August 11, 2026
There is no single generally accepted “data valuation multiple” comparable to an EBITDA or ARR multiple. In this paper we look at operational data supplied back to a company or OEM through IOT solutions or more broadly hardware and software solution combinations that provide monitoring of equipment performance, utilization, failure modes, operating conditions, efficiency, maintenance, etc. The most defensible approach is to value the economic benefit that can be derived from the data. A useful framework is set out in the table below:
| Valuation method | How it works | Relevance to equipment data |
| Cost approach | Cost to independently collect, clean, structure and maintain equivalent data | Useful as a floor value |
| Market approach | Comparable pricing for telemetry/data feeds, benchmarking services, APIs or analytics subscriptions | Useful where comparable commercial arrangements exist |
| Income / economic benefit approach | PV of incremental profits or cost savings the OEM can generate from the data | Usually the best method due to ease of value calculation |
| Relief-from-royalty | Estimate what OEM would pay to license access to equivalent proprietary data | Particularly useful for licensing negotiations |
| Incremental value / “with-and-without” | Compare OEM economics with access to the dataset versus without it | Often the strongest strategic valuation method |
An Example: Estimating the value of the data across five economic benefit pools
Suppose a technology company collects detailed operating data from machinery in the field and provides that information to the company or the OEM. The value isn't merely “we have 10 million data points.” It is what those observations allow the company or OEM to do. Quite often they are simply the opposite sides of the same coin company vs OEM.
1. Product development savings. Field data can reduce physical testing, accelerate engineering iterations, identify design weaknesses and improve component selection. If an OEM spends $20 million annually on engineering and the dataset allows a 5% improvement in development efficiency, that potentially creates roughly $1 million/year of economic benefit.
2. Warranty and reliability savings. This can be particularly valuable. If the OEM can identify operating conditions associated with premature failures, improve predictive maintenance or distinguish equipment defects from misuse, the information can reduce warranty claims materially. For example:
Annual warranty cost = $30M; Data-enabled reduction = 4%; Annual benefit = $1.2M
3. Customer productivity / equipment efficiency. Data showing how machines actually operate can enable better fuel efficiency, cycle times, throughput, uptime, energy consumption or consumables usage. Some of this value could potentially be captured through higher equipment prices, service contracts or software subscriptions. For companies, it can improve operational excellence driving down both operating and repair and maintenance costs.
4. Parts and aftermarket revenue. Operational data can identify component degradation and replacement cycles. This is particularly valuable because aftermarket parts and service can carry substantially higher margins than the original equipment (for OEMs) or lower repair and maintenance costs (for companies).
5. Strategic/product intelligence. A sufficiently large dataset can tell the OEM things it otherwise has difficulty knowing: how equipment is actually being used, which features customers use, what operating conditions produce failures, where operators are inefficient, which components are over/under-engineered and how equipment performs across applications. That category can create something more valuable than individual data records: a proprietary operating dataset accumulated across an installed base.
A Practical Valuation Model
For an industrial technology company, an annual OEM data value model could approximate the following:
| Economic benefit | Illustrative annual value |
| Reduced warranty claims (to OEM) | $750,000 |
| Engineering/R&D efficiencies (to OEM) | $500,000 |
| Improved equipment performance (to Company) | $600,000 |
| Predictive maintenance / service revenue (to OEM or Company) | $750,000 |
| Parts/aftermarket optimization (to OEM or Company) | $400,000 |
| Product/customer intelligence (to OEM or Company) | $500,000 |
| Total Potential Value | $3,500,000 |
To determine what proportion of that value is attributable to the data and can realistically be captured by the data provider is the next step. For example, if the parties (OEM or Company) can capture $3.5 million annual economic value, it would be difficult for the data provider to demand the entire $3.5 million. A value-sharing model might support compensation equal to perhaps 10–30% of demonstrable economic benefit, depending heavily on uniqueness, substitutability and bargaining power. At 20%, (ignoring the split between company and OEM for the moment), this would imply: $3.5M × 20% = $700,000 annual value of the data relationship (s). If the data stream is durable and recurring, it could then be valued as a recurring revenue/licensing asset rather than merely as a one-time dataset.
The most important factor in value: Uniqueness
The value of data rises dramatically depending upon how difficult the data is for the company or the OEM to obtain elsewhere. It can be thought of approximately this way:
| Data characteristic | Effect on value |
| OEM already collects essentially identical telemetry | Low |
| Data is available but requires substantial processing | Low–moderate |
| Data provides operating parameters OEM doesn't currently capture | Moderate |
| Cross-customer dataset reveals field performance patterns | High |
| Data links operating parameters to productivity/quality/failure | Very high |
| Proprietary longitudinal dataset covering thousands of machines | Potentially strategic |
The distinction between raw telemetry and derived operational intelligence is especially important. For example, knowing that a motor operated at 72°C for 18 minutes is data.
Knowing that operation above 68°C under a particular load profile increases component failure probability by 4.2× within the next 400 operating hours is proprietary intelligence. The second is potentially worth orders of magnitude more.
Example: Framework to use in a negotiation on Data Value
“Our data is worth $X” is not a compelling argument. Three negotiating reference points that illustrate value are: Cost floor → Comparable market price → Economic value ceiling
OEM Data Value = Warranty Savings + Engineering Savings + Productivity Gains + Aftermarket Profit + Incremental Software/Service Revenue + Strategic Product Development Value
For industrial equipment operational data, the economic value ceiling is often vastly higher than the cost of collecting the data, which is why a simple cost-based valuation can substantially undervalue it.
Of Note:
Separate "data rights" from "data value"
If you are considering this in the context of a technology company, caution should be applied to simply granting an OEM ownership of the data in exchange for a commercial relationship. There are really four separate assets:
Raw Data → Aggregated Data → Derived Data → Models/Insights
The technology company can permit the OEM to use the first or second category while retaining ownership of the latter categories. That matters because the aggregated dataset may eventually become a substantial component of the technology company's enterprise value. For example: Equipment → Operational Data → Benchmark Dataset → Algorithms → Predictive Models → OEM Decision Support The farther to the right you move, the less the product resembles commodity data and the more it resembles proprietary software/IP.
