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ROI CALCULATOR 

Your Data Testing ROI Calculated in 2 Minutes

Most organisations don’t know — because the cost is buried in engineer hours, missed deadlines, and downstream errors. This ROI calculator surfaces it.

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85%

Reduction in manual testing hours

100+

Enterprise deployments

3-6 Months

Average testing cost reduction

60–70%

Average testing cost reduction

Build Your Business Case

Calculate the True Cost of Your Current Testing Process

Enter your environment parameters. The model does the rest — cost savings, ROI, payback period, and a 3-year projection.
⚠️ These are indicative estimates, not guaranteed projections: Figures are based on average outcomes across 100+ Datagaps enterprise deployments. Your actual results will depend on environment scale, process complexity, tooling, and implementation scope. Use them as a directional starting point for your business case — then request a personalised ROI Workshop for a model built on your specific numbers.
Understanding Your Results

Three Numbers. A Complete Financial Picture.

Each metric captures a different dimension of the return on automation.

Cost Savings

How much your testing spend changes

The percentage reduction in annual testing cost when manual processes are replaced with automation. This figure scales with environment size — larger pipelines and higher test frequency produce proportionally greater savings. Enterprise deployments consistently land between 60% and 86%.

ROI Multiple

Return relative to the investment

The ratio of total savings generated to the total cost of the Datagaps platform. A 7× return means the savings produced are seven times what the platform costs to run. This metric reflects the economic efficiency of automation independently of the absolute dollar amounts involved.

Payback Period

When cumulative savings exceed the cost

The month in which total savings cross the total investment in the platform. Most Datagaps deployments reach this point within 3 to 6 months — meaning the platform pays for itself within the same financial year it is deployed, with compounding returns in every subsequent year.

Get the strongest number: run all four calculators

ETL Validator, BI Validator, DQ Monitor, and Compliance Reporting — gives you a view of total DataOps automation savings. Individual product returns are meaningful in isolation. The combined picture across all four workstreams often reveals a significantly larger opportunity than any single number suggests.

Verified Outcomes

What This Looks Like in Practice

Documented results from live Datagaps deployments

ETL Validator
Financial Services
Data Migration

Fortune 100 Financial Services: Mainframe-to-Snowflake Migration

100% data validation coverage achieved without proportional headcount growth. Data errors caught at the point of movement, not discovered post-migration.
A large-scale mainframe-to-cloud migration where manual testing was creating timeline risk and budget overruns. Datagaps ETL Validator ran validation continuously alongside migration cycles, removing the bottleneck.
Download Case Study
ETL Validator
FMCG / Consumer
ETL Automation

Global Consumer Brand: How a French Consumer Brand Automated Testing and Cut Migration Time by 60%

60% reduction in migration time. A multi-month validation programme compressed, with engineer capacity freed for downstream phases.
A French consumer brand running a complex multi-system data migration. Manual testing was the critical path constraint. Automation removed it and accelerated the overall project timeline.
Download Case Study
DQ Monitor
Healthcare
APCD Compliance

Top 3 US Health Insurer: APCD Compliance Validation at Scale

Automated DQ validation across multi-state APCD submissions – manual effort per cycle eliminated, non-compliance submission risk removed.
APCD submissions carry compliance obligations that vary by state. Manual data quality checks were creating submission delays and exposure to error. Automation standardised the process across all states simultaneously.
Download Case Study
BI Validator + DQ Monitor
Financial Services
Compliance

Midwest Insurer: NAIC MAR Compliance Cycle Automated End-to-End

Quarterly compliance cycle compressed. Financial reconciliation fully automated — analyst time shifted from data preparation to review and decision-making.
NAIC MAR filings required significant quarterly effort from a compliance analyst team. Manual reconciliation was slow and carried human-error risk in a regulated context. The automated cycle now runs in a fraction of the time.
Download Case Study
BI Validator
Healthcare / Pharma
Tableau Automation

Global Pharmaceutical Leader: Automated Tableau Validation, Measurable QA Cost Reduction

Quarterly compliance cycle compressed. Financial reconciliation fully automated — analyst time shifted from data preparation to review and decision-making.
A pharma company running Tableau across hundreds of dashboards faced a growing regression burden after each data refresh. Manual spot-checking was slow, error-prone, and scaling poorly with the expanding report estate. Datagaps BI Validator took over the full regression cycle — running validation overnight and surfacing exceptions for human review rather than requiring full manual re-checks.
Download Case Study

View all 17 case studies across Financial Services, Healthcare, Life Sciences, Retail & Higher Education →

Next Steps

Want These Numbers Validated Against Your Actual Environment?

The ROI calculator gives you a directional view. Our team will map it against your specific pipelines, team structure, and data volumes — and produce a model grounded in your numbers.

