Multi-Tenant Analytics: Why it’s Hard to Build

What is Multi-Tenant Analytics?

Multi-Tenant Analytics refers to a scenario where multiple users or groups of users, also known as “tenants”, can securely access and analyze their own data within a SaaS platform. Each tenant’s data remains private and separate from others in such an application.

Why is Multi-Tenant Analytics Important to the Success of SaaS Companies?

Multi-tenant analytics for SaaS is a powerful tool that is becoming increasingly important to SaaS companies. This technology allows these companies to embed analytical capabilities directly within their applications, creating a seamless user experience for their customers.

But why is this so important? The answer lies in the nature of SaaS businesses. These companies often serve numerous clients, each with their own unique set of data.

With multi-tenant embedded analytics, each customer – or tenant – can analyze and extract valuable insights from their own data. This helps them make informed decisions, optimize their operations, and ultimately, get more value from the SaaS product.

Moreover, multi-tenant analytics also ensures the privacy and security of each customer’s data. Despite all customers using the same application, their data remains isolated and secure from other tenants. This is crucial in a time when data breaches and privacy concerns are top of mind for many businesses.

What are the Benefits of Multi-Tenant Analytics to Users of SaaS Platforms?

Self-Service Report Creation

The key advantage is that it enables self-service reporting. This means that users can generate their own reports, customize their data, and create visuals. All of this is possible without needing to rely on the IT department.

Custom Dataset Creation

Additionally, multi-tenant analytics also facilitates the creation of custom datasets. Users can gather data from many sources, combine it into one set, and analyze to find important information. Customizing data and analyzing it in depth boosts the benefits users get from their SaaS platforms.

Analytics functions can change to fit business needs. This helps users adapt to new challenges and environments.

What are the Advantages of Multi-Tenant Analytics for Product and Engineering Teams?

Multi-tenant analytics is particularly beneficial to teams that build and maintain SaaS platforms.

Here’s why:

Cost Efficiency

All tenants split costs for upkeep and improvements, saving money by sharing resources. White-labeling an embedded analytics platform such as Qrvey can also be a shared among your user base.

Scalability

Multi-tenant architecture allows for easy scaling with shared infrastructure. Platform growth directly dictaates Infrastructure growth which means you can control when to increase resources.

Improved Data Analysis and Insights

With each tenant having access to their own data, they can analyze in-depth and gain valuable insights. This information can help them make informed decisions and optimize their operations.

Greater Personalization

Multi-tenant analytics tools allows for higher levels of personalization. Each tenant can tailor the system to suit their specific needs, without affecting the user experience of others.

WATCH: Check out an overview of how Qrvey handles user personalization.

Enhanced Data Security

Despite the shared nature of multi tenancy, each tenant’s data remains secure and isolated from others. This is crucial in the modern business world, where data breaches and privacy concerns are a top priority.

For the Product and Engineering teams, multi-tenant analytics provides the opportunity to innovate and enhance their offerings. Companies can use system insights to enhance products, meet client needs, and remain competitive in the market.

Integrating a turnkey solution like Qrvey allows these teams to allocate their resources more effectively. You can focus on areas that add the most value to their product.

Why Building Multi-Tenant Analytics is Incredibly Hard

We see companies struggle with this repeatedly. Creating performant, secure, and scalable multi-tenant analytics requires overcoming steep data engineering and infrastructure challenges that stretch the limits of most software teams.

Most engineering teams try to connect their front-end dashboards directly to their database. This works for static, one-size-fits-all reporting. Engineering teams typically either create a database per tenant or add a tenant variable to a shared database.

But that doesn’t offer much value to end users.

Without Qrvey, engineering teams have to build:

1) a data pipeline for any necessary data transformations

2) another database or data warehouse specifically for analytics

3) a semantic layer that adds multi-tenant security logic

4) an entitlements database that maps to user security in the overall SaaS application

5) an API layer so the front end can make calls to its back end.

Phew….that becomes a ton to build and maintain over time. Your application without Qrvey looks something like this:

ANALYTICS WITHOUT QRVEY (scroll down for WITH QRVEY)

Ensuring Tenant Data Security

SaaS multi-tenant analytics platforms must isolate data between different customer tenants. This involves tackling permissions, access controls, and strict separation mechanisms to prevent exposure beyond authorized users. This becomes critical as data volumes, queries, and user concurrency expand over time.

Compliance regulations like GDPR and HIPAA further complicate data management because of auditing requirements and data sovereignty considerations.

Many healthcare software companies have trouble adding healthcare analytics to their app. That’s why we only support cloud-native software at Qrvey.

