The Return On Investment (ROI) of Automated Data Testing with AI

Making a business case for QuerySurge + AI is easy with an ROI of 877% over 3 years!

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QuerySurge Return On Investment (ROI) Calculation

The metrics below show the ROI of utilizing the QuerySurge AI module vs. testing with an In-house solution.

Comment: Use of another commercial solution may speed up step (3) — Analysis Time, but it will not speed up step (1) — Test Design

These metrics only take into account ROI from a time/cost perspective of the same resources and not vs. head count redeployment or the impact of bad data.

QuerySurge vs. Non-AI Testing

(using proprietary or in-house framework)

Metrics and Assumptions
  • There are 1,200 data mappings that need to be converted to tests
  • An average data mapping requires approximately 1 hour to manually code into an ETL test (2 queries; 1 at source and 1 at target) , assuming moderate complexity
  • QuerySurge AI can convert 200 mappings of moderate complexity into tests per hour
  • The assumption is that both methods (QuerySurge and in-house solution) can execute in 8 hours
  • In-house analysis takes approximately 1 hour per test to analyze (2 queries of moderate size; 1 million rows and 20 columns)
  • The utilization of a conservative consulting rate of $95/hour, which typically depends on the consulting firm and the resource’s skill level
In-house Testing (using minus queries, sampling, and/or proprietary framework) Automated Testing with QuerySurge + AI
Task Hours Cost Hours Cost
1. Test Design Time
for 1,200 Tests
1,200 $114,000 6 $570
2. QuerySurge
Subscription Licenses
3. Execution and Analysis Time Per Release 1,208 $114,760 8 $760
4. Report Creation 24 $2,280 1 $95
Totals after 1 Test Cycle 2,432 $231,040 15 $31,487
Additional Mappings
(5% per cycle)
60 $5,700 1 $95
12 Cycles Per Year 3,092 $293,740 26 $30,062
3 Year Project
(36 Test Cycles)
9,276 $881,220 78 $90,186

3‑year ROI = $881,220 (manual testing) / $90,184 (QuerySurge)= 877% ROI

This ROI calculation only calculates labor savings and does not take into account the inherent cost of bad data, which analyst firm Gartner states costs the average company $14 million annually.


  • Dramatically decreases time to create tests and analyze results
  • Enables increase in data quality due to much faster & more thorough testing cycle
  • Reduces need for skilled tester
  • Facilitates increase in ETL testing coverage to upwards of 100%
  • Allows for redeployment of testing head count
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