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What counts as innovation—and what does not?
The Oslo Manual 2018, published by the OECD and Eurostat, defines an innovation as “a new or improved product or process (or combination thereof) that differs significantly from the unit’s previous products or processes and that has been made available to potential users (product) or brought into use by the unit (process).” The definition makes implementation central: an idea, patent, R&D expense, or prototype by itself is not necessarily an innovation.
The manual also states that “the baseline definition of innovation in this manual does not require it to be a success.” Whether an implemented change succeeds is a separate outcome question. This distinction keeps an organization from treating activity as proof of value—or from erasing a real innovation from its records merely because its results fell short.
How should you organize the measures?
Track distinct stages rather than compressing them into one innovation score. Each stage answers a different question, and evidence at one stage does not prove the next.
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| Stage | Question | Possible evidence | What it can show |
|---|---|---|---|
| Resources and capabilities | Does the organization have conditions that may support change? | Relevant skills, collaboration, experimentation, or investment in intangible assets | Potential capacity; not implementation or customer value |
| Innovation activities | What work is being undertaken? | Development, testing, design, or other activities tied to the focal work | Effort and activity; not an implemented innovation |
| Implemented changes | What significantly different or improved product or process was made available or put into use? | A defined count or description of qualifying product and process innovations during a stated period | Implementation; not whether the change succeeded |
| Consequences | What changed as a result? | Customer, quality, operational, financial, workforce, societal, environmental, access, or mission outcomes, as relevant | Observed results; attribution may require more than a simple before-and-after comparison |
This separation follows the Oslo Manual’s cross-sector approach to measuring innovation activities, outcomes, and impacts. The 2018 manual describes innovation surveys conducted in more than 80 countries in its publication context; that figure describes the reach at that time, not a current country count.
Which measures show quality beyond revenue?
Quality should be assessed at more than one point: what customers experience, how the product or service performs, and how reliably the underlying process works. Choose indicators that reflect the specific product, service, or process rather than adopting a generic KPI list.
| Measure group | Examples | What to define |
|---|---|---|
| Customer outcomes | Customer satisfaction or engagement | Whose experience is measured, how it is collected, and the observation period |
| Product or service quality | Defect levels, service errors, reliability, or consistency | What counts as a defect or error, the denominator, and the affected population |
| Process outcomes | Process performance, variation, rework, or another indicator tied to the process objective | The process boundary, measurement method, and conditions under which the process is observed |
| Financial outcomes | Revenue, cost, productivity, or margin | The relevant accounting or operational definition and period |
| Broader outcomes | Workforce, societal, environmental, access, or mission outcomes | Why the outcome matters to the organization and whether a credible measurement method exists |
NIST’s Baldrige materials connect product and operational performance—including defects and service errors—to quality and customer results. ISO guidance on statistical process control describes monitoring process performance to detect variation that could result in defects. Neither a single customer measure nor a process measure should stand in for the entire quality picture.
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How do you build a practical measurement dashboard?
1. Choose the decision and unit of analysis
Start with the decision the measures should inform. Specify whether you are assessing a product launch, a service process, an organization, a region, or a public program; who is meant to benefit; and the time horizon. A measure useful for a product team may not answer a question about a whole organization. The Oslo Manual’s object-based approach focuses data collection on a defined focal innovation, which can also improve survey data quality.
2. Select a compact set of connected indicators
Choose a small group that spans the measurement chain: relevant enablers and activities, implemented changes, quality and customer outcomes, process results, and any material mission or financial outcomes. Treat investment, training, experimentation, and collaboration as possible drivers rather than evidence that innovation has produced value. Include revenue, cost, productivity, or margin where they matter, but read them alongside the other outcomes.
3. Write a measurement specification for every indicator
Before comparing results, record what each measure means and how it is produced. A useful specification includes:
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- Name and purpose: what the indicator is called and which decision or outcome it informs.
- Definition and unit: the exact event or condition counted, along with the unit used.
- Numerator and denominator: where a rate or proportion is reported, specify both; for a count, state the counting rule.
- Population and scope: which customers, products, teams, transactions, or processes are included and excluded.
- Data source and owner: how the information is collected, who is responsible, and how missing data are handled.
- Baseline, target, and period: the reference point, intended level, and observation window or reporting frequency.
- Limitations: known uncertainty, changes in collection or definitions, and incentives that could distort behavior.
The OECD’s guidance addresses survey design, data sources, indicator construction, and limitations; stable definitions are necessary if results are to be compared over time.
4. Compare levels and trends fairly
Read a result as both a level and a trend, then compare it with a suitable target or peer group when comparable data exist. Before drawing conclusions, check that the organizations or products have similar definitions, markets, service mixes, populations, and observation windows. If those conditions differ, describe the comparison limits rather than presenting the figures as equivalent.
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NIST’s Baldrige approach asks organizations to examine results in terms of levels, trends, comparisons, and integration. For processes, it considers approach, deployment, learning, and integration—not simply whether a target was met once.
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5. Check the links between changes and outcomes
Ask whether a process or product change came before a shift in defects, service errors, customer experience, or another intended outcome; whether the intended users experienced the change; and whether the result persisted. A before-and-after association alone does not establish that the innovation caused the outcome. When attribution matters, use a stronger evaluation design or state the limits of what the available evidence can show. The Oslo Manual discusses uses of innovation data in multivariate analysis and policy evaluation, while emphasizing that indicators and their interpretation have limits.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which frameworks can help?
OECD/Eurostat Oslo Manual 2018
The fourth edition is international guidance for collecting, reporting, and using innovation data. It covers innovation activities, outcomes, data collection, indicators, and analysis across sectors. Use it as a reference for definitions and measurement design, not as a universal corporate KPI list.
NIST Baldrige Excellence Framework
NIST describes Baldrige as “a nonprescriptive framework that empowers your organization to reach its goals, improve results, and become more competitive.” Its categories include Leadership, Strategy, Customers, Measurement, Analysis, and Knowledge Management, Workforce, Operations, and Results. It provides an organizational assessment and improvement structure, rather than prescribing one set of indicators for every organization.
ISO quality-management guidance
ISO guidance covers quality assurance, evidence-based monitoring, statistical process control, and continual improvement. Guidance or certification can support a quality-management system, but certification by itself does not demonstrate that a product or service is high quality.
What measurement mistakes should you avoid?
- Counting ideas, patents, training hours, or spending as if they prove successful innovation.
- Calling a change an innovation without establishing that it is significantly different or improved and was made available or put into use.
- Using revenue as the sole outcome, or customer satisfaction as the sole quality measure.
- Comparing organizations or periods with materially different definitions, populations, markets, or measurement windows without explaining the difference.
- Combining unlike indicators into a single score without showing its components, weights, and assumptions. Dashboards, scoreboards, and composite indexes simplify performance; aggregation can hide trade-offs.
- Reporting improvement without a clear baseline, population, period, sampling approach, or account of missing data.
- Assuming that one framework, a certification, or a single KPI guarantees innovation or quality.
There is no universal threshold or KPI set established for every company, industry, or public program. Choose measures according to the decision and mission, and make gaps, comparability limits, and possible unintended incentives visible.
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