How To Choose Embedded Analytics Platforms for Pharmaceutical Clients

This has never happened to us before.

Even before Vikram, CTO at Datacubed Health, bought Holistics, he suggested a partnership.

“I’m happy to co-market with you guys," Vikram said. "Not for any price discounts, we want to do this from a collaborative sense that we care about the interactivity and security of our preferred embedded BI tool.”

The offer came out of a deeper frustration.

Their previous embedded BI tool, built long before the cloud went mainstream, had forced their team into endless workarounds. Basic requirements turned into custom engineering projects. Routine upgrades broke critical dashboards.

With Holistics, we saw how different embedding could be. Embedding was built into your product from the start, whereas for most companies, it’s an afterthought.

It was a customer success call, and it was supposed to be 45 minutes, but we went on for 3 hours, learning about the state of analytics in the pharmaceutical and healthcare technology world.

We learned that, whether you’re building an eClinical platform like Datacubed, managing clinical data for a biotech partners, or embedding dashboards into a health-tech SaaS product, the expectations are uniquely high (compliance, security, data isolation, performance, and more), and choosing the right embedded platforms for pharma users is uniquely hard:

• If you’re an eClinical, health tech SaaS, or CRO company working with pharmaceutical clients, you probably face complex embedding needs: HIPAA and GDPR compliance, data isolation, multi-tenant access control, and performance at scale.
• If you’re a pharmaceutical division or biotech firm managing internal analytics across research and clinical data, your needs center more around governance, data lineage, and workflow integration.

In this piece, we’ll focus primarily on the first segment, eClinical and health tech SaaS providers building analytics for pharmaceutical partners, based on what we learnt from working with our health tech customer.

How to Choose the Right Embedded Analytics Platform for Pharma

The difference between a “good” analytics platform and a great one, unfortunately, reveals itself only after deployment; when a client’s compliance team starts asking questions, or when an upgrade suddenly breaks every embedded dashboard in production. You want to avoid these moments, and here's how:

1. Compliance and Security

If your product serves pharmaceutical or clinical research clients, clicks have legal implications. One misconfigured dashboard or shared data set can put your customer and your reputation, at risk.

What to look for:

• Region-based data isolation: Make sure your platform supports physical or logical separation of data (e.g., data in Germany stays in Germany, data in US stays in the US).
• HIPAA, GDPR, and SOC 2 alignment: You shouldn’t have to engineer compliance from scratch; your BI vendor should already have it baked in.
• Granular access control: Role-based access control and row-level permissions, ideally controllable through APIs, to enforce dynamic restrictions.
• Audit logging: Complete traceability of who viewed, queried, or exported what data, with the option to disable exports.
• No-Persistence Security Architecture: The tool shouldn't persist raw customer data on its servers. All queries should run live against your customer’s own warehouse, and only temporary query results are cached, automatically expiring after a set period.

2. Reliability and Performance

If you’ve ever seen a BI demo, you know the scene: fast dashboards, beautiful visuals, zero lag. Then you deploy it in production, and every query runs 30 seconds slower.

Well, it's because the demo wasn’t running live queries against 500 million rows of clinical data.

Pharma-scale analytics require query optimization, caching, and smart pushdown to your warehouse. If your platform doesn’t handle that gracefully, you’ll pay for it in performance and credibility.

What to look for:

• Query optimization: Native support for query pushdown and smart aggregation caching (e.g., BigQuery BI Engine, Snowflake cache).
• Data freshness indicators: Visual cues that tell users when data was last updated.
• Error alerting and monitoring: Built-in alerts when dashboards fail or queries time out.
• Load testing and sandboxing: The ability to test performance under simulated client load before rollout.
• Multi-environment reliability: Flexible deployment across dev, staging, and production environments with git-based code synchronization across regions.

3. Usability and Brand Experience

In regulated industries, it’s easy to obsess over compliance and forget about the end user. But user experience can be your differentiator.

Pharmaceutical clients expect professional-grade interfaces; dashboards that feel like part of your platform, not a bolt-on. That’s where customization and embedding depth come in.

