What it means, why it matters, and how it works. This guide provides definitions and practical advice to help you understand modern analytics as a service.
Analytics as a service refers to a subscription-based model in which data analytics and BI processes take place on cloud-based, vendor-managed systems rather than using on-premise hardware. With the growth of big data, analytics as a service (AaaS) typically offers you far lower costs and a faster ROI than the traditional on-premise approach which requires upfront hardware investments and in-house expertise to maintain the software and servers.
Ultimately, AaaS provides you a wide variety of analytics capabilities, such as data visualizations, interactive dashboards, reporting and alerts. The top tools go further, with sophisticated augmented analytics, predictive analytics, machine learning, embedded analytics, and the ability to trigger actions in other systems.
These capabilities help you identify patterns and gain insights. And these insights lead to actions which can increase your company’s efficiency, revenue, and profits.
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:
In analytics as a service, data analytics and BI processes take place on cloud-based, vendor-managed infrastructure rather than on-premise hardware. This is why it is sometimes referred to as cloud analytics. The analytics vendor typically manages setup and maintenance, which makes it easier and less expensive for you to operate.
As shown in the diagram above,
Most companies use a mix of providers and cloud types to support their analytics as a service needs. There are four main data service options for you to consider.
Data Stored in a Hybrid and Multi-cloud: To optimize security, scalability and total cost of ownership, many companies employ a mix of public, private and on-premises approaches. Best-in-class AaaS solutions embrace this approach so organizations can choose where different datasets are stored and where analytics takes place.
If you have strict governance rules and/or data sovereignty requirements, hybrid analytics as a service allows you to realize the benefits of the cloud even when you can’t move all your data to the cloud.
See how to explore information and quickly gain insights.
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:
A best-in-class, self-service business intelligence architecture is just one way Qlik Sense® sets the benchmark for next-generation data analytics technologies. Our one-of-a-kind associative analytics engine and sophisticated AI empowers people at all skill levels to freely explore data, make bigger discoveries and uncover bolder insights that can’t be obtained using other analytics tools. With Qlik, you can support nearly any use case and massively scale users and data, empowering everyone in your organization to make better decisions every day.