Cloud Analytics

9 min readNov 23, 2022

What Is Cloud Analytics?

Cloud analytics refers to the manipulation and analysis of data that happens in the cloud instead of locally, in an on-premises business system. Analytics systems hosted in the cloud empower users to access, aggregate, analyze and utilize data. With these platforms, users can work with large data sets, identify trends and pinpoint areas for improvement across the organization.

While your laptop’s spreadsheet program or on-premises analytics may be sufficient for a list of a few thousand data points, those needing to analyze complex data sets made of tens of thousands to millions of inputs won’t find much success using such a program. Cloud analytics allow companies to process large data sets in a scalable, more affordable means than building infrastructure to handle the process on-site. That’s just one reason to look to the power of the cloud for your data analytics needs.

Cloud Types

While we may imagine the cloud as an intangible entity, it’s actually a term for large computer networks hosted in one or more data centers. Depending on your security, performance and access needs and goals, among other considerations, one of the types of cloud models below may make sense for your analytics platform.

Public Cloud

A public cloud is a platform offered by third-party providers over the public internet, making them available to anyone who wants to use or purchase them. It uses a standard cloud computing model to make resources, including virtual machines, applications and storage, available to remote users. The most common public cloud resources you may use regularly are websites, shopping apps, or software as a service (SaaS) applications that handle email, calendars, project management, or even your bank accounts.

Private Cloud

A private cloud is an on-demand computing system only accessible inside your private network. Private clouds are not shared with any other organization. They work similarly to your company’s intranet and other resources you can only use when you’re in the office or connected with a virtual private network (VPN). All clouds become private clouds when the underlying IT infrastructure is dedicated to a single customer with completely isolated access. Private clouds provide increased infrastructural capacity to handle large data storage, on-demand services and increased visibility into resources across the infrastructure.

These resources come with additional security, though, there may be more costs to set up and maintain the networks and applications than services in a public cloud.

Hybrid Cloud

Hybrid cloud is a solution that combines a private cloud with one or more public cloud services. It uses proprietary software to enable communication between each distinct service. A hybrid cloud strategy can connect multiple computers through a network, consolidate IT resources, move workloads between environments and incorporate a single, unified management tool.

Cloud Analytics Explained

Cloud analytics software takes large databases and other data sets and the information into useful summaries and insights — sometimes via visualizations or charts — that inform business decisions.

Some cloud analytics software is focused on just one type of data, like social media interaction or website usage. Others are broad and work with a number of data sets, no matter how disparate, to provide a unified picture of how your business is operating.

When you use the cloud, your team can capitalize on powerful computing resources in remote data centers, allowing you to handle larger tasks that may have required a supercomputer not long ago. Once the data is imported, employees and others can log in via their computer, smartphone or tablet to view and work with the results.

How Cloud Analytics Work

As the name suggests, cloud analytics systems must be hosted on an internet platform. In most cases, they are run on state-of-the-art data centers. Data centers are physical locations with servers that can compute and store information as well as connect via networks with other centers. Data centers can provide the processing power and storage space needed for analyzing massive amounts of data. And for these, remote centers usually require massive amounts of infrastructure and equipment to process all of the data that’s sent to it. While many companies have the resources to build and manage their own data centers, for most it’s too expensive. That’s where cloud computing comes in.

In cloud analytics systems, all generated data is collected and securely stored in the cloud, where it can be accessed from any internet-connected device. The cloud analytics system can then clean, organize, process, and analyze the data using proprietary algorithms. These insights are presented to the user through different data visualizations and other intuitive formats, which helps your team to reach new productivity levels while finding improved and more accurate results. It offers a win-win for most businesses, making data processing affordable and accessible.

Each cloud analytics solution comes with its own particular set of features, but all solutions have several common components.

Data sources: These are the various sources from which your business data originates. Common examples include web usage and social media data, as well as data from CRM and ERP systems.
Data models: A data model structure retrieves data and standardizes how data points relate to each other for analysis. Models can be simple — using data from a single column of a spreadsheet, for example — or complex, involving several triggers and parameters, in multiple dimensions.
Processing applications: Cloud analytics uses special applications to process huge volumes of information stored in a data warehouse and reduce time to insight (more on this below).
Computing power: Cloud analytics requires sufficient computing power to intake, clean, structure and analyze large volumes of data.
Analytic models: These are mathematical models that can be used to analyze complex data sets and predict outcomes.
Data sharing and storage: Cloud analytics solutions offer data warehousing as a service so that the business can scale quickly and easily.

In addition to these features, AI is becoming a more integral part of cloud analytics. Machine learning algorithms, in particular, enable cloud analytics systems to learn on their own and more accurately predict future outcomes.

Major benefits of cloud analytics over other solutions are additional sharing and collaboration, improved security, lower costs and tremendous scalability. With the information learned from analytics, companies can gather metrics that will give greater insight into how operations, marketing, finances, and other departments are functioning as a whole, allowing for better decision-making.

How Businesses Use Cloud Analytics

Just as there are a nearly infinite number of ways to run a business, there are infinite ways to use cloud analytics. For example, a digital music streaming company would have different cloud analytics needs than a retailer.