What to look for:

• White-labeling: Custom CSS, custom themes, fonts, color palettes, and domain masking for a seamless experience.
• User roles: The ability to create “viewer-only” roles for external users and “builder” roles for internal analysts.
• Self-service controls: Drilldowns, filters, and ad hoc exploration without leaving your product’s environment.
• Embedded AI Chat: Let your customers query data using plain English and delight them with quick, easy data exploration with AI.

A clean, branded analytics experience tells your clients you take data integrity seriously, even before they read the numbers.

4. Maintainability

Your embedded analytics tool should handle the heavy lifting for your engineers. Less maintenance is always better. This frees the BI team to focus on delivering insights instead of maintaining the platforms.

That said, this is not easy to come by. Many BI tools started as internal analytics platforms. They added embedding later as an afterthought, a “nice-to-have” bolted on top of a GUI-centric workflow.

That’s where things start to break. In these setups, every new client dashboard becomes a copy. Every schema change triggers a rebuild. Upgrades risk breaking dependencies. Before long, the data team becomes a maintenance team.

What to look for:

• Analytics-as-code: The tool should let you define datasets, metrics, and dashboards as code, making them Git version-controlled, reusable, and deployable.
• Git integration: You should be able to branch, test, review, and merge changes — exactly like your engineering team does.
• CI/CD-style deployment: The ability to test dashboards in staging, validate data consistency, and promote changes safely to production.
• Dynamic environment syncing: Automated promotion of analytics logic between multiple client environments or regions.

5. Embedded-friendly Economics

Pricing is where many embedded BI relationships break down. Named-user licenses make sense for internal teams, but not for B2B SaaS or CRO models serving multiple clients. This was what happened to our customer, Datacubed, whose customers primarily accessed dashboards through embedded portals. Even though hundreds of pharma users viewed reports, only a handful ever logged into the BI tool directly.

Yet, Datacubed still had to pay for expensive user licenses, and each additional environment (development, QA, UAT, production) carried a steep cost.

What to look for:

• Usage-based pricing: Costs tied to query execution or compute time, not headcount.
• Tiered environments: Lower-cost pricing for dev and staging environments.
• Unlimited viewers: Essential for pharma-facing portals where you can’t predict how many end users will log in.
• Predictable billing: Clear visibility into query costs and performance usage metrics.

6 Best Embedded Analytics Tools for Pharmaceutical Companies and Clients

1. Holistics

Holistics was built around one specific dilemma, often met by product engineers when customers ask for data: Buy or Build?

Traditionally, you have 2 options:

• Build: You build analytics in-house. You’ll have full control and unlimited customization, but it burns engineering resources (and $$$), pulling your product and engineering teams off other key initiatives.
• Buy: You buy an off-the-shelf BI tool. It’s faster at first, but most weren’t built for customizable embedding or software-grade governance. They box you with rigid UX, and don’t align with your engineering team’s need for Git-based workflow.

Holistics Embedded gives you the best of both worlds.

It’s a programmable embedded analytics platform that lets you embed dashboards, deliver AI-driven insights, and scale governance, all while keeping your brand and data integrity intact.

Core Embedded Analytics Features:

• Self-serve interface for users: Let customers build their own reports with AI, or drag-n-drop report builder right inside your product.
• Robust customization: Full CSS control, custom visualizations, custom themes, and a canvas dashboard for a fully branded experience.
• Accurate, governed metrics: Ensure metric consistency with a semantic modeling layer that defines business logic once and reuses it everywhere.
• Programmable, maintainable workflow: All datasets, dashboards, and metrics are defined as code, version-controlled in Git, so that product engineers can deploy safely and easily.
• Enterprise-grade Security: Protect customer data with SOC 2, HIPAA, and GDPR compliance, audit logging, and fine-grained access controls (RBAC and row-level permissions).
• Reusable components: Parameterize your reporting widgets and dashboard components using variables, and reuse them anytime, anywhere.
• Usage-based pricing: Scales by query volume, not user count, ideal for SaaS and CRO models.

