Harmonization Center

Mapping fragmented survey fields into a unified financial inclusion intelligence model.

● COMPILED PARALLEL DATASETS ACTIVE
Active Dataset
A2F Survey
2023
Detected Fields
1,611
Mapped Indicators
12
Validated States
37
Data Harmonization Workflow
Raw Dataset
A2F_2023_Survey_Dataset_for_publishing.csv
Schema Profiling
1,611 fields detected
Mapping Rules
source field → canonical field
Intelligence Layer
dashboard, API, sandbox
Canonical Mapping Rules

These mappings show how raw survey fields are converted into standardized analytical fields.

Source Dataset Raw Field Canonical Field Category Status
A2F Survey 2023 state_name standard_state Geography Mapped
A2F Survey 2023 region standard_region Geography Mapped
A2F Survey 2023 lga_name standard_lga Geography Mapped
A2F Survey 2023 sector urban_rural_classification Geography Mapped
A2F Survey 2023 e6 standard_gender Demographics Mapped
A2F Survey 2023 banked banked_rate Financial Access Mapped
A2F Survey 2023 Mobile_money mobile_money_adoption Digital Financial Services Mapped
A2F Survey 2023 Savings_F formal_savings_indicator Savings Detected
A2F Survey 2023 Credit_F formal_credit_indicator Credit Detected
A2F Survey 2023 Insurance_F insurance_penetration Insurance Detected
A2F Survey 2023 FinHealth_Indicator_FINAL financial_health_score Financial Health Detected
A2F Survey 2023 Finlit_Cap_Final financial_literacy_capability Financial Capability Detected
Climate Dataset flood_risk / climate_zone climate_vulnerability_score Climate Intelligence Production Extension
Mapping Status Reference
Mapped

Field has been validated and linked to the canonical model. These fields are currently powering dashboard metrics, API responses, opportunity mapping and sandbox analytics.

Detected

Field has been automatically discovered during schema profiling and identified as potentially relevant. Final semantic validation may still be required before inclusion in production analytics.

Production Extension

Future integration target. Represents external datasets such as climate, geospatial, demographic or regulatory sources that can be harmonized into the unified intelligence model.

Why This Matters

EFInA datasets may differ by year, structure, question codes, response format and thematic modules. This harmonization layer ensures that once a dataset is profiled, its important fields are mapped into a stable canonical model. That canonical model then powers the dashboard, API layer, opportunity map and research sandbox without rebuilding the platform for every new survey round.