What it means, why it matters, and how it works. This guide provides definitions and practical advice to help you understand modern 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 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.
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:
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.
Let’s walk through the diagram above.
Learn the top 10 things to look for so you can choose one that fits your data requirements, business needs, and IT ecosystem.
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:
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.
Learn more about cloud data services.
See how to explore information and quickly gain insights.