In most cases, the ultimate goal of cloud analytics is to improve profitability, but businesses could have more targeted needs like driving more profitability from existing customers or increasing international sales. Reports and visualization tools can shed light on areas where a company is over performing or underperforming to shape larger strategies. Small businesses and large enterprises alike can use cloud analytics to comb through troves of customer and business data for key insights.

Companies that rely on a large sales team, for example, could use cloud analytics to better understand how one region outperforms another. You might use analytics to find areas where you’re dominant and may succeed by doubling-down or regions where competitors are making it tough to land deals. A social media influencer might discover trends in the types of content that garners the most likes. Or a genetics scientist might use cloud analytics to sequence the millions of nucleotides that make up a genome. These are just a few examples of a nearly endless list of potential use cases.

Why Use Cloud Analytics?

Cloud analytics have major benefits compared with traditional methods like spreadsheets and other desktop- or on-premises solutions. The advantages of cloud analytics make it an obvious choice.

Advantages of Cloud Analytics

Here are seven advantages of cloud analytics that all business owners and managers should consider when choosing an analytics solution.

Growth and Scalability: Cloud platforms offer powerful capabilities on-demand with near-limitless flexibility. These resources are available as needed for scaling up or scaling down, without any need to purchase, set up or maintain any of your own servers or other resources as your needs shift with growth.
Integrated access from anywhere: When your finance, IT, marketing and sales teams all manage their own database and use different analysis tools, workers may need to be trained on multiple systems. As a result, they waste time looking for data from disparate systems or need to be trained on multiple solutions. Cloud analytics pulled from your company’s ERP system gives your entire workforce a single source of information and analytics, no matter where they’re working from.
Break Down Silos: When your entire staff uses one system, it facilitates collaboration between different departments. Even though users only get access to the information they need, teams can more easily communicate across departments to find more useful and valuable insights.

Cost Reduction: The costs of housing and maintaining any on-premises systems include IT headcount, hardware and development efforts — cloud analytics means just one bill and, in many cases, lower total costs.
Find Answers Faster: The more powerful servers available through cloud computing, as opposed to on-premises systems or employee laptops, allows for faster data processing.
Better Sharing and Collaboration: The cloud is inherently better for sharing. Instead of email attachments, network drives and confusion about multiple versions of files, workers can work from the same data sets and share reports in a few clicks.
Data Integration: Big data produced from numerous, disparate sources across the organization makes it nearly impossible to get a unified view. Cloud analytics brings all of a company’s data sources together to produce a more complete picture. All stakeholders, regardless of their physical location (or the data’s location), can easily access this data in one place, to gain more accurate insights and make better business decisions in real time.
Enhanced Security: With most cloud analytics, data is regularly backed up to servers in multiple locations so it’s protected during a fire or natural disaster. Since no information is stored locally, there are no local hard drives to steal, and sensitive data is not shared through insecure methods like email or flash drives. All data is password-protected, users only have access to what they need, and audit logs provide visibility into who accessed what and when and what they did with it.

What Are Cloud Analytics Tools?

There are several types of cloud analytics tools. Many of these can be easily accessed through your web browser. Here are some examples of a few popular types of cloud analytics tools:

Website Analytics: One of the most common types of cloud analytics is website traffic analytics. These cloud analytics tools help you understand a website’s traffic, conversion rate, bounce rate and more so you can make adjustments that improve the user experience, boosting revenue and profitability.
Sales Analytics: Sales analytics platforms help you manage customers, leads, evaluate sales across geographies, and monitor the performance of your sales team. This information can reveal important trends or signals that help leaders develop more effective sales strategies.
Financial Analytics: Financial analytics go beyond financial statements to draw out revenue and expense trends and details in your financial results that would be impossible to find without a large team of financial analysts.
Performance Analytics: Performance analytics look at sales, production or other data to find bottlenecks, sources of expenses and improvement opportunities.

Among any of these categories of tools, you may find more specialized software and varying features. Basic tools may simply summarize and help you understand data, while more advanced tools leverage technology like machine learning and artificial intelligence (AI) to analyze large volumes of data and make predictions based on all of that information.

Key Takeaways

Cloud analytics offer access to more processing power for analyzing large data sets than programs that run on local servers or your computer.
Cloud analytics software is accessible to anyone in your business with the right credentials and kept safe with strong security protocols.
Advanced cloud analytics tools include AI algorithms that help you understand trends and opportunities in very big data sets.

Capabilities of Cloud Analytics Tools

When shopping for a cloud analytics platform, look for these common features that can boost your productivity and results:

Data Sources: Link the solution to one or multiple data sources with automatic refreshing to always have the latest results.
Data Models: Look for solutions that include data models to match your company’s data sets, such as sales orders, customers, items, etc.
Processing Applications: Work with your data and alter it to uncover unexpected insights, novel uses and patterns.
Data Sharing: Easily share information between team members to optimize productivity and efficiency across the company.
Data Storage: Think about both what your company will need now and in the future as you consider the data storage limits from vendors.
Analytics Models: Tap into prebuilt analytics models designed for different use cases, or build your own.
Reporting: Create accurate, detailed reports that you can confidently bring to the C-suite, board of directors, lenders or investors.