Decisions leaders can defend.
Quantitative and qualitative analytics, Tableau and Power BI dashboards, and the financial data-warehouse engineering beneath them.
Decision Analytics, Visualization & Data Management
Decision analytics is the layer that turns program, resource, and mission data into something a leader can act on: one authoritative figure, a view that explains it, and a path back to the record it came from.
Pueo does the analysis and the engineering beneath it. Quantitative work covers trend, variance, and scenario analysis. Qualitative work covers structured interviews, surveys, and process observation, for the questions numbers alone do not answer. Results publish to Tableau and Power BI dashboards built for the role that reads them, with executive rollups that drill down to the transaction.
Underneath sits the financial data warehouse Pueo engineers: pipelines from authoritative source systems, data models shaped to the questions being asked, automated refreshes, and the governance (data dictionary, lineage, quality rules, role-based access) that makes a dashboard trustworthy enough to brief.
What’s Inside
Quantitative and qualitative analysis
Trend, variance, and scenario analysis across program and financial data, paired with structured interviews, surveys, and process observation where context matters as much as the figure.
Dashboards and visualization
Tableau and Power BI dashboards designed by role, from executive rollup to analyst drill-down, with definitions documented on the page so two readers reach the same conclusion.
Financial data-warehouse engineering
Pipelines from authoritative source systems, data models shaped to the questions being asked, and automated refreshes that retire hand-built spreadsheet cycles.
Data management and governance
Data dictionary, lineage, quality rules, reconciliation checks, and role-based access, so a published figure can be traced and defended.
Reporting automation
Recurring products generated from the warehouse on a schedule, so analysts explain the report instead of rebuilding it and every copy stays consistent.
Outcomes
- One authoritative figure, defined once and used everywhere.
- Dashboards that drill from the executive rollup to the source record.
- Analyst time moved from rebuilding reports to interpreting them.
- Data-quality problems caught by reconciliation, not by the audience.
- Decisions backed by analytics leaders can defend.
Where this connects.
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