Cloud analytics 101: Key concepts and techniques explained

Every time you need to answer a business question, you're playing a waiting game. Your team submits a request, an analyst queues it up, builds a dashboard, and eventually—maybe days or even weeks later—you get an answer. But by then, the market has shifted, the opportunity has passed, or the problem has gotten worse. The bottleneck isn't your data or your people: it's the infrastructure holding both of them back.

Cloud analytics removes that bottleneck entirely. You get direct access to live data analysis without provisioning servers, managing infrastructure, or waiting in line for technical resources. The platform scales automatically with your workload and delivers real-time analytics at the speed of your curiosity, rather than your data team's capacity. 

In this guide, we'll walk you through the fundamentals you need to understand, from core concepts and architecture patterns to practical adoption challenges and platform capabilities that matter most.

What is cloud analytics (and why now)?

Cloud analytics is the practice of analyzing your data through cloud infrastructure without spinning up servers or maintaining hardware. You get scalable computing resources on demand and pay only for what you actually use.

The difference shows up in how fast teams can act. Your competitors aren't waiting days for reports anymore. They're making decisions in the moment, analyzing massive datasets instantly without the upfront costs or technical overhead of traditional systems. 

A recent analytics trends report confirms what you're already feeling: static, scheduled reports can't keep pace with the speed of modern business. Cloud analytics gives you live, interactive exploration of your data wherever it lives, turning analytics from a waiting game into an immediate conversation.

Where cloud analytics runs

Before you decide how to deploy your analytics, you’ll face one foundational choice: cloud or on-premises. This decision shapes everything,  from how much infrastructure you maintain to how fast your business can adapt.

Cloud vs. on-premises analytics

Cloud analytics gives you scalable resources that scale up or down with demand, along with faster access to new features and capabilities, and managed services that handle maintenance and updates. 

On-premises analytics offers full control of your infrastructure, predictable capital expenditures, and the assurance that your data never leaves your own environment.

Cloud analytics continues to gain momentum because businesses need to analyze more data without the delays that come with provisioning new hardware. When your competitors can spin up new analytics capabilities in hours instead of months, the speed advantage becomes impossible to ignore.

Public vs. private vs. hybrid cloud

Once you’ve chosen cloud, your next step is selecting the right deployment model. Each option balances cost, control, and security differently:

• Public cloud: You share infrastructure with other customers on platforms like AWS, Google Cloud, or Microsoft Azure. This offers the greatest scalability and cost-effectiveness.

• Private cloud: You get dedicated infrastructure that can be hosted in your own data center or managed by a third party. This provides more control and security, which regulated industries often require.

• Hybrid cloud: You combine both models, keeping sensitive data on private infrastructure while using public cloud for less-sensitive workloads.

Many organizations also adopt multi-cloud strategies, spreading workloads across several providers to reduce vendor lock-in and choose the best tool for each use case.

Cloud data architecture patterns

How you structure and store your data lays the foundations for what's possible with your analytics. Modern cloud architectures give you flexibility and performance that legacy systems simply couldn't deliver, letting you adapt quickly as your business needs evolve.

The major architecture choices for cloud analytics today include:

• Data warehouse: A centralized repository designed for structured, processed data that's optimized for fast querying and business intelligence (BI)

• Data lake: A storage system that holds raw data in its native format, ideal for large volumes of unstructured information, but can become disorganized without proper data governance

• Lakehouse: This newer approach combines the flexibility of a data lake with the management features of a data warehouse, letting you run BI and machine learning directly on raw data

This shift toward simpler, more unified architectures is already resonating with data leaders. As Chris D’Agostino noted in a recent discussion on top trends for data leaders:

"We're presenting the Lakehouse architecture, and the light bulb is going off. [Customers are] seeing that they can actually simplify the architecture. They can have fewer running systems. The more systems you have, the more complexity you have, the more DevOps support you need. If you can reduce that complexity, you can actually do more with data because you're reducing the number of copies."

