Embedded Analytics Requirements: 8 Key Considerations for SaaS
David Abramson··32 min read
⚡Key Takeaways
- Embedded analytics requirements cover eight areas: product scope, data architecture, multi-tenancy, embedding, self-service and AI, performance, deployment, and commercial terms. Translate customer workflows into prioritized, testable criteria before evaluating vendors.
- Multi-tenancy requires more than RLS. Verify isolation and permissions across dashboards, exports, APIs, self-service content, and AI, while checking that embedding and white labeling support the interactions customers need inside your product.
- Compare internal builds, general-purpose BI, and purpose-built platforms against projected usage and three-year TCO. Include engineering, infrastructure, warehouse usage, and support costs; AI coding tools accelerate development but leave ongoing maintenance with your team.
- Qrvey helps SaaS teams reduce custom analytics engineering through built-in data management, tenant-aware security, JavaScript embedding, white labeling, self-service, and governed AI, with flat-rate licensing that avoids per-user license cost growth.
The easiest way to choose the wrong embedded analytics platform is to start with the vendor demo.
Attractive dashboards, AI features, and visualization libraries are easy to compare. The harder questions are whether the platform fits your users, data architecture, multi-tenant security model, deployment process, performance needs, and growth plans.
This guide breaks embedded analytics requirements into eight practical areas so you can define what your SaaS product actually needs, ask better questions during vendor evaluations, and compare platforms against real requirements rather than the most impressive feature list.
What Are Embedded Analytics Requirements?
Embedded analytics requirements are the functional, technical, operational, and commercial criteria an analytics solution must meet to work within your SaaS product. They define what customers need to accomplish, how analytics must fit your application, and what your team needs to deliver and maintain it.
For SaaS companies, those requirements must reflect real production conditions. A capability available in a standalone environment may have limitations when embedded across multiple tenants. Your evaluation needs to establish whether the capabilities you select work together within your application’s data model, access rules, and operating environment.
Useful requirements connect an intended outcome to something you can verify. If your goal is to reduce custom reporting requests, for example, define which tasks customers should complete independently and ask vendors to demonstrate those tasks in a representative tenant environment.
Documenting these expectations gives product, engineering, and business teams a shared basis for comparing vendors and identifying implementation gaps. It also helps distinguish what is essential at launch from what the platform must accommodate as your product and customer needs evolve.
1. Product and User Scope Requirements
Start with the people who will use analytics and the decisions they need to make. Their needs determine everything from data preparation to permissions, interface design, and cost.
Identify whether you’re serving internal staff, customer administrators, everyday customer users, or customers of your customers. A customer administrator may need to distribute dashboards across departments; an everyday user may only need to check exceptions and take action. Those are different product requirements.
Define your scope across five areas:
- Growth: Expected tenants and users today, in 12 months, and in three years.
- User autonomy: Who views dashboards, creates reports, explores data, asks AI questions, or configures workflows?
- Tenant variation: Which customers need their own datasets, metrics, branding, roles, or dashboards?
- Packaging: Which capabilities belong in each customer tier, and is analytics included or sold as a premium feature?
- Business outcomes: Which reporting requests should disappear, which decisions should become easier, and how will you measure adoption?
Use product scenarios to make those requirements concrete:
| Product scenario | What customers need | What to establish before evaluation |
|---|---|---|
| Basic customer reporting | View standard dashboards and retrieve relevant records | Required views, filters, exports, and access rules |
| Enterprise SaaS analytics | Adapt reporting to departments, roles, and customer-specific processes | Tenant customization, delegated administration, and contractual requirements |
| Fully self-service analytics | Create and share analytics, explore with AI, and configure permitted actions | Authoring rights, supported workflows, and boundaries around customer autonomy |
Tenant count alone won’t tell you how complex the implementation will be. Twenty enterprise customers with different data models and approval processes can create more work than hundreds using the same configuration.
Prioritize capabilities that improve your customers’ actual workflows, then distinguish launch essentials from future needs. That gives vendors a product scenario to demonstrate and your team a clear basis for judging fit.
2. Data Architecture and Connectivity Requirements
Operational data often needs preparation before customers can analyze it reliably. Establish who will own that work and where it will run.
- Source Connectivity and Data Access
Inventory your operational databases, cloud warehouses, NoSQL systems, APIs, flat files, and customer-provided sources. For each, specify freshness requirements and how updates, deletions, and schema changes must be handled.
