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The full cost of your data

The license is the smallest line you can see. The bigger costs are the ones nobody budgets for.

What most budgets track

What they usually don't

Engineering time, technical debt, delay, the cost of data that never arrived, and the value of data you never got to use.

Today's Approaches

  • Tools and services
  • Compute to refresh pipelinesFull refresh or incremental, depending on how each pipeline is built
  • Engineering to build and maintainMapping every feed, fixing every break
  • Technical debtGrows with every schema change: patches, workarounds, and code nobody wants to touch
  • DelayMonths waiting for usable data
  • Missing data ?Unseen until an audit, a wrong answer, or a missed payment
  • Data you can't use yetSet aside because making it analytics and AI ready costs more than it seemed worth

The DataPancake Approach

  • DataPancake licenseKnown before you build
  • Compute to scan and discover schema, and refresh Dynamic TablesIncremental, as your data changes
  • Engineering: minutes to hours
  • Optional AI token cost for data dictionary builder and MCP Server/CoCo integration

Illustrative, not to scale. Your proof of value fills in the real numbers, starting with what your current pipelines miss.

For finance: build your business case

Build your business case with your own numbers.

Your finance team supplies the inputs; we help fill them in during the proof of value.

Use the same rates, volumes, and costs in both columns. Only the hours, months, incidents, and datasets change.

1. Cost savings (annual)

Cost line

  • Today (annual cost)
  • With DataPancake (same measure)
Maintenance and technical debt
Engineer hours per month maintaining these pipelines, including workarounds and rework after schema changes × loaded hourly rate × 12
Engineer hours per month reviewing changes and regenerating × the same rate × 12
Adding new data
Hours to bring a new source into your tables (a new partner, application, system, or file type) × new sources per year × loaded hourly rate
Hours to scan, configure, and generate a new source × the same sources per year × the same rate
Compute
Current annual compute for these pipelines
Projected annual compute from the proof of value: scanning, reconciliation metrics, and incremental refreshes
Certification and audit evidence
Hours per year assembling evidence for audits, examinations, and certifications (sampling, queries, reconciling by hand, answering examiner requests) × loaded hourly rate + outside fees for lookbacks, audit support, or consultants
Hours per year pulling reconciliation results and source-to-column lineage for the same audits × the same rate + outside fees you still need
License
Current tools and services for these pipelines
DataPancake's annual price, plus any current tools you keep

2. Losses avoided (annual)

Loss line

  • Today (annual loss)
  • With DataPancake (same measure)
Missing data
Incidents from missing or wrong data in the last 12 months × cost per incident (rework, penalties, write-offs, missed recoveries)
Incidents still expected once reconciliation checks every load × the same cost per incident (the proof of value shows which past gaps it catches)

Count each regulatory finding or penalty in one row only.

3. New value

Value line

  • Today
  • With DataPancake (same measure)
Data you can't use yetannual
$0 realized from each dataset set aside because making it analytics and AI ready costs too much
For each dataset you now refine, the annual value of the decisions, reports, recoveries, products, or AI use cases it supports (its cost is already in the compute and license lines)
Delayone-time, first year
Months until the data is usable × monthly value of having it
Months until usable with DataPancake, from the proof of value × the same monthly value
Net annual savings
= Today − With DataPancake, across the cost savings rows
Losses avoided
= Today − With DataPancake for missing data
New value
= annual value of data you can now use + one-time value of having it sooner

Judge payback on savings alone. Losses avoided and new value are upside.

Start from your number, not ours. The proof of value runs reconciliation on your own data, so the missing-data line starts from what your current pipelines actually miss.

Build your business case with us

“With 1.5 engineers in a few weeks, we used DataPancake to generate thousands of lines of production-ready code that would have taken a full team months to write by hand.

Without it, this migration would not have been economically feasible or completed on time.”

— Jon Edwards, VP Carrier Data Acquisition and Integration, Quility

See your price on your own data.

Schedule a demo to see DataPancake in action, then a guided proof of value that measures your data and shows your projected annual price before you commit.

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