💡Relevant read: Data lake vs data warehouse: 7 Key differences you should know

Key technologies for cloud analytics

Beyond core storage architectures, modern cloud analytics relies on several architectural innovations that make your data more accessible and powerful:

• Serverless computing and separated storage/compute: You can scale your processing power independently from your storage, paying only for the compute resources you actually use. This separation means you're not locked into fixed capacity, and your analytics can handle sudden spikes in demand without manual intervention.

• Streaming data ingestion: Services like AWS Kinesis and Google Cloud Pub/Sub let you analyze data as it arrives, rather than waiting for batch processing. This real-time capability means you can spot issues and opportunities the moment they happen.

• Zero-copy data sharing: Instead of duplicating datasets across teams and systems, modern platforms let you share live data without creating copies. This reduces storage costs, eliminates version conflicts, and ensures everyone works from the same source of truth.

• Vector stores and RAG for AI analytics: When you're building AI-powered analytics, vector databases store embeddings that let you perform semantic search across your data. Retrieval-augmented generation (RAG) combines these capabilities with large language models, grounding AI responses in your actual business data rather than generic training information.

How cloud analytics works

Moving from raw data to useful answers follows a clear path in the cloud. Here's how your data flows from collection to decision-making:

1. Ingest: Data is collected from various sources, either in scheduled batches or through live streams

2. Store: Raw information lands in scalable, cost-effective storage like a data lake or lakehouse

3. Model: Data gets cleaned, modeled, and prepared for analysis using modern ELT processes

4. Analyze: With platforms like ThoughtSpot, anyone can ask questions in natural language and get instant, accurate answers from cloud data.

5. Activate: Insights turned into action through embedded charts, automated alerts, or direct integration into the applications your team uses daily

Unlike traditional BI approaches that require you to wait for analysts to build reports, modern platforms achieve true data democratization by letting anyone explore data conversationally. You can type "show me sales by region this quarter" and get an immediate, interactive visualization. That user-friendly flexibility can deliver major benefits for your organization, which we’ll investigate next.

8 Benefits of cloud analytics that drive growth

As your organization grows, so does your data. But that data becomes a competitive asset only when it's accessible and actionable. Here's how modern cloud analytics solutions can help your organization scale intelligently and grow faster:

1. Building in elasticity as you scale

Cloud analytics scales with your business without the hardware costs and resource constraints of on-premises solutions. You pay for what you use, when you use it, supporting dynamic computing and storage needs as demand shifts.

2. Improve data sharing and collaboration

Data trapped in silos offers little business value. Cloud analytics connects your team to all your data, creating a single source of truth instead of conflicting spreadsheets across departments. With everyone working from the same information, transparency improves, and decisions move faster.

3. Utilize third-party data

Cloud analytics also makes it easy to bring in external data, like weather patterns, job reports, or social sentiment, to enrich your analysis. Mani Gopalakrishnan, VP of digital transformation at Kraft Heinz, put it well in Harvard Business Review:

"Data is going from being something that's guarded in a company to being a robust platform to being a service…Having information is powerful, for sure, but sharing information can be even more powerful."

4. Identify opportunities

Cloud analytics gives more users faster access to data, helping you pinpoint successes and eliminate problems. Modern solutions with AI analysts and augmented analytics go further by surfacing hidden opportunities and suggesting actions. Automated analytics offer cross-industry benefits that the speed and flexibility of the cloud make possible.

5. Control costs

Cloud analytics reveals costly inefficiencies across your business. You can identify accounts showing declining engagement before they churn, spot redundant software subscriptions across departments, or discover process bottlenecks that drain resources. Or you might find savings in unexpected places, like discovering one department onboards new hires in 10 days while others take 30, then applying those best practices company-wide to reduce time-to-productivity and associated costs.

6. Enhance product and service quality

Testing and improving products becomes easier with cloud analytics. Embedding analytics directly into your app can also strengthen engagement. MDaudit, for example, grew its user base by 50% in one year after embedding ThoughtSpot analytics into its SaaS product.

