DataConsultant helps product, data, and technology teams plan, design, integrate, govern, and improve analytics inside SaaS products, customer portals, partner platforms, and operational applications. The service connects reliable data, secure access, intuitive user experiences, and scalable analytics architecture so users can act on information without leaving the workflow.
Illustrative interface and figures only; not client results.
An embedded analytics service designs and delivers dashboards, reports, alerts, metrics, and guided analysis inside an existing digital product or business application. It covers the user experience, data model, integration method, security, tenancy, performance, governance, rollout, and ongoing operation required to make analytics a dependable part of the product rather than a separate BI destination.
Users often have to export data, open a separate BI tool, wait for a report, or rely on analysts for routine questions. Embedded analytics places relevant insight where a decision is already being made.
The design should reflect the user’s decision, context, entitlement, and workflow rather than simply placing a generic dashboard inside an iframe.
Account performance, usage, billing, service levels, outcomes, benchmarks, and exceptions for customers.
Tenant-aware dashboards, interactive reports, alerts, exploration, and premium analytics features.
Contextual KPIs and decision support within sales, service, logistics, finance, or workforce workflows.
Governed scorecards and performance views for distributors, suppliers, franchisees, and channel partners.
A reliable implementation connects trusted data, governed metrics, secure analytics services, and a product experience that matches the organisation’s identity and release model.
Sources, pipelines, quality controls, models, metric definitions, metadata, refresh patterns, and lineage.
BI engine, tenant context, row-level security, caching, audit logs, usage limits, and platform administration.
Navigation, filters, visual hierarchy, accessibility, white-labelling, responsive behaviour, alerts, and user guidance.
Deliverables are selected according to whether the engagement is advisory, assessment-led, implementation-focused, or a managed service.
| Deliverable | What it covers | Decision or outcome supported |
|---|---|---|
| Use-case and user-needs brief | Personas, decisions, workflows, information needs, priority features, and acceptance criteria | Confirms product value and scope |
| Architecture and platform recommendation | Embedding method, hosting, capacity, integration, identity, tenancy, environments, and dependencies | Supports technology and investment decisions |
| Analytics UX specification | Page structure, metric hierarchy, filters, drill paths, accessibility, states, and responsive behaviour | Guides design and development |
| Semantic model and metric catalogue | Entities, measures, definitions, ownership, calculation logic, quality rules, and lineage | Creates consistent trusted analytics |
| Security and tenancy design | Authentication, authorisation, row-level controls, tenant isolation, audit, and exception handling | Protects data and supports assurance |
| Implemented analytics components | Configured reports, dashboards, APIs, SDK integration, styling, and product navigation | Provides a working embedded capability |
| Test and release evidence | Functional, security, performance, accessibility, data reconciliation, and user-acceptance results | Supports release approval |
| Operating model and runbook | Ownership, support, monitoring, incident handling, change control, capacity, and enhancement process | Enables sustainable operation |
The stages are adapted to the product, maturity, regulatory context, and delivery responsibility. Fixed timelines are not assumed before discovery.
Clarify users, decisions, commercial goals, workflows, constraints, and measures of success.
Primary output: agreed use cases and scopeReview sources, models, APIs, identity, tenancy, architecture, release processes, and control requirements.
Primary output: readiness findings and dependency logDefine information architecture, visual patterns, user journeys, metric hierarchy, and accessibility needs.
Primary output: experience specification and prototypeSelect the platform and embedding pattern, then define semantic, security, capacity, and operating architecture.
Primary output: solution design and control modelConfigure analytics, integrate the product, test data, security, performance, accessibility, and user acceptance.
Primary output: release-ready embedded analyticsTransition ownership, monitor reliability and adoption, manage capacity, and prioritise enhancements.
Primary output: runbook, KPI baseline, and improvement backlogSelection should be based on product needs, security, tenancy, UX flexibility, capacity, cost, data architecture, skills, and vendor risk—not brand preference alone.
Technology references are illustrative. Suitability, licensing, availability, and security should be validated against current vendor documentation and organisational requirements.
