Embedded analytics have evolved from a nice-to-have feature to a board-level requirement. By allowing executives to access data insights within the working environments where they already spend their time, embedded analytics is fast becoming a necessity for steering mission-critical decisions.  

“To make business analytics more accessible and relevant, organizations have been striving heavily to put analytics in the context of their business applications and workflows rather than treat it as a separate set of tools and functions running in its own sandbox,” says Avi Perez, Chief Technical Officer of Pyramid Analytics.  

Perez cautions that organizations need to evaluate their needs to ensure a good fit. “The process of embedding analytics is now a top-tier demand, and it comes with its own set of problems and complexities,” he adds.  

To address these concerns, here are nine questions for organizations evaluating their options to ensure success in mission-critical applications of these technologies. 

1. Who are the primary users of the embedded analytics features?

Identify whether the audience is internal staff, end-customers, or both, to clarify the design persona. In addition, here’s where multi-tenancy will play an important part, which involves protecting data across both internal and external users – a challenge that might not be considered in use cases that are solely internal. 

Each of these factors carries distinct UX requirements. Operations teams require granular tables, while clients prefer concise KPIs and guided narratives. Map these personas to data literacy levels, device preferences, and time-to-insights expectations before even writing a single line of code or considering a solution to embed into your platform. You can validate these assumptions with user interviews or analytics from your existing dashboards. 

A recent study by Dresner Advisory Services points to differing priorities according to organizational role. Data science, sales and marketing, and information technology departments each had their priorities and needs: contextual insights for internal users for data scientists, external-facing objectives for sales and marketing, and efficiency for IT. 

2. What decisions will analytics features empower users to make?

How will your embedded analytics deployment be useful to the people who engage with it? According to a report by Aberdeen, 37% of business leaders say more data is available than can be used for meaningful analysis. 

Start with the operational aspects, according to user needs. Are you spotting supply chain delays, identifying churn risk, and the like? Then, translate these into concrete metrics, filters, and visualizations. Try to avoid feature creep, or tagging every requested chart just to address all outcomes. You can differentiate between essential and nice-to-have visualizations, which can be turned on once the organization has proven the advantage of adopting embedded analytics. 

3. Should we build our own solution or use a third-party embedded analytics platform?

Building in-house maximizes control, but will involve higher initial expenditure and might result in higher maintenance costs, too. According to Full Scale, third-party tools can ensure faster time-to-market than building from scratch, from a year to as quickly as one quarter in deployment. However, vendor lock-in and per-seat pricing can also be costly as usage scales. A build approach can save 35% in the long term, but a buy approach can save 45% in the short term. 

To address this, prototype a slim build of your hardest-to-model visualization. If you cannot deliver a maintainable version in just a few agile sprints, an off-the-shelf solution may be justified, with your own practical customizations. It may not be that simple, however. Other considerations apart from cost and time-to-market can include security, integration, scalability, and technology, as part of the total impact of a build vis-a-vis buy decision.

4. What data sources and pipelines need to be integrated?

According to a study by IDC, businesses face an average of 12% revenue loss due to data fragmentation and silos. Analytics can amplify bad data as it can the good, possibly resulting in garbage in, garbage out outcomes. 

List all tables, APIs, and flat files that feed your app’s core workflows to create an integration inventory. Check whether data is siloed in legacy solutions or trapped in silos that lack export APIs. If batch ETL adds hours of delay, consider event-streaming or change data capture to maintain freshness, to ensure the architecture aligns with real-time demands. However, you would have to budget time for data-quality rules to account for latencies. 

5. Can our existing app architecture support this integration? 

Audit frontend frameworks for component reuse. React or Angular apps can host embedded dashboards through iframes or component libraries. In contrast, vanilla JSP may need heavy refactoring. Measure current API response times and memory headroom.  

Visual queries often multiply backend load when filters stack. If you run microservices, isolate the analytics engine to limit load in your main platform during spikes to add resilience.

6. How will we handle user-level data security and access control in a multi-tenant environment?

According to Verizon’s recent Data Breach Investigations Report, misdelivery, misconfiguration, and publishing errors are the top reasons for security compromises. Meanwhile, privilege misuse also accounts for 10% of such security breaches. 

Assume multi-tenancy by default. B2B customers increasingly expect a single logical instance with tenant isolation, aligning with SaaS norms. Implement attribute-based or row-level security so that users only see rows tagged with their tenant ID or role. This enforces the concept of least privilege. You can also automate policy tests in CI to avoid regressions, ensuring access control is continuously implemented in the development cycle. 

7. What visual and interaction experiences do our users expect?

Dashboards are a staple in business environments. Downloadable CSV or PDF reports are non-negotiable for finance and audit teams to meet their compliance needs. Include in-context tooltips and “why it matters” annotation layers, as contextual analytics improves feature adoption.  

Mobile-first loading and pinch-to-zoom charts are essential if your app sees at least 30 percent mobile traffic. Test for load speeds – according to Google, 53% of visits are abandoned if a mobile site or app takes longer than three seconds to load. 

8. How scalable and performant does our analytics need to be?

Model best-case and worst-case workloads. If concurrent query volume doubles during month-end, for instance, the data analytics dashboard needs to be able to handle these peaks.  

Plan for horizontal scaling – columnar warehouses, result caching, and async rendering can cut lag from seconds to milliseconds to keep the UX snappy. Measure service-level objectives against render time and query cost to avoid surprise cloud utilization spikes, which can have an impact on your organization’s budget. 

9. How will analytics be maintained and updated post-integration?

Without ownership, projects tend to get abandoned beyond proof-of-concept. Gartner predicts that at least 30% of generative AI projects will be abandoned at this stage. 

Define ownership upfront. Product owns the roadmap, engineering owns the pipelines, and data teams own the semantic models. This avoids orphaned dashboards. Schedule quarterly schema reviews. Feature rollouts often require new measures or dimensions. Automate regression tests on visuals so version bumps in libraries don’t break embedded widgets. Finally, publish a changelog or in-app banner when KPIs change. Nothing erodes stakeholder trust faster than silent metric shifts.

The Takeaway

Embedding analytics can unlock new revenue, reduce churn, and help users make data-driven calls without leaving your app. Yet every benefit stems from clear answers to the questions above. Start small: pilot with one persona, one decision flow, and one well-governed dataset. Measure adoption, iterate on UX, and only then expand to additional tenants and use cases, to ensure disciplined scaling. By treating embedded analytics as a product, not a project, you’ll turn data into a durable competitive advantage rather than a perpetual backlog item.