7. Improve customer experience

Cloud analytics helps you monitor and optimize the customer experience in real time. You can make meaningful improvements at every stage of the buyer's journey with increased personalization and engagement. As Ameet Shetty, Chief Data and Analytics Officer at Pilot Flying J, said on The Data Chief podcast: 

"How do you differentiate yourself? Well, you do it by knowing your clients better…That's what data does, driving those insights to help us create those connections so that we aren't another commodity."

8. Optimize sales, pricing, and packaging

Cloud analytics helps you identify buying patterns, customer behaviors, and how strategies affect purchasing decisions. This enables better pricing, packaging, and more effective marketing campaigns. By analyzing data across your entire business, you can understand how different areas contribute to overall growth and profitability, then optimize your go-to-market strategy accordingly.

Best practices for analytics in the cloud

Successful cloud analytics implementations share common patterns. Here's how to build a foundation that scales securely, performs reliably, and stays within budget.

1. Governance and security

Strong data governance from day one means setting clear rules about who can access what data and how sensitive information gets protected. Think: role-based access controls, encryption, and clear processes for handling personal information. When you can trace every insight back to its source, you avoid security issues and compliance problems down the road.

2. FinOps: Managing cloud costs effectively

Cloud resources scale automatically, and so can your bills. The fix is proactive cost management. Tag your resources so you know which department or project is spending what, set up budget alerts before costs surprise you, and regularly review where you can scale down overprovisioned resources. If you have predictable workloads, reserved instances, or committed use discounts can cut costs compared to on-demand pricing.

3. Performance optimization

Cloud-native performance starts with choosing the right data structure for your warehouse and understanding how your platform handles queries. The biggest decision you'll face is when to use live queries versus cached data. Live queries give you real-time accuracy but cost more, while cached extracts save money for data that doesn't change often. Match your approach to how your teams actually use each dataset.

4. Reliability and monitoring

Set clear expectations for your analytics workloads through service level agreements (SLAs), then configure autoscaling to handle demand spikes automatically. Monitor query performance and system health so you can spot issues before they affect your users.

What to look for in a cloud analytics platform

Cloud analytics isn't a single product category, but an ecosystem of specialized tools that work together. Understanding where each type fits helps you build a stack that actually serves your business needs.

Categories of cloud analytics solutions

• BI and AI analytics platforms let your teams explore data and surface insights. Look for search-driven interfaces, interactive dashboards that update automatically, and AI that proactively flags anomalies or opportunities worth investigating.

• Data platforms like cloud data warehouses (Snowflake, BigQuery, Redshift) and lakehouses (Databricks) provide the foundation where your data lives. These handle storage, compute, and query processing at scale.

• Streaming and real-time solutions ingest and process data as it arrives, enabling immediate analysis of events like transactions, clickstreams, or sensor readings.

• Data integration and transformation tools move data between systems and prepare it for analysis. Modern ELT approaches and transformation frameworks like dbt let you model data directly in your warehouse.

• Embedded analytics puts insights directly into the applications your teams already use—CRM systems, operational dashboards, customer-facing portals—so your team doesn’t have to context-switch to find answers.

Your evaluation checklist for cloud analytics platforms

When you're comparing solutions, looking at these capabilities will help determine whether a platform is fit for purpose within your organization:

• Governance and PII controls: Can you enforce role-based access, mask sensitive data, and maintain audit trails? Regulated industries need granular control over who sees what.

• Data freshness and latency: How current is the data your teams analyze? Live queries give you real-time accuracy; extracts introduce delays that can range from minutes to days.

• Concurrency and scale: Will the platform handle hundreds of users running queries simultaneously, or will performance degrade during peak usage?

• Live versus extract: Does the platform query your cloud data warehouse directly, or does it require copying data into its own storage layer? Live connectivity eliminates data duplication and keeps insights current.

• Lineage and observability: Can you trace every metric back to its source data and transformation logic? This matters for trust and troubleshooting.

• Interoperability with cloud data platforms: Does it connect natively to your existing warehouse or lakehouse, or will you need more extensive integration work?

• Alerts and activation: Can insights trigger automated actions such as notifications, workflow updates, or API calls, so your team can respond immediately?

