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Data · Analytics platform

Data Science Dashboard

Reporting was assembled manually from exports of several systems. By the time figures were agreed they described a period that had already closed, and definitions differed between departments.

Project type
Analytics platform
Industry
Multi-sector
Domain
Data
Delivery
Pipelines, then dashboards and forecasting

Technologies

  • Python
  • Pandas
  • FastAPI
  • PostgreSQL
  • React
  • Scheduled ETL
Metric definitions
Single source
Refresh
Scheduled
Drill-down
To source record

These describe scope and architecture. We do not publish performance figures we cannot substantiate.

Business problem

Reporting was assembled manually from exports of several systems. By the time figures were agreed they described a period that had already closed, and definitions differed between departments.

Our approach

We built pipelines that consolidate source systems on a schedule into a modelled warehouse with a single agreed definition per metric, then exposed it through dashboards that drill from summary down to the originating transaction.

Result

Departments work from one definition of each metric, figures refresh automatically, and any number on a dashboard can be traced back to the records behind it.

What was built

Key features

The capabilities that mattered most to the outcome.

  • Scheduled pipelines consolidating multiple source systems
  • Executive summary with drill-down to transaction level
  • Cohort, trend and variance analysis views
  • Forecast overlays with confidence bands
  • Scheduled report delivery by email
Engineering

Technologies used

Chosen for the requirement rather than by habit — we explain the trade-offs before building.

Python
Pandas
FastAPI
PostgreSQL
React
Scheduled ETL

Your project would not necessarily use the same stack. We choose per requirement, and we will explain the reasoning before anything is committed.

Next step

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Seyffluo Technologies

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