Embedded analytics, the integration of reporting and data visualization capabilities into existing software applications, is becoming a requirement across industries. It powers next-generation digital experiences that delight users and provides competitive differentiation for SaaS companies.

However, custom data models lie at the heart of any strong embedded analytics feature set.

Delivering impactful and tailored analytics within applications introduces unique data modeling challenges. When customers want custom fields and different reporting views, the challenge for software engineering teams grows exponentially.

Here we will look at the immense value of custom data models in enabling flexible yet consistent embedded analytics. The value it brings can unlock a multitude of benefits for both SaaS companies and their users.

What is a Custom Data Model

A custom data model is an abstracted, logical representation of the diverse data structures, relationships, and semantics required to serve each customer or tenant using an application.

Within multi-tenant software platforms, custom data models:

  • Reflect individual tenant entities, attributes, metrics, and insights needs
  • Standardize and map disparate data sources into an integrated view
  • Govern how users interact with data through fine-grained security policies

With custom data models, tenant analytics becomes truly customizable instead of one-size-fits-none.

The Limits of One-Size-Fits-All Data Models

These tools come with ready-made, generic data architectures that each and every customer is required to adapt to fit their needs. While this might sound appealing due to its supposed simplicity and ease of implementation, it comes at a cost.

For example, popular solutions such as Tableau and QuickSight are known for their internal analytics capabilities. But to address that use case, they use standardized schemas that impose severe restrictions when it comes to enabling users to tailor their analytics experience.

This restriction becomes apparent when users attempt to merge and integrate an assortment of data sources. Because of the inflexible nature of predefined data models, these users encounter big issues along the way.

Read: 3 Reasons Engineers Struggle with Snowflake for Multi-Tenant Analytics

They are compelled to force diverse and distinct data types and use cases into constrained and rigidly defined templates or frameworks. This act of shoehorning can often lead to inefficient and less optimal outcomes.

Another area negatively impacted by this lack of flexibility involves the core systems themselves – particularly the functions related to roles and permissions. These essential components of an analytics platform are frequently entrenched in the system’s code.

Such rigidity in role definition and permission allocation can stifle organizational workflows rather than streamline them.

Limitations of Conventional Data Warehousing in Accommodating Multi-Tenant, Customizable Data Models

Traditional data warehouse technology is limiting. While it forms the backbone for embedded analytics within Software as a Service (SaaS) applications, there are a host of limitations.

These systems were not originally designed to handle the dynamic and varied needs of end users. When each tenant has unique data structures data warehouses fall down.

Read: Data Warehouse vs Data Lake

Rigid Architecture

One key problem with legacy solutions is their inherently rigid architecture. This often results in a disjointed and compartmentalized approach to data management, where data is stored in separate silos, making it challenging to achieve a holistic view across customers or tenants.

Consequently, when analytics are embedded into these SaaS applications, they are met with substantial customization barriers due to the inflexible nature of these warehouse solutions.

Difficult Maintenance

The hurdles continue to mount as SaaS engineering teams grapple with the increasing intricacies of tailoring storage solutions, ETL (Extract, Transform, Load) processes, and hardware infrastructure to meet the specific analytical requirements of product teams. They must navigate through the labyrinth of configuration without much guidance, leading to a significant amplification in complexity and resource commitment.

Too Many Versions

Further exacerbating the situation is the issue of “version sprawl.” As data warehouse providers update and improve their platforms, customers using older versions find themselves locked out from these advancements.

They are left dealing with outdated systems that no longer align with the evolving functionalities provided by newer releases. The lack of backward compatibility or smooth migration paths means that they are stranded on these antiquated platforms, unable to leverage the innovation and enhancements offered by newer technologies.

Lack of Focus on Semantic Models

Another critical shortcoming of traditional warehouse technology is its negligible focus on semantic modeling. Semantic layers allow for the addition of business context to raw data, enabling users to interact with it in a more intuitive and meaningful way.

