 

It’s true self-service analytics that empowers faster, smarter decisions. 

See how it works or explore our enterprise platform built for scale and flexibility.

Benefits of Cloud Based Analytics

Why are organizations rapidly migrating from on-premises analytics solutions to cloud-based alternatives? The advantages extend far beyond simple cost savings.

Dramatic Cost Savings

Cloud analytics eliminates the need for dedicated servers, on-premises data centers, and specialized software licenses. Instead of large upfront investments, you pay for actual usage through predictable subscription models.

Traditional business intelligence implementations can cost hundreds of thousands of dollars before processing a single report. 

Cloud-based solutions often deliver similar capabilities at 50-70% lower total cost of ownership.

Faster Time to Value

Cloud analytics platforms deploy in days or weeks rather than months. Pre-built connectors, templates, and automated processes accelerate implementation while reducing the risk of project delays.

Unlimited Scalability

Cloud computing automatically scales resources up or down based on demand. During month-end reporting cycles or peak usage periods, cloud analytics tools provision additional computing power without manual intervention.

Automatic Updates and Maintenance

Cloud service providers handle software updates, security patches, and infrastructure maintenance. Your team focuses on analyzing data rather than managing servers and premises systems.

Improved Security

Multi-tenant analytics platforms like Qrvey’s offer strong security features like encryption, access controls, and audit trails that many internal systems can’t match. 

In fact, self-service analytics improves security by limiting access and automating governance. For SaaS teams, embedding analytics in the cloud is often safer than building it all in-house.

Still thinking of building it yourself? Use our free ROI calculator to see the hidden costs and how fast a cloud-based solution pays for itself.

Challenges & Considerations

What obstacles do organizations face when implementing cloud-based analytics solutions? Understanding these challenges helps you prepare for a successful deployment.

Data Security and Privacy Concerns

Moving sensitive information to cloud environments raises questions about data protection and regulatory compliance. Financial services and healthcare organizations must ensure their cloud analytics provider meets strict industry standards.

Internet Connectivity Dependencies

Cloud analytics requires reliable internet access for optimal performance. Organizations in areas with limited resources or unstable connectivity may experience slower query response times during network disruptions.

Vendor Lock-in Risks

Some cloud analytics platforms use proprietary formats that make migration difficult. Choosing solutions with open standards and export capabilities protects your long-term flexibility.

Note: Can’t decide which analytics solution is best for your use case? First understand the fundamental differences between embedded analytics vs business intelligence to avoid costly implementation mistakes 

Differences Between Cloud Analytics and Data Analytics

While often used interchangeably, cloud analytics and data analytics are not the same. One is defined by where it’s done, the other by what is done. 

Aspect	Cloud Analytics	Traditional Data Analytics
Infrastructure	Cloud service providers handle servers and maintenance	On-premises servers require internal IT management
Scalability	Automatic scaling based on demand	Fixed capacity requiring hardware upgrades
Cost Structure	Pay-per-use subscription models	Large upfront investments in dedicated servers
Deployment Time	Days to weeks implementation	Months for full on-premises setup
Accessibility	Available from anywhere with internet	Limited to office networks or VPN access
Maintenance	Automatic updates by cloud provider	Manual patching and system maintenance
Collaboration	Built-in sharing and real-time collaboration	Email-based report distribution
Security	Enterprise-grade security by default	Requires internal security expertise

Cloud Based Analytics Tools and Platforms

The cloud analytics world includes dozens of specialized platforms, each optimized for different use cases and business requirements.

Qrvey – Embedded Analytics for SaaS

Qrvey is purpose-built for SaaS companies that need to embed analytics directly into their applications. Unlike traditional BI tools, Qrvey supports multi-tenant use cases, so each customer sees only their own data securely.

With a container-based architecture running on Kubernetes, Qrvey works across AWS, Azure, or hybrid cloud setups. You won’t be locked into one provider.

The dashboards are white-labeled to look and feel like your product; your users won’t even know it’s powered by Qrvey. 

And with self-service tools, they can build custom reports without needing IT.

Want to stop chasing analytics feature requests? Schedule a demo and see how Qrvey helps you move faster on any cloud.

Google Cloud

Google BigQuery and Google Cloud Dataflow offer enterprise-scale data warehousing and real-time stream processing. 

The platform excels at handling massive datasets with machine learning integration for advanced analytics.

Amazon Web Services

Amazon Redshift, AWS Glue, and Amazon QuickSight provide a comprehensive cloud analytics ecosystem. Organizations already using AWS analytics platforms find the service model integrates well with existing infrastructure.

But if you need stronger multi-tenant features, a SaaS analytics platform like Qrvey may be a better fit than general cloud tools.

Azure analytics platforms are a good choice for Microsoft-focused teams, thanks to easy Active Directory integration and existing enterprise agreements.

How to Choose the Right Cloud Analytics Platform

What factors should guide your cloud analytics platform selection? Use this checklist to match your needs with the right tools.

Assess Your Analytics Use Cases

Self-service analytics for business users requires different capabilities than embedded analytics for customer-facing applications. Define whether you need internal business intelligence or customer analytics before evaluating platforms.

Evaluate Integration Capabilities

Your chosen platform must connect seamlessly to existing data sources and applications. Look for pre-built connectors to your current cloud data warehouses, databases, and business apps. Qrvey takes the complexity out of data management with a unified data lake built for analytics. 

You can easily consolidate multiple pipelines, ingest all data types, sync or connect live data, and apply built-in transformations to speed up processing.

Consider Scalability Requirements

Startups and large enterprises need different analytics tools. Choose a platform that can grow with you without needing a full rebuild later. Think about whether you need self-service tools for internal teams or embedded analytics for customer-facing apps.

Review Security and Compliance Features

Make sure your cloud analytics solution follows the rules for your industry and keeps sensitive data safe. Some companies may need simple tools that grow with them, while others need advanced options like embedded analytics for member platforms or IoT analytics for connected devices.

Embed. Analyze. Automate. Start with Qrvey Today

For SaaS companies, embedded analytics IS the next evolution in customer experience. Your users get the contextual insights they need without leaving your application, while you avoid the technical debt of building analytics features from scratch.

Qrvey makes it easy to add analytics to your product without worrying about data security, scalability, or supporting multiple customers. Our flexible cloud setup works across platforms and adjusts to fit whatever stage you’re at, new build or enhancement.

Your users expect more. Give them embedded dashboards that fit your UI, work instantly, and keep up with demand.

After switching to Qrvey, JobNimbus reached 70% user adoption among enterprise clients without months of custom dev. See how Qrvey can do the same for you