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Data Formulator 0.7 adds persistent enterprise data connections

Microsoft Research’s analytics release combines reusable data connectors, context-aware agents and a workspace for branching visual analysis.

Announcement: · From Microsoft Research

Microsoft Research released Data Formulator 0.7 on May 28, extending its open-source analytics system with reusable data connections and a workspace for iterative analysis. The release overview combines connection management, agent-assisted exploration and visualization editing.

Analysis retains its working context

The Data Connectors feature supports persistent connections across several data-source types. Context-aware agents can inspect the workspace, prepare data and generate charts. A Data Thread records analytical steps and supports branching into alternative investigations, while an interactive canvas lets users refine visual presentation.

Microsoft positions the release as a foundation teams can adapt to their own enterprise requirements, rather than a single chat response that disappears from the workflow.

Keep the calculation reviewable

Our suggested adoption test starts with a question whose correct answer is already known. Follow the generated analysis from source rows through transformations to the displayed chart. A convincing graphic should not substitute for checking a join, aggregation or filter.

Include ambiguous business terms in the test. If a request asks for “active customers,” the application should obtain or expose the definition rather than silently choose one. Keep that definition visible beside the result so a later viewer understands what was counted.

Test the shared-workspace boundary

Reusable connections reduce repeated setup, but teams should still verify which data each user and agent can access. Use a test account with deliberately limited permissions and inspect whether previews, saved results and shared analyses respect that boundary.

For a useful pilot, ask a second analyst to reproduce an earlier conclusion from the recorded steps. The practical value of an AI analytics workspace is not only producing a chart quickly; it is helping another person understand and check how the chart was made.

SOURCES & CONTEXT

See the original announcement for availability and release details.