Hours your analysts spend gathering data instead of using it.
Most operations teams spend 30–60% of an analyst’s week pulling reports, stitching CSVs, and reconciling numbers across systems. This calculator puts a dollar figure on that work — and what it looks like once the pipeline runs on its own.
Data intelligence builds replace the weekly report-gathering loop with a live dashboard that queries your actual systems. The math below: analyst FTE × weeks × hours/week spent on reporting × fully-loaded hourly cost. Automation typically recovers 60–80% of that time; we use 70% as a conservative default.
Your numbers
Salary + benefits + overhead
Conservative default 70%
The math
Analysts × hours/week × 50 weeks × automation capture share.
Want to see what that pipeline actually looks like?
A 30-minute call walks through where your analyst hours are going and what a data-intelligence build would replace. We can scope a pilot against one reporting loop first so the ROI math is concrete before you commit to a full build.
Book a scoping callFrequently asked questions
- For routine reporting loops (weekly KPI decks, reconciliation checks, dashboard refreshes) yes — often higher. For work that requires real judgment (anomaly interpretation, stakeholder conversations) the automatable share is lower. The default is conservative on purpose so the math isn’t oversold.
- A live dashboard backed by queries against your actual systems (data warehouse, CRM, ops tools) plus scheduled jobs that handle the gathering work. Stack is usually dbt or SQL + a BI layer (Looker Studio, Metabase, Grafana) + light automation. You own the whole pipeline.
- A first data-intelligence loop ships in 3–6 weeks depending on scope. Scoping work usually happens in the AI Audit ($5,000, 4 weeks) or AI Sprint ($3,000, 2 weeks) so we’re not building against assumptions.