• Total cost of ownership: Look beyond license fees to understand compute costs, data egress charges, and whether you'll need reserved capacity commitments. Apply FinOps principles to track spending by team or project and optimize resource allocation over time.

• Cost allocation: Can you attribute analytics spending to specific departments or initiatives? This visibility helps you justify investments and identify optimization opportunities.

Where ThoughtSpot fits

ThoughtSpot's Agentic Analytics Platform connects live to your cloud data through platforms like Snowflake, Databricks, and BigQuery. Anyone can ask questions in natural language through Spotter and get immediate, trustworthy insights without waiting for analysts to build dashboards, while interactive Liveboards update automatically as new data arrives.

Beyond exploration, ThoughtSpot operationalizes insights through automated alerts when metrics cross thresholds and embedded analytics that put these capabilities directly into your existing applications and workflows. When you reach people where they're already working and give them the tools to do their jobs more effectively, you're planting the seeds of a data-first culture.

Top use cases for cloud analytics

Cloud analytics changes how every role in your company works with data. When it comes to the power of micro-decisions, Vijay Kotu puts it best: 

"The purpose of analytics is to help us make decisions, is to create decision-making capabilities across the company...these are not like once in a quarter, boardroom decisions. I'm talking about everyday decisions that many of our colleagues make every single day." 

That idea captures what modern cloud analytics delivers: data that fuels better, faster decisions at every level of the business.

• For your executive team: Interactive Liveboards deliver live KPI visibility that refreshes as new data arrives. Spot emerging trends and address issues in real time rather than discovering them weeks later in static monthly reports.

• For your product managers: Dive into feature adoption patterns, user behavior flows, and conversion funnels to prioritize what actually moves the needle. Embedded analytics brings these insights directly into the tools where roadmap decisions happen.

• For your marketing team: Go deeper than surface-level campaign metrics. Understand the full customer journey—from lifetime value and cross-channel attribution to how each touchpoint influences conversion.

• For your operations team: Leverage streaming data and predictive models to stay ahead of demand shifts, optimize inventory levels, and eliminate supply chain bottlenecks before they impact your business.

Put your cloud analytics strategy into action

The shift to cloud analytics represents an opportunity to fundamentally change how your organization works with data. Modern platforms eliminate the traditional bottlenecks between questions and answers, so your team members are actively exploring data through conversational AI rather than passively consuming reports.

Ready to experience what's possible with your cloud data? Start your free trial today and see how conversational analytics can change the way your whole team makes decisions.

Cloud Analytics FAQs

What do "analytics in the cloud" and "cloud and analytics" actually mean?

Both phrases refer to the same thing: cloud analytics (also called cloud-based analytics). This means you use cloud infrastructure to analyze your data without managing your own servers. For example, you might run analytics across your CRM and product event data simultaneously, using zero-copy sharing to query everything in place without moving or duplicating datasets.

What are the main differences between public, private, and hybrid cloud analytics?

Public cloud uses shared infrastructure from providers like AWS or Google Cloud, and it tends to offer the best scalability and cost-effectiveness. Private cloud provides dedicated infrastructure for greater security and control. A hybrid cloud combines both approaches to balance cost, security, and performance needs.

How does a lakehouse differ from a traditional data warehouse?

A data warehouse stores structured, processed data optimized for specific queries. A lakehouse, by contrast, combines the raw data storage capabilities of a data lake with the management and performance features of a warehouse. This lets you run analytics directly on unprocessed data. 

What's the best way to control costs when using cloud analytics?

Start with resource tagging to track spending by department or project. Set up budgets and alerts to monitor usage before costs surprise you, and commit to regular resource allocation reviews to eliminate waste. For predictable workloads, consider reserved instances or committed use discounts to reduce your overall spend.

When should I use live queries versus data extracts in cloud analytics?

Live queries are your best choice when you need current information for real-time decisions, especially if your cloud data warehouse can handle the query volume. For data that doesn't change often, extracts offer a cost-effective alternative. Keep in mind that extracts introduce delays between data updates and analysis, which can lead to decisions based on outdated information.