However, most legacy data warehouses do not include native support for semantic layer capabilities, which are essential for implementing fine-grained access control, ensuring data governance, and managing metadata effectively.

For multi-tenant software, where these aspects are paramount in maintaining the individuality and security of each tenant’s data environment, the absence of such features is a sizable deficiency.

As a result, SaaS providers and their customers in need of embedded analytics face considerable challenges due to the shortcomings of these legacy systems.

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The Custom Data Modeling Imperative

Without custom data models, even the most advanced analytics fail to deliver value, leading users to abandon platforms.

Fortunately, purpose-built solutions overcoming legacy limitations have emerged, combining:

  • Cloud elasticity and scalability
  • Shared metadata catalogs
  • Tenant-level and user-level data security controls
  • Integrated governance guardrails
  • Automations streamlining access
  • Flexible deployment models

Together, these capabilities empower tailored analytics aligned to customer needs – at any scale.

Read: Why engineers struggle with Amazon Redshift for multi-tenant analytics

Qrvey: Removing the Burden From Engineering Teams

Qrvey makes multi-tenant analytics with custom data modeling accessible for SaaS providers. We provide the necessary components to create an analytics layer within your SaaS application. This means you do not have to build it yourself. Or worse, force an internal BI tool into a multi-tenant use case.

Multi-Tenant Data Lake

We know that you cannot have a strong analytic feature without first investing in the data layer. This is one of the primary reasons why customers choose Qrvey over the competition.

Qrvey consolidates diverse data into a high-performance multi-tenant data lake. It handles a wide range of data types. This includes structured, semi-structured, and unstructured data. And with a unified data pipeline approach, you can use streaming and batch data at any volume.

Qrvey’s platform includes security features as well at the tenant, user, and row/column level. This keeps the data and platform secure with any framework a SaaS platform implements.

Tenants access shared data securely through isolated roles. Real-time visibility and direct interconnectivity preserve data integrity without movement as Qrvey is deployed into cloud environments like AWS VPCs.

Cloud-Native Architecture

On-demand, consumption-based infrastructure optimizes costs while auto scaling when you’re ready.

In the old way of setting up servers, developers end up spending money on the system even if no one is using their apps. This can waste cash, especially when the number of people using the app goes up and down often.

Orchestration Automation

Qrvey automates orchestrating custom models, data integration, transformation, and lifecycle management. Qrvey leverages ML for automatic data profiling on ingestion to simplify the process of understanding data.

Additionally, Qrvey includes the most comprehensive workflow automation solution amongst embedded analytics software providers. As an embeddable component, SaaS users can create customer automation specific to their requirements using those same custom data models in use for analytics and reporting.

Embedded Data Management

Companies use custom data models in different ways, but many Qrvey customers offer custom dataset creation within each tenant on a SaaS platform. As an embeddable widget or using the platform APIs, product leaders can allow users to select the specific data points they want and create custom datasets on the fly to use with specific reports.

With Qrvey, SaaS providers readily grow recurring analytics revenue while delighting users with models tailored to their needs…without infrastructure or modeling complexity.

The Core Competency Driving Success

Legacy solutions failed to deliver custom data modeling capabilities, crippling multi-tenant analytics software. Qrvey overcame these limitations through cloud agility, automation and purpose-built functionality that comes with thoroughly understanding the challenges of SaaS platform development.

As embedded insights create workflows across industries, custom data model flexibility unlocks immense potential. For SaaS companies and users seeking differentiated value with analytics-enabled apps, they often find themselves in a strong competitive situation.

Through accessible, secure, and scalable custom modeling, Qrvey pioneers this new era of multi-tenant embedded analytics with infinite potential.

Get Started with Embedded Analytics Built for SaaS

See for yourself.

Qrvey delivers the best embedded analytics tool built for custom data modeling legacy BI software cannot handle.

Request a demo and we’ll show you how we can reduce your development time and spend.

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