Cost of Poor Data Quality
Definition
Poor data management leads to discrepancies in clinical and research data, requiring rework that increases costs and delays biotech projects.
Key Findings
- Financial Impact: 5-10% of project budget in rework costs (industry standard for biotech trials)
- Frequency: Per study or trial
- Root Cause: No single source of truth for trial and research data
Why This Matters
The Pitch: Biotechnology firms in Australia 🇦🇺 incur 5-10% project cost overruns from data rework. Automation of discrepancy reconciliation eliminates this risk.
Affected Stakeholders
Biostatisticians, Clinical Data Managers, Principal Investigators
Deep Analysis (Premium)
Financial Impact
Financial data and detailed analysis available with full access. Unlock to see exact figures, evidence sources, and actionable insights.
Current Workarounds
Financial data and detailed analysis available with full access. Unlock to see exact figures, evidence sources, and actionable insights.
Get Solutions for This Problem
Full report with actionable solutions
- Solutions for this specific pain
- Solutions for all 15 industry pains
- Where to find first clients
- Pricing & launch costs
Methodology & Sources
Data collected via OSINT from regulatory filings, industry audits, and verified case studies.
Related Business Risks
Capacity Loss from Data Bottlenecks
Decision Errors from Data Silos
TGA CTN/CTA Notification Costs
Biosafety Non-Compliance Fines
HREC and SSA Approval Delays
Embryo Research Licensing Overhead
Request Deep Analysis
🇦🇺 Be first to access this market's intelligence