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Savings calculator

What is slow data movement costing you?

Data pipelines steal time in units nobody itemizes. Here's the itemization — pre-filled with typical numbers, ready for yours.

pipeline hours returned = (your baseline − our minutes) × jobs/week × 52
Your numbers. Your math. Our engine.

Operational value · the pipeline's clock
Pipeline hours returned / year
Wall-clock time your jobs stop consuming — regardless of who's watching
Speedup per job
Baseline ÷ DataForge
Pipeline days returned / year
Batch windows, SLAs, onboarding, data freshness
Organizational value · your people's clock
Human hours returned / year
Pipeline hours × human time allocated per job
Dollars returned / year
Human hours × your loaded cost

That is the annual output of full-time engineers — spent entirely on waiting.

⚠︎ Sanity check: this result implies full-time employees doing nothing but waiting, all year. If that exceeds the population that actually touches these pipelines, your "jobs" and "human time allocated" inputs are overlapping — the same person's attention spread across several jobs should be counted once. Conservative inputs make this number unbeatable in a meeting; inflated ones make it dismissable.
Your numbers

Adjust to your reality

Every field starts on a typical value. Change any of them and the numbers above update instantly.

Industry norm: 4–8 hrs for multi-GB ingestion
Across all teams and pipelines
Combined share of human time consumed by monitoring, waiting, reconciling, and related attention (0.25 and 1/4 both work)
Salary + overhead, per person
Measured: 2.7 GB in under 2 minutes

One job, side by side

Today's pipeline
DataForge
Bar length is proportional to elapsed time per job. The sliver is not a rendering error.
Methodology & assumptions — how to defend this number

Two clocks, two formulas: Operational (pipeline) hours returned = (baseline − DataForge time) × jobs per week × 52 — pure wall-clock, independent of staffing. Organizational (human) hours returned = pipeline hours × human time allocated per job. Dollars = human hours × loaded cost. Speedup = baseline ÷ DataForge time, per job. The operational number is unaffected by the human-time input — it cannot be inflated. Human time allocated is the combined share of human attention a job consumes (monitoring, waiting, reconciling), entered as a decimal or a fraction — 0.25 and 1/4 are the same value. Zero is honest: unattended pipelines read $0 organizational value while operational value still counts.

Assumptions you are making when you fill it in: each "blocked" person is genuinely stalled for the full job duration (count only real idle time, not everyone downstream in a dependency graph); jobs do not share the same waiting people (if they do, count that person once); baseline reflects your actual measured job times, not worst cases.

One number, one job: this calculator estimates the cost of waiting inside your organization. It is not a market-size estimate — the economy-wide cost of slow data movement is far larger, and is a different number for a different conversation. Keep them separate and both stay credible.

Provenance: the DataForge default reflects published benchmarks — 2.7 GB ingested in under 2 minutes; 1,181,807 rows/sec sustained on commodity infrastructure; 2,608,874,016 rows validated at Δ0 (zero net row delta) on the public CourtListener corpus. Baseline defaults are illustrative industry norms — replace them with your own. Patent‑pending. Live on Google Cloud Marketplace.