Ask: Can the platform deliver analytics from our operational sources, or does it require a separate analytical database first? Confirm which connectors, transformations, and ingestion APIs are included, and what your engineers must build.
- Live, Cached, or Hybrid Query Architecture
Live queries use the source system’s current data, but response times and costs depend on that system’s available capacity. Direct querying can suit teams that deliberately keep analytical data in an existing warehouse.
Cached results or synchronized analytics data can reduce repeated source queries, with freshness determined by refresh behavior. A hybrid approach can serve different workloads differently.
Ask whether the platform includes a data engine and transformation layer. A semantic layer defines meaning; you’ll still need somewhere to prepare and execute queries against the data.
Qrvey’s Data Management Layer can reduce custom pipeline work through three connected capabilities: its Data Pipeline connects and prepares operational data, its Data Lake stores and optimizes analytics-ready data, and its APIs automate data operations.
- Data Modeling and Semantic Definitions
Define shared metrics, calculations, relationships, and business terminology before exposing self-service or AI. Ask how a definition changes across dashboards, reports, and AI answers, and whether customer-specific calculations can coexist with centrally maintained metrics.
Also establish where joins and transformations execute. Their location affects maintenance, freshness, and compute costs.
- Tenant-Specific Data Requirements
Confirm support for your actual tenant model: commingled data, separate schemas or databases, or a hybrid arrangement. Can tenants add fields, connect their own sources, or customize datasets without duplicating the entire pipeline?
Ask whether those capabilities remain available across each supported model. Also verify data portability, including whether stored data is accessible through standard interfaces such as JDBC or ODBC, and what you can retain if you change vendors.
- Warehouse Load and Data Processing Costs
Repeated dashboard loads, filters, exports, and AI questions can multiply source queries. Evaluate query volume alongside synchronization, storage, transformation, and data transfer costs.
3. Multi-Tenancy, Security, and Governance Requirements
Serving several customers proves that a platform can host multiple accounts. Evaluating its multi-tenant architecture means checking how consistently it separates data, permissions, content, and activity throughout the analytics experience.
- Seamless Support of Your Multi-Tenant Data Model
An important consideration for embedded analytics is how well it supports your product’s multi-tenant data architecture. There are two broad approaches:
Shared data: Multiple tenants’ data resides in shared tables, with tenant isolation enforced through controls such as row-level security. Column-level permissions can further restrict access to sensitive fields.
Separate data: Each tenant has its own tables, schema, database, or deployment, with access restricted to the appropriate tenant.
Hybrid models combine these approaches. For example, shared storage for most tenants and dedicated databases for others.
It is important to understand (1) which architecture best suits your product and customers’ requirements and (2) how naturally your embedded analytics platform supports that architecture.
Beyond connectivity, evaluate how the platform enforces tenant access and lets you reuse and update analytics across tenants. An architecture that works for a handful of customers should also be manageable as your customer base grows.
- Tenant Data Isolation
Ask the vendor to show where tenant isolation is enforced: the interface, application logic, semantic model, query execution, or data layer. Security boundaries must hold on the server before unauthorized data reaches the browser.
Confirm how the platform identifies the requesting tenant and prevents users from changing that scope. Test your intended shared or segregated data model, including queries that combine datasets.
- Record- and Column-Level Security
Record-level security, often called row-level security, controls which records a user can access. Column-level controls restrict sensitive fields within those records. A regional manager might see their region’s projects while payroll fields remain restricted to finance.
Require consistent enforcement across dashboards, drill-downs, exports, APIs, scheduled reports, and AI access. Ask how role changes affect existing sessions and saved content, and request a demonstration that restricted fields remain inaccessible through alternate paths.
Qrvey supports record- and column-level access controls, allowing you to restrict which records and fields each user can access. This helps teams accommodate different roles within the same tenant, such as giving managers access to project performance while limiting sensitive financial fields to authorized users.
See how Qrvey approaches RLS in this clickable demo.
- Authentication and Permission Inheritance
Map how your application’s identity, tenant membership, roles, and permissions reach the analytics platform. Single sign-on simplifies authentication; you still need to establish how authorization follows the user into analytics.
For JSON Web Tokens (JWTs) or other security tokens, clarify who issues and validates them, how they expire, and how permission changes take effect. Ask who maintains the rules when you introduce a new role or customer tier.
