From ipad-kit
This skill should be used when the user asks about IPAD maturity levels, L1-L5 assessment, maturity scoring, progression criteria, transition requirements, maturity dimensions, or maturity benchmarks. Triggers: maturity level, L1-L5, assessment, maturity score, progression criteria, transition criteria, maturity dimension, ad-hoc, centralised, federated, cognitive, autonomous, data maturity.
npx claudepluginhub tractorjuice/ipad-kit --plugin ipad-kitThis skill uses the workspace's default tool permissions.
The IPA Maturity Model defines five progressive levels of data management, analytics, and governance capability, calibrated specifically for Investment Promotion Agencies. Each level builds on the foundations of the previous level.
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The IPA Maturity Model defines five progressive levels of data management, analytics, and governance capability, calibrated specifically for Investment Promotion Agencies. Each level builds on the foundations of the previous level.
| Level | Name | Key Characteristic |
|---|---|---|
| L1 | Ad-Hoc (Siloed) | Manual, reactive, spreadsheet-based data in departmental silos |
| L2 | Centralised (Warehoused) | Central database, basic ETL, automated dashboards, IT-controlled |
| L3 | Federated (Mesh-in-Spirit) | Domain ownership, central standards, API-connected, data products |
| L4 | Cognitive (AI-Native) | AI-augmented, predictive/prescriptive analytics, knowledge graphs |
| L5 | Autonomous | Self-organising, self-governing data platform (theoretical) |
| Dimension | What It Measures |
|---|---|
| Data Governance (DG) | Data ownership, standards enforcement, classification, stewardship, decision rights |
| Data Quality (DQ) | Completeness, consistency, accuracy, timeliness, uniqueness, validity monitoring |
| Analytics (AN) | Sophistication of analytical processing (descriptive through cognitive) |
| Technology (TC) | Infrastructure patterns, platform capabilities, automation level |
| Organisational Capability (OC) | Skills, roles, culture, data literacy, staffing model |
| Security (SC) | Access controls, encryption, threat detection, cross-agency security, compliance |
| Dimension | L1: Ad-Hoc | L2: Centralised | L3: Federated | L4: Cognitive | L5: Autonomous |
|---|---|---|---|---|---|
| Data Governance (DG) | Informal, no classification or access policies | Central IT ownership, top-down standards emerging | Federated (central WHAT, domain HOW), CDO as facilitator | Full AI governance, bias monitoring, ethical framework | Autonomous enforcement with human override |
| Data Quality (DQ) | Quality unmonitored, no formal data models | Quality checks periodic and reactive, manual dictionary | Automated quality across 6 dimensions, data contracts, lineage | AI-augmented quality, self-healing pipelines, sustained thresholds | Self-organising quality management, autonomous anomaly resolution |
| Analytics (AN) | Manual reports, spreadsheet charts, backward-looking | Automated dashboards, standard reports, basic BI | Diagnostic + emerging predictive, self-service analytics | Prescriptive analytics, NLP interfaces, automated insights | Fully cognitive, proactive insight generation without requests |
| Technology (TC) | Desktop tools, basic departmental databases, email | Data warehouse, BI tools, basic ETL, some APIs | Data fabric/mesh, API-first, IaC, CI/CD for pipelines | ML/AI platforms, knowledge graphs, GenAI, model registry | Self-healing, self-optimising, quantum-safe platforms |
| Organisational Capability (OC) | No data roles, knowledge in individuals | Central data/IT team, limited self-service | Domain data owners, data stewards, literacy programmes | Data scientists, AI engineers, AI literacy for all | Platform engineers, strategic human oversight only |
| Security (SC) | No formal security controls, manual file exchange | Basic access controls, batch imports, structured exports | API-based access controls, fine-grained authorisation | PQC migration planning, real-time threat detection, zero-trust | Quantum-safe cryptography, autonomous threat response |
| Category | Required |
|---|---|
| Data Governance (DG) | Data ownership assigned; classification applied (Confidential + Restricted tiers) |
| Data Quality (DQ) | Basic quality checks in place for centralised data |
| Analytics (AN) | BI/reporting tool deployed; at least 3 standard reports produced |
| Technology (TC) | Central data store for at least 2 core domains; formal data model documented |
| Organisational Capability (OC) | At least one dedicated data/IT role for central store |
| Security (SC) | Basic access controls; at least 2 data sources feeding central store through defined processes |