Notable healthtech/pharmaceutical customers: Datacubed Health, MedEnterprises, Oxbridge Health, Perfect Rx, and SmartScripts.

Pricing: Starts at $800/month for unlimited viewers.

2. Sisense

If Holistics represents the new generation of embedded-first BI, Sisense is the veteran that proved the model could work at scale.

In the pharmaceutical context, Sisense has a history. Many large life sciences and healthcare organizations have used it to embed dashboards into portals for clinicians, researchers, and CRO partners. Its strength lies in enterprise-grade reliability, strong governance, and a mature ecosystem of integrations.

But that maturity comes with trade-offs. Several eClinical and health tech startups that once adopted Sisense have since cited complexity, cost, and legacy architectural friction as pain points.

Core Embedded Analytics Features:

• Sisense Fusion Embed: Deep integration options using SDKs, APIs, and iFrames for full control of dashboards inside applications.
• AI & Natural Language Features: The “Simply Ask” module lets users query data conversationally, though in regulated settings, accuracy must still be closely monitored.
• Strong Governance: Strong RBAC and row-level security, plus audit logging and HIPAA/GDPR-ready deployment options.
• Alerting & Workflow Automation: Sisense Pulse can trigger alerts or external workflows when metrics cross thresholds.

Notable Customers: Philips Healthcare, Johnson & Johnson.

Pricing: Enterprise-oriented. Typical deployments range from $56,000 to $75,000 per year, depending on users, environments, and query load.

3. Domo

Domo’s biggest strength lies in its end-to-end architecture. It ingests data, transforms, automates, and distributes it through a unified, cloud-native stack. That makes it attractive to large pharma divisions or CROs looking for an “all-in-one” data environment without having to stitch together multiple tools.

Core Embedded Analytics Features:

• Domo Everywhere: Enables secure, branded embedding of dashboards into partner portals and applications.
• Magic ETL & Dataflows: Built-in tools for data transformation and modeling without heavy coding.
• App Studio: Let teams build custom data apps and interactive dashboards tailored for internal or external users.
• Governance and Security: HIPAA-ready, SOC 2–compliant, and supports granular role-based access.
• Collaboration and Alerts: Real-time alerting and commenting features foster engagement around shared data.

Notable Customers: GE Healthcare, Regional One Health, Dr Consula

Pricing: Typically $25,000–$100,000/year, depending on data volume, connectors, and embedding use cases. Learn more about Domo pricing here.

4. Power BI Embedded

Few tools in the analytics world are as instantly recognizable as Power BI. For pharmaceutical enterprises already invested in Microsoft’s ecosystem like Azure, SQL Server, or Office 365, Power BI feels like a natural extension. It’s powerful, well-documented, and battle-tested at scale.

In large pharmaceutical divisions, Power BI often starts as the internal reporting tool of choice. Its integration with Active Directory, Azure Data Lake, and Microsoft Fabric makes governance and user management straightforward for IT-led teams. The platform’s visual layer, combined with data modeling through Power Query and DAX, allows for complex analytics ranging from clinical trial performance to supply chain optimization.

Core Embedded Analytics Features:

• Power BI Embedded (Azure): Lets developers embed reports securely into web apps or portals via Azure capacity.
• Row- and Column-Level Security: Granular access control aligned with Microsoft’s enterprise security model.
• Integration with Azure Synapse and Fabric: Streamlined data movement within Microsoft’s cloud stack.
• AI Visuals and Q&A: Users can query dashboards in natural language with contextual suggestions.
• Compliance Credentials: HIPAA, GDPR, and SOC 2 alignment via Azure cloud infrastructure.

However, Power BI’s architecture comes with real trade-offs for embedded use. It doesn’t offer an embedded report builder, meaning external users can view dashboards but not create or edit their own within the embedded environment. Moreover, true multi-tenant isolation requires complex Azure configuration, and premium capacity licensing drives costs up quickly.

Pricing: Start at $9,000/year. Power BI Embedded starts at $735.91/month for an A1 node (1 vCore) and increases with node type and counts.