Qrvey supports passing user and tenant context from the host application through security token authentication. That allows analytics to follow the application’s existing permissions and access rules, reducing the need to maintain separate user and permission models.
- Tenant Content and Asset Isolation
Isolation must extend to dashboards, reports, datasets, saved queries, and AI conversations. Tenant A mustn’t discover or access Tenant B’s assets through search, shared links, exports, or identifiers.
Define who can create, edit, publish, and share content within each tenant. If administrators can distribute shared templates across customers, ask how the platform separates those templates from customer-owned modifications.
- Auditing, Governance, and Compliance
Request evidence of access logging, administrative changes, export activity, retention controls, encryption, and incident investigation support. Logs should identify the relevant tenant, user, action, and time. Confirm which events your team can retrieve and how long they’re retained.
Certifications can support a vendor assessment, but your implementation still needs correct access controls and configuration.
Ask the vendor to demonstrate these checks:
- Open the same dashboard as users from two tenants and verify the permitted data for each.
- Repeat the exercise through drill-downs and exports.
- Attempt to access the other tenant’s saved content and restricted fields.
- Change a user’s role or customer tier and show how access updates.
Qrvey’s multi-tenant architecture supports both commingled and segregated tenant data, with customer-specific data models, dashboards, and configurations.
Your team can tailor analytics to each customer’s requirements while maintaining consistent tenant isolation and access controls across the platform. This reduces the custom engineering needed to accommodate different customers as the number of tenants, roles, and datasets grows.
4. Embedding, White-Label, and UX Requirements
Your embedding requirements should describe how analytics behaves inside the product: where it appears, how it interacts with the page, and how much control your team retains.
- Embedding Method and Product Integration
Compare approaches against the interactions your product needs:
| Approach | What to evaluate |
|---|---|
| Iframe embedding | The vendor’s supported controls for styling, navigation, authentication, and communication with the host page |
| Native JavaScript components or web components | Component placement, themes, host-page interaction, events, callbacks, and programmatic configuration |
| SDKs and APIs | Available integration functions, framework compatibility, and the interface and maintenance work your team must own |
These approaches can overlap: an SDK may wrap an iframe or expose native components. Ask what actually renders in the browser and which interactions are supported.
Qrvey does not support iframe embedding. Its native JavaScript components let teams embed analytics within the application’s interface and control how customers interact with it.
- White-Label and Branding Control
White labeling includes typography, terminology, navigation, and interaction patterns alongside colors and logos. Establish whether configuration applies globally, per tenant, or per component.
Ask whether vendor identity can be removed from login flows, URLs and domains, exports, scheduled reports, emails, dashboard builders, and AI interfaces. Confirm whether the controls you need are included in the proposed license.
Qrvey supports fully white-labeled analytics that can follow your product’s UX, branding, and navigation. That includes dashboards, individual visualizations, embedded builders, and branded reporting experiences.
Your team retains control over how and where customers interact with analytics, helping users experience it as a native capability of your product.
- Responsive and In-Context UX
Test analytics in the screens and devices customers actually use. A chart inside an account page has different space and navigation constraints from a dedicated reporting workspace.
Verify responsive layouts, keyboard access, loading and error states, and persistence of relevant filters. Can selecting an account or date range in the host application update analytics without making the user repeat those selections?
- Embedding Individual Components vs. Full Dashboards
List the elements you need to embed independently: charts, tables, filters, dashboards, report builders, or AI interfaces. Confirm that every customer-facing capability remains available inside the product, including creation and editing.
Practical demo test: Give the vendor an existing product screen and ask them to embed one visualization or builder within it. Test the surrounding navigation, interactions, and styling.
5. Self-Service Reporting, AI, and Agentic Workflow Requirements
Define self-service by what each user should accomplish independently. Everyday users may need quick answers; power users may need to create datasets, build reports, and share analytics with colleagues. Your application should determine the data, permissions, and actions available to each.
- Dashboard and Report Consumption
Specify the viewing experience: filtering, drill-downs, saved views, subscriptions, and permitted exports. Check whether users can personalize a dashboard and retain their changes across sessions without altering the shared version.
Ask vendors to demonstrate a typical customer question from opening the dashboard through finding the relevant records. This establishes whether the experience works for users who won’t build reports themselves.
- End-User Self-Service Authoring
Determine who can create dashboards, rename fields, define calculations, customize datasets, and publish content to colleagues. Confirm whether these capabilities work with commingled tenant data or require a separate model, database, or engine for every customer.