Book a Free ROI Workshop
Download ROI Whitepaper
Trusted by 100+ enterprise teams · Fortune 500 customers across Financial Services, Healthcare, Life Sciences & Retail · Gartner-listed · SOC 2 Compliant
Frequently Asked Questions

Common Questions About the Numbers

How accurate is this calculator?

The figures are indicative estimates — derived from average outcomes across 100+ Datagaps enterprise deployments. They give a credible directional view of the magnitude of savings available, but are not a contractual projection. Actual results depend on environment scale, existing tooling, team structure, and how broadly Datagaps is deployed. Adjusting the assumptions in each calculator tab to reflect your own numbers will bring the output closer to your environment.

What is a typical payback period?

Based on 100+ enterprise deployments, the investment is typically recovered within 3 to 6 months. The primary driver is the elimination of manual testing labour — which in most data teams represents a recurring, high-volume cost across ETL, BI, and compliance workstreams. After payback, the savings compound with every subsequent test cycle.

What assumptions are built into the model, and can I change them?

The model compares your current manual cost — inputs multiplied by standard manual testing time per test case and an engineer hourly rate — against the automated cost, which reflects reduced execution time plus the Datagaps licence. All default assumptions (hourly rate, minutes per test case, automated execution time, annual licence cost) are fully visible and editable in the Adjust Assumptions section of each calculator tab. We recommend reviewing and updating these before drawing conclusions.

What does the ROI multiple actually represent?

The ROI multiple is the ratio of total savings generated to the total cost of the platform. A 7× return means that for every $1 spent on Datagaps — licence, implementation, and ongoing cost — the organisation recovers $7 in testing labour savings. The multiple is independent of scale, which makes it useful for comparing the efficiency of the investment across different environment sizes.

Can the calculator outputs be used in a formal business case?

Yes, with appropriate framing. These are indicative estimates based on industry benchmarks — they give a credible starting point for an investment evaluation, not a guaranteed outcome. The case studies on this page provide documented, real-world evidence to support the figures. For a more precise analysis built on your specific data volumes and team structure, a free ROI Validation Workshop with our team is the most efficient next step.

Is there a way to evaluate Datagaps before committing commercially?

Yes. Datagaps offers a structured free trial and a proof-of-value programme. We also run a free 30-minute ROI Validation Workshop — a working session where our team maps your environment against deployment benchmarks and produces a tailored financial model. There is no commercial obligation attached.
Schedule one here →

How does Datagaps compare to iceDQ, QuerySurge, and Wiiisdom on ROI?

The comparison is best understood at the total cost of ownership level, not product-by-product.

Datagaps vs. iceDQ · QuerySurge · Wiiisdom

  • Platform breadth reduces total spend: iceDQ and QuerySurge address ETL testing only. Wiiisdom addresses BI analytics only. Datagaps covers ETL, BI validation, data quality monitoring, compliance reporting, and test data management in a single platform — eliminating the need for multiple point-tool licences and the integration overhead between them.
  • AI automation reduces ongoing labour: Datagaps uses Agentic AI to generate test cases and adapt to schema changes automatically. iceDQ and QuerySurge require ongoing manual test maintenance. Customers moving from QuerySurge to Datagaps report a further 25–40% reduction in ongoing testing labour from AI-driven automation alone.
  • Independent recognition: Datagaps is the only vendor listed in both the Gartner® DataOps Tools and Data Observability Market Guides. No comparable listing exists for iceDQ, QuerySurge, or Wiiisdom.

Is Datagaps suited to regulated industries — Financial Services, Healthcare, Life Sciences?

Yes, with documented deployments in all three. In Financial Services: SOX-compliant financial reporting, NAIC MAR reconciliation automation, and mainframe-to-cloud migration validation. In Healthcare: APCD submission automation, HIPAA-compliant pipeline validation, and clinical data testing. In Life Sciences: 21 CFR Part 11 compliant validation workflows. In regulated environments, the cost of a data error — through audit findings, rework, or compliance penalties — typically makes the financial case for automation significantly stronger than the calculator alone captures. 
See industry-specific deployments →

How does Datagaps pricing work?

Pricing is scoped to your environment — number of data sources, test case volume, and which Datagaps products you deploy. Most enterprise customers recover the annual licence cost within the first quarter of operation from labour savings alone. A scoped commercial proposal is typically available within a few business days of an initial conversation. 
View pricing overview →

Does Datagaps work with our existing data stack and BI tools?

Datagaps connects to 200+ data sources — including Snowflake, Databricks, Azure Synapse, Salesforce, SAP, and Oracle. BI Validator integrates natively with Power BI, Tableau, and Oracle Analytics. Your existing infrastructure stays in place — Datagaps validates it without requiring changes to the underlying stack or re-architecting pipelines.

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