| Principles | All 10 CRITICAL principles documented as commitments; P6 basic controls; P9 quality baseline |
| Category | Required |
|---|---|
| Data Governance (DG) | Federated model operational; CDO role established; regular governance forum running |
| Data Quality (DQ) | Automated monitoring across 6 dimensions; data contracts for 3+ producer-consumer pairs |
| Analytics (AN) | Self-service analytics for domain analysts; diagnostic capabilities emerging |
| Technology (TC) | API-first for 3+ external sources; automated pipelines with quality gates; IaC |
| Organisational Capability (OC) | Domain data owners across all major domains; data literacy programme |
| Security (SC) | API-based access controls; fine-grained authorisation; sharing with 2+ external partners |
| Principles | All HIGH principles addressed; P10 (3+ data products), P14 (federated governance), P20-P22 (DevOps) |
| Category | Required |
|---|---|
| Data Governance (DG) | AI governance framework approved; ethical framework operational (incl. dark data) |
| Data Quality (DQ) | Quality scores above thresholds sustained 12+ months |
| Analytics (AN) | Diagnostic across all domains; predictive models for 2+ use cases with validated accuracy |
| Technology (TC) | ML/AI platform with model registry; GenAI piloted for 1+ use case |
| Organisational Capability (OC) | Data science and AI engineering roles; AI literacy programme for non-technical staff |
| Security (SC) | Post-quantum cryptography migration plan documented |
| Principles | All 26 mandatory and enforced; P24 (AI governance), P23 (dark data), P13/P17/P25 fully implemented |
| Category | Required |
|---|---|
| Data Governance (DG) | Autonomous governance with human override |
| Data Quality (DQ) | Self-organising quality management; autonomous anomaly resolution |
| Analytics (AN) | Proactive insight generation without explicit requests |
| Technology (TC) | Self-healing, self-optimising platforms |
| Organisational Capability (OC) | Platform engineering focus; strategic human oversight |
| Security (SC) | Quantum-safe cryptography operational; autonomous threat response |
| Principles | All principles autonomously enforced |
Overall maturity level = minimum score across all 6 dimensions (weakest-link approach).
An IPA cannot claim Level 3 if any dimension remains at Level 1. This ensures advancement claims are genuine across all capabilities.
| Answered Questions | Confidence |
|---|---|
| 90-100% | High confidence |
| 70-89% | Medium confidence |
| 50-69% | Low confidence |
| < 50% | Insufficient -- reassessment recommended |
| Maturity Level | Analytics Tier | Capabilities |
|---|---|---|
| L1: Ad-Hoc | Descriptive (basic) | Manual reports, spreadsheet charts |
| L2: Centralised | Descriptive (automated) | Automated dashboards, standard reports, trend analysis |
| L3: Federated | Diagnostic + Predictive (emerging) | Root cause analysis, cross-domain diagnostics, early ML |
| L4: Cognitive | Prescriptive + AI | AI-generated recommendations, NLP, automated insights |
| L5: Autonomous | Cognitive (full) | Proactive insights, autonomous analysis, self-optimising |
| Framework Layer | L1 | L2 | L3 | L4 | L5 |
|---|---|---|---|---|---|
| L1: FDI Investment Drivers | Partial | Full | Full | Full | Full |
| L2: Raw Sources | Partial | Full | Full | Full | Full |
| L3: Data Fabric | -- | Partial | Full | Full | Full |
| L4: Data Stores | -- | Partial | Full | Full | Full |
| L5: 360 Insights | -- | Partial | Partial | Full | Full |
| L6: Insight Delivery | Partial | Full | Full | Full | Full |
| L7: Stakeholder Engagement | Partial | Partial | Full | Full | Full |
| L8: IPA Outcomes | Partial | Partial | Full | Full | Full |
| ICA Security Layer | Basic | Standard | Full | Enhanced | Autonomous |
| Dimension | L1 | L2 | L3 | L4 | L5 |
|---|---|---|---|---|---|
| Composite Minimum | 0.50 | 0.65 | 0.80 | 0.90 | 0.95 |
| Completeness | 0.40 | 0.60 | 0.80 | 0.90 | 0.95 |
| Consistency | 0.40 | 0.60 | 0.75 | 0.85 | 0.95 |
| Accuracy | 0.50 | 0.65 | 0.80 | 0.90 | 0.95 |
| Timeliness | 0.40 | 0.55 | 0.75 | 0.85 | 0.95 |
| Uniqueness | 0.50 | 0.65 | 0.80 | 0.90 | 0.95 |
| Validity | 0.50 | 0.65 | 0.80 | 0.90 | 0.95 |
| Level | Funnel Coverage |
|---|---|
| L1: Ad-Hoc | Outreach + Lead (basic), Delivery (manual) |
| L2: Centralised | Full funnel with centralised tracking |
| L3: Federated | Full funnel with domain ownership and automated monitoring |
| L4: Cognitive | AI-augmented funnel with predictive conversion and prescriptive actions |
| L5: Autonomous | Self-optimising funnel with autonomous lead scoring and investment matching |
Reassessment annually or after significant capability changes (e.g., major platform migration, organisational restructure, AI platform deployment).