Qrvey’s self-service analytics supports dashboard personalization, embedded authoring, and data preparation for authorized power users. Teams can expose different levels of autonomy while controlling the datasets, definitions, and permissions underneath.
Ask how customer-created metrics are distinguished from centrally approved definitions and what happens to saved reports when the underlying data model changes.
- Operational and Pixel-Perfect Reporting
Invoices, statements, and other operational documents need precise layouts, pagination, headers, footers, and totals. Specify output formats, scheduling, distribution, tenant branding, and recipient access requirements separately from interactive dashboard features.
Qrvey supports pixel-perfect reporting with embedded authoring, scheduled delivery, and tenant-aware templates. Test a real multi-page document, including long tables and customer-specific branding, to verify that the output meets your requirements.
- Conversational AI and Agentic Analytics
Turn AI requirements into specific customer tasks and acceptance tests:
- Can users ask natural-language questions and receive answers grounded in approved datasets and business definitions?
- Can AI create visualizations and explain existing charts while respecting tenant and role permissions?
- Can your team define each agent’s context, accessible assets, permitted actions, and approval requirements?
- Can you adapt models and agent behavior as your product evolves?
Qrvey Sidekick provides the conversational interface. Qrvey’s Chart Builder Agent creates visualizations from natural-language requests, while Smart Analyzer Agent helps users investigate existing charts.
Custom Agents let teams define product-specific context and use cases; the Qrvey MCP Server connects AI to datasets, dashboards, metadata, and tenant-aware permissions.
Ask vendors to demonstrate an ambiguous question, a request for restricted data, and an attempted action outside the agent’s scope. Assess the response and the controls your team can configure.
- Alerts, Workflows, and Automated Actions
Define what should happen after analytics detects a condition. For example, an overdue-project threshold might notify a manager or start an escalation workflow. Specify who can configure triggers, which actions are allowed, and when human approval is required.
Qrvey’s AI-Powered Workflow Automation connects analytics conditions to notifications and operational actions, with AI assistance for guidance and recommendations. Test the complete sequence, including tenant scope, failed actions, and retries.
6. Performance and Scalability Requirements
Set measurable expectations for the workload your product will generate. A vendor’s test environment is useful only when you understand how closely it resembles yours.
- Concurrent User and Query Load
Build a workload profile covering tenant count, active users, peak concurrent sessions, dataset sizes, dashboard complexity, refresh frequency, and expected AI query volume.
Include the number of queries each interaction produces. A dashboard with several visualizations, background refreshes, and active filters can generate multiple requests per user. Test interactive usage alongside exports and scheduled reporting.
- Dashboard and Query Response Time
Define acceptable response times and availability based on your product experience, then establish how they’re measured. Track initial dashboard loading, filtering, drill-downs, and longer-running analytical requests separately.
Include a percentile target, such as the response time met by 95% of requests, so averages don’t hide slow experiences. Where analytics is strategically important, require a proof of concept using realistic datasets and concurrency before agreeing to service levels.
- Caching and Workload Isolation
Ask what happens when a large tenant launches a complex analysis during peak traffic. Can the platform limit or separate that workload so other customers retain acceptable performance?
Test warm and cold caches, refresh activity, and sudden usage spikes. Clarify how tenant permissions apply to cached results, how quickly extra capacity becomes available, and whether scaling shifts additional load onto your warehouse.
- Scaling Across Tenants
Evaluate whether growth requires additional shared capacity, dedicated resources for selected tenants, or separate infrastructure for every customer. Ask how the platform monitors tenant-level usage and diagnoses slow queries as the deployment grows.
Real example: JobNimbus replaced inflexible reporting with customizable analytics, rolling out Qrvey to 6,000 customers. Within months, adoption reached 70% among targeted large enterprise users. Customers gained more flexibility to explore their business data, while JobNimbus reduced churn tied to reporting limitations.
7. Deployment, DevOps, and Analytics Lifecycle Requirements
Analytics becomes part of your software lifecycle as soon as customers depend on it. Include installation, releases, tenant onboarding, recovery, and ongoing ownership in the evaluation.
- Hosting Model and Data Residency
Compare vendor-hosted SaaS, deployment in your own cloud, private environments, and hybrid arrangements against your security policies, customer contracts, infrastructure strategy, and operational capacity.
Ask which cloud providers and regions are supported today and where data, backups, logs, and AI processing reside. Establish responsibilities for patching, monitoring, scaling, and recovery, including any manual server management.
