What is Cloud Analytics?

What it means, why it matters, and how it works. This guide provides definitions and practical advice to help you understand modern cloud analytics.

Image of cloud computing platform featuring diverse cloud services. Data processing by cloud analytics for actionable insights and application events.

What is Cloud Analytics?

Cloud analytics is a service model in which data analytics and business intelligence (BI) processes take place on vendor-managed infrastructure rather than a company’s on-premise servers.

Because the analytics vendor or third-party partner typically manages setup and maintenance, cloud data analytics makes it easy for you to empower all employees with deep data insights through scalability, performance, reliability and cost savings.

Hybrid cloud analytics services allow you to still enjoy the benefits of cloud analytics even if you can’t move all your data to the cloud due to data sovereignty requirements or strict governance rules. Hybrid cloud services extend SaaS analytics capabilities to wherever your data must reside, whether that’s on-premises, in a virtual private cloud, or a public cloud.

Cloud Analytics Benefits

Now more than ever, businesses are choosing SaaS for modern analytics. That’s because today’s vastly increased appetite for data-informed agility, together with more distributed working practices, is demanding a shift away from on-prem solutions. And going forward, cloud computing for big data analytics will underpin the rapid innovation, collaborative analysis, and real-time insights that will characterize the next generation of decision-making.

Let’s dig deeper on the reasons why companies are moving their analytics processes to the cloud:

  • Lower Costs and Faster ROI: With cloud based analytics, you’ll reduce the risk that comes with making upfront hardware investments for on-premise servers. In addition, because you won’t need in-house expertise to maintain servers and software, you’ll cut even more costs.

  • Power Remote and Hybrid Work: The workplace has changed, and your distributed workforce, as well as your suppliers and partners, need immediate, governed access to fully interactive analytics from anywhere and on any device.

  • Easy Collaboration: The best cloud analytics platforms allow you to take snapshots of analytics, add commentary for improved context, and tag in the discussions. This creates a collaborative canvas for both real-time and asynchronous decision making.

  • Up-to-Date Capabilities: Updates and upgrades are performed automatically in cloud data analytics solutions. That means organizations save costs in the long run, as they don’t have to worry about ongoing maintenance associated with on-premise servers.

  • Flexible Performance: Rather than purchasing new hardware as your data needs change, cloud data analytics gives you the ability to turn services on and off as needed. For example, if you have a spike in data, you can quickly scale up your services, and then scale back down again when there’s less activity.

  • Reliability and Security: SaaS environments lower data security risk. First, the likelihood of error is far lower when servers aren’t manually configured. And second, SaaS security certifications require providers to meet stringent standards.

  • Centralized Data: When data is scattered and siloed across your CMS, ERP, marketing automation and countless other systems, it’s hard to get a complete view of your business. A cloud data analytics solution brings all of this data together to bring a complete picture of your business and maximize insights for everyone across the organization.

How Cloud Analytics Works

In cloud based analytics, data analytics and BI processes take place on vendor-managed infrastructure rather than your company’s on-premise servers. This is why it is sometimes referred to as analytics as a service. The analytics vendor typically manages setup and maintenance, which makes it easier for you to gain deep data insights while also having scalability, performance, reliability and cost savings.

Diagram showing how data is processed by cloud analytics into actionable insights and application events

Let’s walk through the diagram above.

  • Data comes from both cloud and on-premises sources and applications. The best cloud analytics platforms can manage hybrid data delivery and application automation. Examples of data sources include transactional, website usage, social media, and CRM data.

  • Data is stored in a cloud data warehouse from a vendor such as Amazon Redshift, Google BigQuery, Microsoft Azure, or Snowflake.

  • The cloud analytics tool uses this data to let you perform a variety of analytics use cases such as creating visualizations, dashboards and cloud reporting. The best tools go further by enabling you to perform augmented analytics and predictive analytics, machine learning or AutoML (automated machine learning), embed analytics into other applications, and trigger alerts and actions in other systems.

  • This array of analytics capabilities helps you to identify patterns and develop insights that lead to actions which can increase efficiency, revenue and profits. Top tools can also integrate with other applications to trigger automated, data-driven events.

Which Cloud Analytics Platform is Right For You?

Learn the top 10 things to look for so you can choose one that fits your data requirements, business needs, and IT ecosystem.

Selecting a Cloud Analytics Platform

Not all cloud based analytics tools are created equal. It’s important to choose the one that best fits your data requirements, business needs, and IT ecosystem. Here are the 10 things to look for in a cloud based analytics solution.

