embedded analytics.
Embedded analytics places dashboards and reports inside another application, so users see analytics in the product they already use rather than logging into a separate BI tool.
Where embedded analytics fits on a long engagement.
Embedding is usually driven by adoption. Analytics in a separate tool with a separate login is used by a small fraction of the people who would benefit; the same charts inside the product are used by most of them.
Multi-tenant isolation is the hard requirement. A customer seeing another customer's data is a serious incident, and the isolation must be enforced at the data layer with authentication passed through correctly. This is security engineering, not report configuration.
What an assigned team does with embedded analytics.
Performance expectations differ sharply from internal BI. An analyst tolerates a ten-second query; a customer inside a product does not, and embedded analytics that is slow reflects on the product rather than on the BI tool.
Meeting that usually means pre-aggregation and caching designed in from the start, which is why the work is scoped with data engineering outsourcing alongside the application capacity.
What we use embedded analytics for.
- Analytics people actually use Insight inside the product rather than behind a second login.
- Tenant isolation enforced at the data layer Access proven rather than assumed, because a leak between customers is severe.
- Product-grade response times Pre-aggregation and caching designed in, because customers will not wait.
How embedded analytics capacity is assigned.
Embedded analytics is assigned across application and data capacity on one agreement, since isolation and performance sit on both sides of the boundary.
Tell us what your roadmap needs embedded analytics for.
A service delivery manager replies with the disciplines we would assign, the monthly capacity and what the first month looks like.
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