What does data downtime cost you?
A dashboard showing wrong numbers, a table that stopped updating, a model fed incomplete data. Answer 5 questions to see what it costs you a year.
- No credit card, no commitment
- Based on Monte Carlo's surveys
- Calculated in your browser
Question 1 of 5
How many tables are in your data warehouse?
Not sure? 100-500 is typical for a mid-sized company.
Question 2 of 5
How many data engineers and analysts do you have?
Question 3 of 5
How do you find out something broke?
Question 4 of 5
What is your annual revenue?
Question 5 of 5
How much does revenue depend on up-to-date data?
Your results are ready
Leave your details to see the full report now:
- How many incidents and hours of downtime to expect a year
- How much engineering time goes to firefighting
- The hit to revenue
- What automated monitoring would save
Your data downtime cost
Scale
- Data incidents a year
- Hours with wrong or missing data
Yearly cost
- Engineering time on incidents
- Revenue impact
Capped at 15% of revenue, the low end of Redman's research on the cost of poor data quality. The lines shrink in proportion.
Opportunity
- Yearly savings with automated monitoring
You already monitor automatically. The next step is AI-assisted root cause analysis and clear ownership of every table.
An estimate based on industry averages. Your own numbers may differ.
We have your details. One of our data and AI engineers will get back to you within one business day to go through the results with you.
Next steps
How we calculate
The calculation is based on the formula Monte Carlo, the data observability company, published for its own calculator, and on its surveys. Anything that is our own assumption is marked as one.
| Line | Calculation | Source |
|---|---|---|
| Incidents a year | Tables × 0.07. | Monte Carlo 2022: about 70 incidents a year per 1,000 tables. |
| Downtime hours | Incidents × hours each: 19 without monitoring, 12 with manual checks, 6 with automated monitoring. Up to 8,760 hours a year. | Monte Carlo survey, 2023: 15 hours to resolve and about 4 to detect. 12 and 6: our assumption. |
| Engineering time | Engineers × yearly cost × share of time on incidents: 40%, 25% or 12%, but never more than 3 engineer-hours per hour of downtime. Yearly cost: $112K in dollars or euros, ₪535K in shekels. | Monte Carlo 2022: data engineers spend 40% of their time on bad data. $112K: Monte Carlo's $62 an hour × 1,804 hours. ₪535K: Israel's average high-tech wage (CBS 2026) with employer costs. 25%, 12% and 3 engineer-hours per downtime hour: our assumption. |
| Revenue impact | Revenue per hour × dependency on data (5%, 10% or 15%) × downtime hours, up to $15M. | The formula, levels and cap of Monte Carlo's calculator. |
| Cap | The yearly cost never exceeds 15% of revenue. When the cap applies, the lines shrink in proportion. | Redman, MIT Sloan Management Review 2017: poor data quality costs 15%-25% of revenue. |
| Savings with monitoring | Today's cost minus the cost with automated monitoring. | The calculation itself. |
Each answer range is calculated at its midpoint. Exchange rate: $1 = ₪3.6.
Questions
What is a data incident?
Any time data is wrong, missing or stale: a table that didn't load, a field that changed without notice, duplicates. The incident itself is quiet; the damage comes when someone decides on a wrong number.
We don't have a data warehouse. Is this relevant?
Less so. The data value calculator fits better, and it will also show whether it's time for a central warehouse.
How does automated monitoring work?
Tools that keep checking whether tables updated on time, whether volumes look normal and whether the schema changed, and alert you before a user runs into the problem.