Evaluate suitability, readiness, platforms, architecture, risks, effort, and a prioritised implementation plan.
Provide product, data, architecture, security, UX, and governance guidance alongside internal teams or vendors.
Design, configure, integrate, test, release, document, and transfer an embedded analytics capability.
Operate, monitor, support, govern, optimise, and enhance the capability against agreed service measures.
A responsible estimate requires a defined scope. Platform licences are only one part of total cost.
Product owners, user representatives, commercial stakeholders, and decision-makers who can clarify priorities and approve trade-offs.
Architecture, source information, metric definitions, APIs, environments, identity patterns, release processes, and relevant technical specialists.
Security, privacy, legal, risk, compliance, procurement, and audit stakeholders where the scope or data requires their review.
Share your product, users, data landscape, platform constraints, tenancy model, and desired outcomes. DataConsultant will recommend an appropriate assessment, design, implementation, or managed-service approach.
Embedded analytics places dashboards, reports, metrics, alerts, or interactive analysis directly inside a customer portal, SaaS product, operational application, or partner platform. Users can access relevant insight within the workflow instead of opening a separate BI tool.
Scope can include discovery, product and user research, use-case prioritisation, analytics UX, data modelling, semantic-layer design, platform selection, security, multi-tenancy, API or SDK integration, white-labelling, testing, governance, rollout, training, support, and managed optimisation.
Typical buyers include chief product officers, product managers, founders, CTOs, CIOs, heads of data, analytics leaders, application owners, customer-experience leaders, and procurement teams. Security, privacy, architecture, finance, and legal teams may also participate.
A normal BI dashboard is usually accessed through a separate analytics environment. Embedded analytics is integrated into another product and must account for product navigation, identity, tenant context, entitlements, white-labelling, application performance, release management, support, and customer-facing reliability.
The decision depends on UX flexibility, time to market, analytical depth, skills, security, tenancy, licensing, capacity, maintainability, accessibility, and roadmap needs. A hybrid approach may use a BI engine for governed analysis and custom components for tightly controlled product experiences.
Yes. A suitable design can use tenant-aware authentication, row-level or object-level security, isolated models where necessary, entitlement rules, usage controls, audit logging, and testing that confirms users cannot access another tenant’s data.
Options may include Microsoft Power BI Embedded, Tableau Embedded Analytics, Looker, Qlik, ThoughtSpot Embedded, Sigma, Amazon QuickSight Embedded, or custom visualisation libraries. Selection should be validated against current vendor capabilities, licensing, regional availability, security, and product requirements.
There is no reliable fixed duration before discovery. Timing depends on use-case count, data readiness, semantic-model complexity, platform selection, identity and tenancy, custom UX, performance targets, testing, release governance, and stakeholder availability.
Cost is influenced by user and tenant volumes, platform licensing, concurrency, data architecture, refresh frequency, custom design, integration effort, environments, security controls, testing, support, and ongoing enhancement. DataConsultant can provide a scope-led estimate after initial discovery.
The design can address authentication, authorisation, tenant separation, least privilege, encryption, audit logging, data minimisation, retention, residency, monitoring, incident handling, and third-party risk. Requirements should be approved by the organisation’s authorised security, privacy, legal, and compliance specialists.
Often yes, but the level of control varies by platform and licence. White-labelling may include colours, typography, logos, navigation, URLs, loading states, error messages, email alerts, exports, and help content. Accessibility and maintainability should remain part of the design.
Measurement can include eligible versus active users, repeat usage, feature reach, time spent, filter and drill behaviour, exports, alert subscriptions, task completion, support queries, cohort retention, customer feedback, and whether users act on the information presented.
Yes. DataConsultant can provide assessment, design authority, architecture, data modelling, governance, implementation support, testing, assurance, or managed operation alongside internal teams, software vendors, systems integrators, and product-development partners.
Post-launch work may include monitoring reliability and capacity, reviewing adoption, reconciling metrics, managing access, triaging incidents, updating content, improving performance, adding use cases, controlling releases, and maintaining documentation and ownership.
