What is Analytics as a Service?
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.
What is 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.
Analytics as a Service Benefits
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.
How Analytics as a Service Works
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,
- Data flows in from cloud-based and on-premises sources and applications. Examples of data sources include web analytics, transactional, social media, and CRM data.
- The best analytics as a service platforms can handle hybrid data delivery and application automation.
- All your data is stored in a cloud data warehouse from a vendor such as Microsoft Azure, Snowflake, Amazon Redshift, or Google BigQuery.
- The analytics as a service tool uses this data to enable you to create interactive visualizations, dashboards and reports. The best tools go further by allowing you to perform augmented analytics, predictive analytics, machine learning or AutoML (automated machine learning), embed analytics into other applications, and trigger alerts and actions in other systems.
- These capabilities help you identify patterns and develop insights that lead to actions which can increase efficiency, revenue and profits.
- Top AaaS tools can also integrate with other applications to trigger automated, data-driven events.
AaaS Infrastructure Options
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 On-Premises: If your organization deals with highly sensitive data, you may choose to store your data on servers or computers located at your headquarters for maximum security.
- Data Stored in a Private cloud: Private cloud services will dedicate hardware, usually behind a firewall, to only your company and provide you greater data governance and control of your data.
- Data Stored in a Public cloud: In this approach, multiple companies share the analytics services but each company’s data and applications are hidden from each other. This gives you the high performance and manageability of the cloud but with lower costs.
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.
Selecting an Analytics as a Service Platform
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
Complete, cloud-native analytics platform
Learn how Qlik Sense® offers full enterprise SaaS usage, and client-managed options such as multi-cloud and on-premises deployment.
Take Analytics as a Service to a whole new level with Qlik
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.