What is Analytics as a Service?

Business today requires you to make data-driven decisions based on massive amounts of data. Plus, your teams and partners are likely to be more distributed than ever before. To deploy and manage an on-prem analytics solution that can meet these needs requires considerable hardware and software and time-intensive, complex data integration work. That’s why AaaS, also known as data analytics as a service (DAaaS), has grown to be the preferable approach over an on-prem solution.Here we describe the key reasons behind the shift to analytics as a service in cloud computing:

• Lower Cost & Fast ROI: Avoiding large upfront hardware investments for on-prem servers lowers your risk. Plus, you’ll cut more costs because you don’t need in-house expertise to maintain servers and software.

• Up-to-Date Capabilities: Automatic updates and upgrades give you state of the art capabilities and save you money because you don’t have to continually invest in software development and maintenance.

• Remote Workforce Support. Your distributed teams, suppliers, and partners will have immediate, governed access to interactive analytics from anywhere and on any device.

• Centralized Data = Complete Picture: An analytics as a service solution brings all of your data together to give you a complete picture of your business and maximize insights for everyone across your organization.

• Better Collaboration: The best analytics as a service platforms allow you to take snapshots of analysis, add commentary for better context, and tag in the discussions. This provides a collaborative place for real-time and asynchronous decision making.

• More Flexible Performance: AaaS makes it easy for you to turn services on and off as your data needs change instead of having to buy or sell hardware and software.

• More Reliable and Higher Security. SaaS security certifications require providers to meet stringent standards and the likelihood of error is far lower when servers aren’t manually configured. This makes SaaS environments a lower data security risk than on-prem.

You have your own unique business needs, data requirements, and IT ecosystem. That’s why it’s important to carefully select the tool that best fits your situation. Below are 10 key capabilities of a modern analytics as a service solution.

1. High performance and scalability.
   Most analytics solutions slow to a crawl when dealing with large datasets. This is because they’re query-based, which means they restrict you to analysis based on predetermined paths in the data and require you to reformulate queries whenever you want to pivot. Look for a modern tool which calculates quickly even when used on big data analytics and is used by a large number of users simultaneously.

2. Truly SaaS-based.
   Many platforms claim to be based in the cloud but still require you to install local software. Plus, your provider should handle all infrastructure costs and management, automatic updates, and disaster recovery.

3. Allowing choice of cloud (public, private, multi-cloud, and hybrid).
   You’ll want to be able to perform AaaS in the cloud approach of your choosing. To comply with data governance and sovereignty requirements, you may use multiple clouds to manage your data and run applications and you may also keep some analytics on-premises, or in a virtual private cloud.

4. No data “lock-in”.
   Make sure the tool you select allows you to keep your data wherever it’s most productive for you. Many AaaS vendors require you to move your data to their cloud. This can be expensive and can introduce latency and performance issues.

5. Single point of login.
   Your platform should make it simple for all users to use it by providing a single point of entry for login. Plus your administrators and IT team should have one management console to change the deployment model at any time and manage the data and analytics across various clouds, regions, and users.

6. Self-service for everyone.
   You shouldn’t have to be a data scientist to get deep insights from your data. The best analytics as a service solutions give all users easy access to data through a catalog, a simple user interface where they can find datasets and view data lineage, and then make it easy for them to explore and analyze data without limits.

7. Fully interactive mobile.
   The top analytics as a service tools give you a consistent, comprehensive experience from laptops to smartphones, letting you analyze and share data and apps from anywhere.

8. AI & augmented analytics.
   Look for a solution that uses AI to augment the user experience with capabilities such as natural language interactions and insight suggestions. This will give you computer intelligence which augments your intuition and understanding.

9. AI-driven automation.
   AI can also speed your time-to-insight by automating a variety of tasks such as combining data sets, prepping and transforming data, and creating visualizations. This modern automation is greatly accelerating analytics delivery and insight discovery.

10. Secure, governed collaboration.
    You want your data to stay secure and for the right people to have access to the right data. Your analytics as a service platform should make it easy for you to assign and change permissions.

PRO TIP: When you’re evaluating moving workloads to a SaaS platform, it’s important to confirm that the service provider is following open and audited processes for security controls. Look for these security certifications:

• SOC 2 Type 2

• SOC 3

• ISO27001