Qrvey supports deployment in your AWS or Azure environment using container-based architecture, with GCP support coming soon. That gives teams control over where the analytics platform runs while fitting it into their existing cloud operations.
- Development, Testing, and Production Environments
Require separate environments where your team can validate integrations, permissions, and dashboard changes before release. Confirm how closely they can match production and whether additional environment licenses are required.
Ask how the platform handles environment-specific connections and secrets, and how you can test realistic tenant configurations without exposing production customer data unnecessarily.
- Content Promotion and Version Management
Request a demonstration of moving a dataset, its dependent dashboards, and related configuration from development through staging into production. Check dependency handling, release versions, rollback options, and preservation of tenant customizations.
Qrvey includes content deployment capabilities for moving analytics through development, testing, and production. Validate the proposed promotion and recovery process against your release requirements.
- Tenant Provisioning and Operational Automation
Document everything required to onboard a customer: baseline datasets, dashboards, roles, permissions, branding, and configuration. Ask which steps can run programmatically and how failures are detected and recovered.
The same assessment should cover plan changes, customer-specific updates, and offboarding. Repeating manual setup across hundreds of tenants creates avoidable operational work and inconsistent configurations.
- APIs and CI/CD Integration
Check API coverage for the assets your team must manage: users or identities, groups, datasets, semantic definitions, dashboards, and configuration. Request documentation for authentication, versioning, rate limits, and error handling.
For continuous integration and delivery (CI/CD), ask how deployment scripts, validation checks, and release approvals connect to your existing pipeline. Establish whether containers, Kubernetes, or serverless services fit your operational capabilities.
8. Pricing, Support, and Commercial Requirements
Model the commercial agreement around successful adoption. Your forecast should show what happens when more customers use analytics frequently and expect more from it.
- Licensing and Pricing Model
Identify what increases the bill under each proposed model:
| Model | What to clarify |
|---|---|
| Per user or viewer | Registered versus active users, authors, viewers, and external-user minimums |
| Per tenant | How customer accounts, subsidiaries, and test tenants are counted |
| Capacity-based | Included capacity, concurrency limits, and upgrade thresholds |
| Usage-based | Which queries, API calls, exports, or AI interactions are metered |
| Compute-based | Resource charges, autoscaling behavior, and idle capacity costs |
| Flat-rate or platform licensing | Included capabilities, deployment rights, and separately billed services |
The right model depends on usage patterns. Scrutinize per-user and metered charges when analytics will reach a large external audience.
Qrvey offers flat-rate licensing with unlimited tenants and users, helping teams forecast license costs as adoption grows. Model infrastructure and other operating costs alongside that license.
- Three-Year Total Cost of Ownership
Forecast today’s deployment, expected growth, and a higher-adoption scenario across three years. Include licensing, cloud infrastructure, warehouse queries, data pipelines, additional environments, professional services, internal engineering, and ongoing administration.
Account for migration and upgrade work as well as launch costs. Use the workload assumptions from performance testing so the technical and commercial evaluations describe the same deployment.
Qrvey’s built-in Data Management Layer lets SaaS teams ingest, transform, and optimize multi-tenant data, reducing the time and cost of building custom pipelines. It supports both live and managed (synced) datasets, with synced data helping offload computation and minimize expensive, repeated queries to external warehouses such as Snowflake.
Its flat-rate licensing also includes development, testing, and production environments, so your team can maintain separate stages of its CI/CD pipeline without additional environment licensing fees.
- Implementation and Support Model
Clarify who helps with architecture, integration, migration, launch, upgrades, and production incidents. Establish responsibilities, support hours, escalation routes, and response commitments for critical issues.
Request references from SaaS customers with similar tenant complexity. Ask how the vendor supported them after launch and whether advisory services can help with adoption, analytics packaging, and monetization as requirements evolve.
Qrvey’s onboarding services provide platform training, architecture guidance, and implementation assistance through your first production launch.
Its engineer-led Premium Support helps with troubleshooting and optimization.
For ongoing work, the optional Technical Advisory Service provides an assigned expert through an annual subscription, supporting initiatives such as tenant onboarding automation, platform upgrades, and content deployment.
- Contract and Scaling Considerations
Document renewal terms, annual increases, minimum commitments, overages, feature entitlements, and expansion rights. Confirm whether new products, regions, customer tiers, or AI capabilities change the agreement.
