Scoring rubric for DBI drivers
| Driver | Score 1 (Low) | Score 2 (Medium) | Score 3 (High) |
|---|---|---|---|
| Data consumption: No. of users | Solution used by < 10% of target population | Solution used by 10–50% of target population | Solution used by > 50% of target population |
| Data consumption: Data literacy | Usage requires advanced technical skills (e.g. data scientists only) | Usage requires specific business training (e.g. business analysts) | Intuitive usage accessible to non-technical staff (high democratization) |
| Data consumption: Time-to-market | Slow deployment (>12 months), typical of rigid waterfall cycles | Standard deployment (6–12 months) | Fast deployment (<6 months), typical of Agile/MVP approaches |
| Business value: Income growth | Expected income growth is < 10% of project cost | Expected income growth is 10%-50% of project cost | Expected income growth is > 50% of project cost |
| Business value: Risk management | Standard compliance with no significant risk reduction | Improves monitoring of specific operational risks | Critical for regulatory compliance, fraud prevention or strategic risk avoidance |
| Business value: Operational efficiency | Marginal efficiency gains (<10% time/cost reduction) | Significant improvement in specific tasks (10–30% reduction) | Radical process re-engineering or automation (>30% reduction) |
| Effort: Operational expenditure | Minimal incremental costs (uses existing infrastructure) | Moderate costs requiring budget allocation (e.g. standard cloud fees). | High recurring costs (e.g., expensive GPU instances, proprietary licenses) |
| Effort: Technical debt management | No new technical debt introduced; architecture enhances existing system stability | Technical debt is tracked and managed with minimal impact on operations | Significant unaddressed technical debt and system instability introduced |
| Effort: Data governance overhead | Uses standard, non-sensitive data with existing policies. | Requires specific privacy/security measures (e.g. GDPR compliance for customer data) | Complex governance required for highly sensitive or unstructured data (e.g. health data, heavy NLP requirements) |
| Driver | Score 1 (Low) | Score 2 (Medium) | Score 3 (High) |
|---|---|---|---|
| Data consumption: No. of users | Solution used by < 10% of target population | Solution used by 10–50% of target population | Solution used by > 50% of target population |
| Data consumption: Data literacy | Usage requires advanced technical skills (e.g. data scientists only) | Usage requires specific business training (e.g. business analysts) | Intuitive usage accessible to non-technical staff (high democratization) |
| Data consumption: Time-to-market | Slow deployment (>12 months), typical of rigid waterfall cycles | Standard deployment (6–12 months) | Fast deployment (<6 months), typical of Agile/MVP approaches |
| Business value: Income growth | Expected income growth is < 10% of project cost | Expected income growth is 10%-50% of project cost | Expected income growth is > 50% of project cost |
| Business value: Risk management | Standard compliance with no significant risk reduction | Improves monitoring of specific operational risks | Critical for regulatory compliance, fraud prevention or strategic risk avoidance |
| Business value: Operational efficiency | Marginal efficiency gains (<10% time/cost reduction) | Significant improvement in specific tasks (10–30% reduction) | Radical process re-engineering or automation (>30% reduction) |
| Effort: Operational expenditure | Minimal incremental costs (uses existing infrastructure) | Moderate costs requiring budget allocation (e.g. standard cloud fees). | High recurring costs (e.g., expensive |
| Effort: Technical debt management | No new technical debt introduced; architecture enhances existing system stability | Technical debt is tracked and managed with minimal impact on operations | Significant unaddressed technical debt and system instability introduced |
| Effort: Data governance overhead | Uses standard, non-sensitive data with existing policies. | Requires specific privacy/security measures (e.g. | Complex governance required for highly sensitive or unstructured data (e.g. health data, heavy |
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