  1. Truly cloud-based
    Many platforms claim to be cloud – and then require local software for development. If creating the analytics requires local software, then it isn’t cloud analytics. Performing the development in the cloud is not only easier for users; it also lowers security risk by removing the need to create local copies of the data. A true cloud provider will also take on support, infrastructure costs and management, automatic updates, and disaster recovery – so you can offload those internal management costs and focus on analytics.

  2. Enabling cloud choice: public, private, multi-cloud, and hybrid
    You may already be using multiple clouds to manage data and run applications. At the same time, to comply with security regulations, you’re probably also keeping some analytics development and consumption on-premises, or in a virtual private cloud. With dispersed architecture, you’ll want the flexibility to bring analytics to your data and run analytics computing in the cloud of your choice.

  3. Accommodating data gravity
    Many SaaS analytics vendors require you to move your data to their cloud. But moving your data can be expensive – and by distancing your data from your users, you can introduce latency and performance issues, too. Search for a solution that lets you keep your data wherever it’s most productive. You’ll want to avoid getting locked into a single vendor, where your options will dwindle.

  4. Single point of entry
    As with any SaaS solution, adoption is key. Make it easy for users by opting for a platform with a single point of entry for login. Administrators and IT leaders also need a simple way to manage data analytics across different clouds, regions, and users. Make sure they’ll be able to manage the entire deployment from one management console – and easily change the deployment model at any time.

  5. Self-service and readily available data, at scale, for all
    You shouldn’t have to be a coding pro to get in-depth insights about your data. The best cloud-based analytics solutions give business users easy access to data through a catalog, a simple user interface where they can “shop for” and select datasets, easily viewing lineage. The solutions also provide intuitive ways to get insights, allowing users to explore and analyze in all possible contexts, without limitations.

  6. Performance and scalability
    Most analytics solutions struggle with performance. That’s because they’re query-based, restricting users to predetermined paths in the data and requiring them to reformulate queries whenever they want to pivot. Look for a high-performing solution that can calculate analytics quickly even when used simultaneously by a great number of users. And make sure that scaling capacity in any direction will be straightforward and fast.

  7. Augmented analytics
    AI capabilities are becoming increasingly integral to analytics, and different platforms employ them differently. Instead of black-box AI that operates independently, look for a solution that uses AI to augment the user experience with things like insight suggestions and natural language interactions. That gives you the best of both worlds: machine intelligence that augments human intuition and understanding.

  8. Orchestration across your cloud ecosystem
    Automation is another tool that’s vastly accelerating analytics delivery and augmenting insight discovery. AI can speed time-to-insight by automating a wide variety of tasks for the user, including combining data sets, preparing and transforming data, and creating visualizations.

  9. Fully interactive mobile analytics
    From laptops to smartphones, the best cloud analytics solutions provide users with a consistent, comprehensive experience. This includes the ability to analyze and share data and apps from anywhere.

  10. A secure, enterprise-class experience with governed collaboration
    Your cloud analytics platform should allow you to easily assign and change permissions, so your data stays secure and the right people have access. And when you’re evaluating moving workloads to a SaaS platform, it’s vital to know that the service provider is following open and audited processes for security controls.

PRO TIP: You should look for these security certifications:

  • SOC 2 Type 2

  • SOC 3

  • ISO27001

Cloud Infrastructure Options

To support their cloud data analytics needs, most organizations use a mix of cloud types and providers to gain the most benefit. Here we take a quick look at the four main data service options.

Diagram showing Data On-Premises, Data in a Virtual Private Cloud, and Data in a Public Cloud flowing into Cloud Analytics.

  1. Data On-Premises: In this approach, data is stored on local hardware (servers or computers) which is typically set up at the company’s headquarters. Organizations with highly sensitive data, such as banks or medical organizations, may choose an on-premises solution for maximum security.

  2. Private cloud: Usually behind a firewall, private cloud services and hardware are dedicated to one company and provide increased data governance and control. As with on-premises, organizations with highly sensitive data might select a private cloud approach for increased security.

  3. Public cloud: Here you get higher performance and manageability with lower costs. Services are shared by multiple companies, but each company’s data and applications are hidden from each other.

  4. Hybrid and Multi-cloud: Many organizations use a mixture of different Public, Private and even on-premises approaches to optimize security, scalability and total cost of ownership. Best-in-class analytics solutions embrace this approach so organizations can choose which data is stored where and where analytics occurs.

Learn More About Cloud Data Services

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