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What downtime and data loss really cost the business
Downtime in the organization spotlights revenue and operational risk. Business leaders can use cost estimates to focus recovery spending where outages cause the greatest loss.
Downtime in a data platform rarely behaves like a neat, time-boxed IT event. Without quick disaster recovery, the business could incur significant costs.
During a data loss event, the business loses more than dashboards. Teams pause work, leaders defer critical decisions, revenue operations run with visibility gaps and high-cost specialists manage incident response. Even after service restoration, leaders must validate recovered assets, reconcile discrepancies and rebuild confidence before resuming operations. That post-recovery window is often the difference between "system up" and "business back."
Outage research from 2025 by the Uptime Institute and a 2024 report from Information Technology Intelligence Consulting show that the cost of significant incidents typically exceeds $100,000, meaning just one or two outages can account for most of a year's downtime risk.
The cost of downtime can be measured. Disaster recovery (DR) is a decision about cost and risk: how much it costs and how much loss it helps avoid. Business leaders can determine that risk in financial terms without building a complex risk model. By calculating downtime cost per hour (DCH) and using incident history to estimate annual expected loss (AEL), leaders can compare today's risk to the reduced risk after a DR investment.
Step 1: Build an auditable DCH
Start with a simple question: if the data platform is down -- or giving incorrect data -- for one hour, what does that hour cost the organization?
Breaking down the cost into components makes estimates easier to review. If people disagree about a cost, estimate it using a low, medium and high range instead of pretending to be precise.
Add the components to calculate DCH. In this example, one hour of downtime costs $6,440.
Step 2: Identify hidden costs
The outage itself shows only the visible costs. Hidden costs appear after the platform is available. Ignoring these costs understates the affect of a data loss event, since much of the expensive work starts after recovery.
Organize the less obvious costs that appear after an incident into the following categories:
- Reprocessing and extra compute costs. Rerunning jobs, using extra warehouse capacity and paying any additional data transfer fees.
- Time to check and validate data. Data engineers, analytics and finance teams verifying restored data is complete, correct and trustworthy.
- Fixing reports and redoing work. Correcting KPIs, revising updates and handling the disruption caused by changing numbers.
- Contract and regulatory exposure. Incorrect or late reporting can be as damaging as missing reporting, leading to hefty fines or penalties.
- Lost time and delayed progress. Roadmap work slows or stops while teams divert effort to rework, audits and rebuilding confidence in data.
- Insurance changes. After a serious incident, organizations can expect higher premiums, higher deductibles and reduced coverage at renewal.
Step 3: Show how DR reduces risk and costs
An untested DR plan might not work when needed. To evaluate DR investments, show how technical capabilities change downtime, data loss and recovery costs. As a practical rule, prioritize controls that detect problems faster, recover faster and limit how much data is affected.
Step 4: Calculate DR ROI using expected loss
After calculating DCH and incident costs, evaluate DR by estimating expected loss with the following equation:
Annual Expected Loss (AEL) = (Annual downtime hours × DCH) + (Annual incidents × Cost per incident)
Organizations should use their own incident history where possible. External incident-cost benchmarks -- such as IBM's annual Cost of a Data Breach report -- can confirm whether the estimate is reasonable.
With a baseline AEL, calculate an improved AEL that reflects the use of DR investments.
Identify the annual cost of DR investments, and how much it reduces downtime and incident costs. Then use the following equation:
Improved AEL = (New annual downtime hours × DCH) + (Annual incidents × New cost per incident)
Find the risk reduction value with the following equation.
Risk reduction value = Baseline AEL – Improved AEL
Finally, to calculate ROI, use the following equation.
ROI = (Risk reduction value – DR costs) / DR costs
If the organization supports multiple data products, group them by how critical they are to the business. For example:
- Tier 0: Billing, finance and fraud.
- Tier 1: Forecasting and measurement.
- Tier 2: Exploration.
For each tier, document the target RTO/RPO and the steps required to complete time to restore trust. This keeps resilience spending proportional to business impact.
Downtime cost example
Using the example from Step 1, assume a DCH of $6,440 per hour, 12 hours of downtime per year and two data-loss/corruption events per year at $9,000 each.
- Downtime loss: 12 × $6,440 = $77,280
- Event loss: 2 × $9,000 = $18,000
- Baseline AEL: $77,280 + $18,000 = $95,280
Now assume DR costs $25,000 per year, reduces downtime to 4 hours per year and reduces event cost to $5,000.
- Improved AEL: (4 × $6,440) + (2 × $5,000) = $35,760
- Risk reduction value: $95,280 − $35,760 = $59,520
- ROI: ($59,520 − $25,000) / $25,000 = 138%
Based on these calculations, investing in DR cuts annual losses by more than half and delivers a 138% return, meaning the avoided losses are more than double the cost of DR.
Helen Searle-Jones holds a group head of IT position in the manufacturing sector. She draws on 30 years of experience in enterprise and end-user computing, utilizing cloud and on-premise technologies to enhance IT performance.
