Kosten durch Datenqualitätsmängel in Experimenten
Definition
Lack of standardized metadata and provenance tracking in experiment execution leads to data quality issues, requiring costly rework and delaying publications or reuse.
Key Findings
- Financial Impact: 10-20% project budget overrun; €20,000-€100,000 per major study rework
- Frequency: Per experiment cycle (quarterly in R&D)
- Root Cause: Manual processes create 'black box' data with unknown provenance, reducing reproducibility
Why This Matters
This pain point represents a significant opportunity for B2B solutions targeting Biotechnology Research.
Affected Stakeholders
Researchers, Bioinformaticians, Project Leads
Deep Analysis (Premium)
Financial Impact
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Current Workarounds
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Methodology & Sources
Data collected via OSINT from regulatory filings, industry audits, and verified case studies.
Related Business Risks
Kapazitätsverluste durch manuelle Datenprotokollierung
DSGVO-Risiken bei sensiblen Biotechnologie-Daten
GoBD-Verstöße durch unzureichende Datenprotokollierung
Überwachungskosten für Fördermittelberichte
GoBD-Verstöße bei Fördermittel-Nachweisen
Strafen und Preisabschläge durch MFG-Nichteinhaltung
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