Finally, establish your exit requirements: access to data, exportable content and metadata, transition assistance, and deletion obligations. Your contract should support both the growth you expect and the flexibility you’ll need if your requirements change.
The Embedded Analytics Requirements Checklist
Use this checklist during vendor demos or proofs of concept. Assign Must-have/Should-have/ Nice-to-have to each blank priority cell. Tenant isolation is premarked for multi-tenant products. Copy the table for each vendor and record supporting evidence alongside it.
| Category | Requirement | What to Verify | Your Priority |
|---|---|---|---|
| Product | Product scope | Demonstrate personas, tier entitlements, and tenant-specific configurations. | |
| Data | Source connectivity | Connect actual sources; test updates, deletions, and schema changes. | |
| Data | Data access architecture | Compare live/synced freshness, query costs, and source load. | |
| Data | Semantic modeling | Reconcile one metric across dashboards, reports, and AI. | |
| Security | Tenant isolation | Switch tenants; verify dashboards, drill-downs, exports, and AI remain isolated. | Must-have |
| Security | Record/column controls | Attempt restricted-record access and sensitive-field exports. | |
| Security | Permission inheritance | Change an application role; verify analytics access updates. | |
| Security | Content isolation | Attempt opening another tenant’s dashboards and saved queries. | |
| Security | SSO/authentication | Test sign-in, token expiry, and revoked-user access. | |
| Embedding | Embedding method | Place a chart inside an existing product screen. | |
| Embedding | White-label depth | Inspect builders, exports, emails, URLs, and AI branding. | |
| Self-service | Authoring | Create and share dashboards using commingled tenant data. | |
| Self-service | Pixel-perfect reporting | Generate a branded, multipage report; test scheduled delivery. | |
| Self-service | AI governance | Request restricted data and unauthorized agent actions. | |
| Self-service | Workflow automation | Trigger an alert; test permissions, failures, and retries. | |
| Performance | Concurrency | Run peak workload alongside a heavily active tenant. | |
| Performance | Query performance | Measure agreed percentiles with warm and cold caches. | |
| Deployment | Hosting model | Confirm regions, data location, and operational responsibilities. | |
| Deployment | Environment management | Test separate development, staging, and production configurations. | |
| Deployment | CI/CD/content migration | Promote dependent assets; test rollback and tenant customizations. | |
| Deployment | Tenant provisioning | Automate a tenant’s datasets, dashboards, and permissions. | |
| Commercial | Pricing model | Calculate charges at projected users, tenants, and usage. | |
| Commercial | Three-year TCO | Include infrastructure, pipelines, services, engineering, and administration. |
For an interactive comparison, Qrvey’s Evaluation Center includes a vendor scorecard focused on SaaS analytics requirements.
Do Your Requirements Point to Build or Buy?
Use your documented requirements to compare three routes: building with AI coding tools, embedding general-purpose BI, or adopting a purpose-built embedded analytics platform. Evaluate each against your must-haves before comparing costs.
An internal build gives you the most control. Even when AI coding tools generate all the code, your team owns data processing, visualizations, tenant security, self-service, AI, performance, releases, and maintenance. AI-generated analytics still requires engineering oversight to maintain secure, reliable infrastructure as tenants, permissions, and workloads change.
The typical trade-offs are:
| Consideration | Build with AI coding tools | General-purpose BI | Purpose-built embedded analytics |
|---|---|---|---|
| Time to launch | Fast prototypes; production hardening remains | Faster with an existing deployment | Faster when requirements fit |
| Engineering ownership | Full stack and operations | Integration and tenant configuration | Product integration and configuration |
| Product control | Highest; customize every layer | Within embedding capabilities | Within APIs and component capabilities |
| Multi-tenant work | Build and maintain controls | Validate and adapt tenant model | Configure and test native controls |
| Ongoing maintenance | Entire analytics platform | Vendor maintains core; you maintain integration and deployment | Vendor maintains core; you maintain integration and deployment |
| Best fit | Specialized requirements; sustained engineering capacity | Existing BI; standardized customer reporting | SaaS self-service, white labeling, and scale |
Building can make sense when differentiated analytics justifies ongoing investment. Buying shifts some responsibilities to a vendor, while retaining integration work and platform constraints. Qrvey, for example, lets teams create analytical assets with AI within an established tenant security and governance framework.