Managing Performance

Multi-tenant analytics has unpredictable traffic patterns, unlike single-tenant applications. This happens because customers can access reports and dashboards on their own, each with their own usage patterns and needs.

The biggest challenge is ensuring the data system can handle various tenant needs and a lot of data efficiently. To do this, we use advanced methods like microservices, container orchestration, and auto-scaling mechanisms.

However, one of the most significant developments in this area is serverless technology. This presents a relatively new and innovative pathway to scaling processes and systems. This is a notable advancement, considering that legacy analytics software typically ran on expensive servers. Those often posed limitations in terms of scalability.

Qrvey helps solve this challenge by developing on serverless technology from the beginning. Qrvey’s solution does not require buying or renting servers, saving money on expensive compute clusters that are often unused.

Integrating Disparate Data Sources

Companies that try to offer multi-tenant analytics struggle to combine different data sources. Some apps can use only one database. However, complex analytics usually require data from multiple sources for multi-tenant scenarios.

These data sources can be as diverse. They range from online databases, cloud storage, log files, and data from IoT sensors. Companies typically have to build separate pipelines with dedicated ETL for every data source.

Given the vast variety of these sources, the task of data integration can become quite daunting.

However, the need for comprehensive insights and reporting capabilities makes this integration essential. Flexible and repeatable pipelines are essential for the platforms that manage these multi-tenant analytics.

Qrvey solves this challenge by offering a unified data pipeline that works with any data type. Simplifying and consolidating development efforts makes engineering more efficient, benefiting end users with a wider range of data for analysis.

Mapping User Roles to Data Access in Multi-Tenant Environments

In a multi-tenant application, customers are accessing a shared data source for reporting. This mean mapping user permissions from the SaaS application to analytics data is to hard to maintain over time.

The use of semantic layers adds to this complexity. Semantic layers, however, are a crucial component to offering reporting for multiple tenants. These layers allow for the implementation of detailed data access controls. However, they can become quite intricate, especially considering the highly dynamic security needs often present in these environments.

Qrvey includes a native semantic layer. We know you can’t be successful without this component and it’s always a task for development teams to build and maintain. With Qrvey, it’s included.

Enabling Tenant-Specific Customization in Multi-Tenant Analytics

In the world of multi-tenant analytics, there are certain elements are typically standard. These include the core dashboards or reports available to all tenants. This standardization is vital for maintaining consistency in the process of analyzing data.

Tenant-specific customization could include elements like

• unique datasets
• data visualization for embedded analytics can include starter templates
• the ability to modify tenant-level reports
• metrics and dimensions available to each user

Striking a balance is key.

Standardization of certain elements and customization of others.

Qrvey makes complex tasks easier by providing a data management layer. Use cases such as custom data models creation are now within reach.

Game changer.

Software Engineers are Not Data Engineers

Software engineering teams are experts in their field. They often find themselves lacking the specialized skills necessary for managing multi-tenant analytics and large data volume queries.

These skills include:

• managing concurrent analytical workloads
• implementing sophisticated security models
• designing high-performing query engines

Development Tasks Become Increasingly Demanding

• Data Migration and Onboarding: As the scale of operations increases, the task of seamlessly migrating tenant data grows. In addition, ensuring smooth onboarding flows becomes progressively more challenging. It requires careful planning and execution to handle the growing data volume. Particularly while minimizing disruption to the end users.
• Monitoring and Troubleshooting: Keeping track of tenant analytic activity is a demanding task. Identifying and resolving issues in various tenants requires a strong understanding of data analytics. This necessitates a robust system for monitoring and troubleshooting.
• Testing and Quality Assurance: Ensuring the integrity and functionality of features across various tenant data permutations is another essential yet demanding task. It requires the implementation of rigorous, automated testing programs. The goal here is to prevent potential issues such as data leakage or access control problems.

This level of quality assurance and monitoring is critical in maintaining trust and reliability with end users.

Qrvey: A Purpose-Built Multi-Tenant Analytics Solution

ANALYTICS WITH QRVEY

Qrvey is a turnkey solution. It helps development teams provide embedded analytics for SaaS apps. It works with any data source, data type, or front-end framework.

Qrvey is a complete solution that uses one data pipeline to collect, combine, and analyze data from different sources. Qrvey offers a suite of APIs and visualization widgets to create customizable analytics experiences for users.

Qrvey can handle various challenges and scenarios for multi-tenant analytics. Our customers deploy use cases such as custom data models, personalized data visualizations, combining multiple data sources, and targeting content deployment. Qrvey can also improve SaaS product metrics (CSAT/NPS) and reduce costs.

Our goal is to simplify the process of providing improved analytics and reporting for engineering teams. Let us show you how.