Data & Analytics ROI Calculator: See Your Real Cost of
Enter 6 numbers about your data environment. Get instant, benchmark-backed estimates of what data inefficiency costs — and what modern pipelines can save.
Your total cost of data inefficiency
Savings from modern pipelines
Hours freed from manual prep
ROI payback period for a typical engagement
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Calculate Your Data & Analytics ROI
Adjust the sliders to match your data environment. Results update instantly.
Your Inputs
Assumptions behind these numbers
Hourly rate: Annual salary ÷ 2,080 working hours/year.
Manual prep cost: Based on team factor (capped at 4× for shared prep work), from IDC DataSphere studies.
Pipeline failure cost: 4 hours average fix time per failure, with 2-person response teams, occurring monthly per pipeline.
Data downtime cost: $500/hour business impact with 2-hour average downtime per failure, based on Gartner Data Quality research.
Savings: 75% manual prep reduction, 60% failure rate improvement, 25% infrastructure optimization.
Payback: Based on a $30K typical engagement cost divided by monthly savings.
Manual prep, failures & downtime annually
Estimated recoverable value
Manual prep time freed per week
Pipeline failure rate improvement
Monthly infrastructure cost savings
Time to recoup engagement cost
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How We Calculate These Numbers
Our methodology is based on peer-reviewed industry research — and we err on the conservative side.
Gartner Data Quality
Cost of poor data quality benchmarks and business impact analysis from Gartner Research.
IDC DataSphere
Data management efficiency and team productivity studies from IDC DataSphere research.
Monte Carlo Data Observability
Pipeline reliability benchmarks and data downtime cost analysis from Monte Carlo reports.
These are conservative estimates. Actual savings typically exceed projections due to compounding efficiency gains.
Data & Analytics ROI Calculator FAQ
Common questions about the calculator and our data engineering process.
How accurate are these data pipeline estimates?
These calculations use industry benchmarks from Gartner Data Quality research, IDC DataSphere studies, and Monte Carlo Data Observability reports. They represent conservative estimates — actual savings often exceed projections because the model doesn't account for improved decision-making speed, reduced compliance risk, or faster time-to-insight for business teams.
What data tools and platforms do you support?
We work across the modern data stack: Apache Airflow, dbt, Spark, Snowflake, BigQuery, Redshift, Databricks, Kafka, Flink, and more. We also support BI tools like Tableau, Looker, Power BI, and Grafana. Our recommendation depends on your data volume, team skills, and business requirements.
How long does a data engineering engagement take?
Quick wins like pipeline monitoring and alerting can be delivered in 2–4 weeks. A full data platform modernization — including pipeline refactoring, observability, data quality checks, and team training — typically takes 8–16 weeks. We prioritize reliability improvements first so you see fewer failures within the first month.
Can you help with both pipelines and dashboards?
Yes, we cover the full data lifecycle: data ingestion, transformation, orchestration, quality monitoring, and visualization. Whether you need to fix unreliable ETL pipelines, build real-time streaming architectures, or create self-service analytics dashboards, our team has the expertise to deliver end-to-end.
What's included in the free data audit?
We'll review your pipeline architecture, identify failure hotspots, assess data quality gaps, and benchmark your infrastructure costs. You'll get a prioritized list of improvements with estimated impact, a reliability scorecard, and a recommended roadmap. No sales pitch — just actionable advice from data engineers who've built 500+ production pipelines.
Book a free data audit to validate your numbers
Our senior data engineers will review your pipeline architecture, identify failure hotspots, and map your top 3 optimization opportunities.