Use the detailed build-versus-buy guide and ROI calculator to compare integration, licensing, infrastructure, maintenance, and opportunity costs over three years.
Common Mistakes When Defining Embedded Analytics Requirements
Incomplete requirements can lead to security gaps, unexpected costs, and extra engineering work after launch. Watch for these common mistakes as you evaluate your options.
- Letting vendor demos define the requirements: Evaluating impressive features instead of your own customer workflows can leave essential needs unsupported, creating unplanned engineering work after purchase.
- Treating multi-tenancy as a checkbox: RLS support alone does not prove tenant isolation across dashboards, exports, APIs, and AI answers. Overlooking these access paths can leave security gaps your team must identify and fix.
- Designing for today’s adoption: Licensing and infrastructure that suit 200 users may become expensive or slow at 20,000. Failing to evaluate projected usage can force architectural changes just as adoption grows.
- Assuming all embedding is equivalent: A dashboard iframe and JavaScript components offer different integration options. Choosing an approach that cannot support your UX requirements creates customization work and inconsistent experiences. Qrvey uses JavaScript components and does not support iframe embedding.
- Ignoring self-service governance: Allowing customers to create datasets, share dashboards, or use AI without consistent permission boundaries can expose restricted information and increase the support burden.
- Calculating license price instead of TCO: Leaving out data infrastructure, engineering, administration, and warehouse usage understates the investment. The lowest software quote can become the more expensive option to operate.
How Qrvey Maps to These Embedded Analytics Requirements
Qrvey brings customer-facing analytics and the infrastructure behind it into one platform, helping SaaS teams reduce custom development while giving customers more control over their reporting.
Qrvey also ranked #1 in Dresner Advisory Services’ Embedded BI Market Study in both 2024 and 2025. These rankings reflect customer assessments of embedded capabilities and architecture, adding evidence from production users to your evaluation.
Apply your checklist to a representative customer workflow, tenant model, and expected workload. Ask which requirements Qrvey meets through configuration, which need integration work, and which depend on your data architecture or licensing tier. Document any gaps and the effort needed to close them. This gives product and engineering teams a shared view of what implementation will involve.
Book a Qrvey demo to review your priorities with an analytics expert and see how the platform fits your product.
FAQs
Product, engineering, data, security, customer success, and commercial teams should all contribute. Each group sees different requirements, from customer workflows and integration constraints to security expectations, support needs, and how analytics will be packaged or monetized.
Ideally, requirements should be documented before vendors are shortlisted or proofs of concept begin. Defining them early helps teams evaluate platforms against real product needs instead of allowing vendor demonstrations to determine what appears important.
Requirements should be specific enough to test. Instead of writing “supports self-service,” for example, define which users should be able to create reports, which datasets they can use, what they can share, and what permissions must remain in place.
They often are. Enterprise customers may introduce additional requirements around branding, data isolation, identity integration, deployment, auditability, contractual SLAs, or customer-specific configurations that smaller customers may not need.
Review them as your SaaS product, customer base, data architecture, and analytics usage evolve. Requirements established for an initial launch may no longer be sufficient once customers adopt more self-service capabilities, AI features, additional data sources, or higher-volume workloads.
Yes. Qrvey can support different combinations of analytics content, permissions, self-service capabilities, datasets, and configurations across customer groups. This can help SaaS teams align the analytics experience with different product tiers or customer requirements without maintaining a completely separate analytics platform for each one.
Teams can map their documented requirements against a representative Qrvey implementation and test them using realistic customer workflows, tenant models, datasets, permissions, and expected workloads. The goal is to identify which requirements Qrvey satisfies through configuration, which require integration work, and which depend on the surrounding architecture or commercial setup.
David is the Chief Technology Officer at Qrvey, the leading provider of embedded analytics software for B2B SaaS companies. With extensive experience in software development and a passion for innovation, David plays a pivotal role in helping companies successfully transition from traditional reporting features to highly customizable analytics experiences that delight SaaS end-users.
Drawing from his deep technical expertise and industry insights, David leads Qrvey’s engineering team in developing cutting-edge analytics solutions that empower product teams to seamlessly integrate robust data visualizations and interactive dashboards into their applications. His commitment to staying ahead of the curve ensures that Qrvey’s platform continuously evolves to meet the ever-changing needs of the SaaS industry.
David shares his wealth of knowledge and best practices on topics related to embedded analytics, data visualization, and the technical considerations involved in building data-driven SaaS products.