Data Literacy for Innovation, R&D & Experimentation Teams Playbook
- Practitioner
- Intermediate
- Template Included
A framework for building data literacy within innovation, R&D, and experimentation teams — rigorous kill-criteria design, distinguishing genuine signal from noise in early-stage data, and resisting sunk-cost-driven reinterpretation of disappointing results — addressing the specific risk of innovation teams unconsciously reinterpreting ambiguous data to favor continuing an initiative they're emotionally invested in.
Isn't some optimism and persistence necessary for innovation to
succeed, given how many genuine breakthroughs initially look unpromising? Genuine persistence based on real signal is valuable, but this is distinct from unconsciously reinterpreting disappointing data as more promising than it actually is due to sunk-cost investment — the framework below is specifically about maintaining that distinction through pre-committed kill criteria.
What's the most effective safeguard against sunk-cost bias in
innovation data interpretation? Pre-registering specific kill criteria before running an experiment or pilot — deciding in advance what result would mean stopping — since this removes the after-the-fact interpretive flexibility that sunk-cost bias exploits.
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