Data Model

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Supply Data
Institution Type misc/message-SIZE-info.png
Company Name misc/message-SIZE-info.png
Branch Name misc/message-SIZE-info.png
# of Locations misc/message-SIZE-info.png
Name of the Source misc/message-SIZE-info.png
# of Savings Accounts misc/message-SIZE-info.png
Total Savings Amount misc/message-SIZE-info.png
# of Loan Accounts misc/message-SIZE-info.png
Total Loan Amount misc/message-SIZE-info.png
# of Deposit Accounts misc/message-SIZE-info.png
Total Deposit Amount misc/message-SIZE-info.png
Description of Source misc/message-SIZE-info.png
Geographical Level 1 misc/message-SIZE-info.png
Geographical Level 2 misc/message-SIZE-info.png
Geographical Level 3 misc/message-SIZE-info.png
Demand Data
Population misc/message-SIZE-info.png
Rural Population misc/message-SIZE-info.png
Urban Population misc/message-SIZE-info.png
Male Population misc/message-SIZE-info.png
Female Population misc/message-SIZE-info.png
Adult Population misc/message-SIZE-info.png
Adult Literacy Male misc/message-SIZE-info.png
Adult Literacy Female misc/message-SIZE-info.png
Literate Adult Population misc/message-SIZE-info.png
Literate Youth Population misc/message-SIZE-info.png
Illiterate Population misc/message-SIZE-info.png
Unemployment population misc/message-SIZE-info.png
# of Households misc/message-SIZE-info.png
Name of the Source misc/message-SIZE-info.png
Credit Service utilization misc/message-SIZE-info.png
Insurance Utilization misc/message-SIZE-info.png
Mobile Money Service Utilization misc/message-SIZE-info.png
Geographical Level 1 misc/message-SIZE-info.png
Geographical Level 2 misc/message-SIZE-info.png
Geographical Level 3 misc/message-SIZE-info.png

Data Gathering

Challenge:
  • No universally accepted reporting standards
  • Fragmented data across multiple sources in several different formats
  • Majority of sources do not have digital / online data available for direct usage
 
FINclusionLab Approach
  • Identifying trusted sources, with input from local country experts
  • Consolidating all the datasets, including identifying relevant fields / elements and standardizing along these elements

Data Gathering

Challenge:
  • No universally accepted reporting standards
  • Fragmented data across multiple sources in several different formats
  • Majority of sources do not have digital / online data available for direct usage
 
FINclusionLab Approach
  • Identifying trusted sources, with input from local country experts
  • Consolidating all the datasets, including identifying relevant fields / elements and standardizing along these elements

Data Gathering

Challenge:
  • No universally accepted reporting standards
  • Fragmented data across multiple sources in several different formats
  • Majority of sources do not have digital / online data available for direct usage
 
FINclusionLab Approach
  • Identifying trusted sources, with input from local country experts
  • Consolidating all the datasets, including identifying relevant fields / elements and standardizing along these elements

Data Clean-Up

Challenge

  • Missing / redundant data that can compromise analysis (“garbage in / garbage out”)
  • Inconsistent data formats across data sources, making “apples to apples” comparisons difficult 

FINclusionLab Approach

  • Standardized, validated, and consistent data model, enhancing analytical accuracy
  • Data pre-processed and readied for analysis and report generation Analytics ready supply and demand data

Data Clean-Up

Challenge

  • Missing / redundant data that can compromise analysis (“garbage in / garbage out”)
  • Inconsistent data formats across data sources, making “apples to apples” comparisons difficult 

FINclusionLab Approach

  • Standardized, validated, and consistent data model, enhancing analytical accuracy
  • Data pre-processed and readied for analysis and report generation Analytics ready supply and demand data

Data Clean-Up

Challenge

  • Missing / redundant data that can compromise analysis (“garbage in / garbage out”)
  • Inconsistent data formats across data sources, making “apples to apples” comparisons difficult 

FINclusionLab Approach

  • Standardized, validated, and consistent data model, enhancing analytical accuracy
  • Data pre-processed and readied for analysis and report generation Analytics ready supply and demand data

Geospatial Enabling

Challenge

  • Opportunities to advance financial inclusion tend to be “localized,” requiring advanced mapping tools to target / prioritize investments
  • Without mapping capabilities, stakeholders lack an intuitive decision-making interface

FINclusionLab Approach

  • Geospatial visualizations and dashboards enabling quick identification of opportunities

Geospatial Enabling

Challenge

  • Opportunities to advance financial inclusion tend to be “localized,” requiring advanced mapping tools to target / prioritize investments
  • Without mapping capabilities, stakeholders lack an intuitive decision-making interface

FINclusionLab Approach

  • Geospatial visualizations and dashboards enabling quick identification of opportunities

Data Visualization

Challenge

  • Identifying interactions / dependencies across multiple variables
  • Quickly surfacing insights from large datasets

FINclusionLab Approach

  • User-friendly and stakeholder oriented presentation of the data and analyses
  • Drill-down capabilities for granular analysis

Data Visualization

Challenge

  • Identifying interactions / dependencies across multiple variables
  • Quickly surfacing insights from large datasets

FINclusionLab Approach

  • User-friendly and stakeholder oriented presentation of the data and analyses
  • Drill-down capabilities for granular analysis

Pages

Azerbaijan
About the Data_Azerbaijan DOWNLOAD
Benin
About the Data_Benin DOWNLOAD
Colombia Bogota
About the Data_Bogota DOWNLOAD
Burundi
About the Data_Burundi DOWNLOAD
Colombia
About the Data_Colombia DOWNLOAD
Ethiopia
About the Data_Ethiopia DOWNLOAD
Ghana
About the Data_Ghana DOWNLOAD
Ghana Demand Side Data DOWNLOAD
Ghana Supply Side Data DOWNLOAD
India
About the Data_India DOWNLOAD
India Cash Points DOWNLOAD
India Contextual/Demand data DOWNLOAD
India Andhra Pradesh
About the Data_India Andhra Pradesh DOWNLOAD
Andhra Pradesh Demand Side Data DOWNLOAD
Andhra Pradesh Supply Side Data DOWNLOAD
India Jharkhand
About the Data_India Jharkhand DOWNLOAD
Jharkhand Demand Side Data DOWNLOAD
Jharkhand Supply Side Data DOWNLOAD
India Karnataka
About the Data_India Karnataka DOWNLOAD
Karnataka Demand Side Data DOWNLOAD
Karnataka Supply Side Data DOWNLOAD
India Kerala
About the Data_India Kerala DOWNLOAD
India Odisha
About the Data_India Odisha DOWNLOAD
Odisha Demand Side Data DOWNLOAD
Odisha Supply Side Data DOWNLOAD
India Uttarakhand
About the Data_India Uttarakhand DOWNLOAD
Uttarakhand Demand Side Data DOWNLOAD
Uttarakhand Supply Side Data DOWNLOAD
Ivory Coast
About the Data_Ivory Coast DOWNLOAD
Ivory Coast Demand Side Data DOWNLOAD
Ivory Coast Supply Side Data DOWNLOAD
Kenya
About the Data_Kenya DOWNLOAD
Kenya Cash Points DOWNLOAD
Kenya Contextual/Demand data DOWNLOAD
Malawi
About the Data_Malawi DOWNLOAD
Mozambique
About the Data_Mozambique DOWNLOAD
Myanmar
About the data_Myanmar DOWNLOAD
Myanmar Cash Points DOWNLOAD
Myanmar Contextual/Demand Data DOWNLOAD
Product Data(Non-mapped)_Myanmar DOWNLOAD
Nigeria
About the Data_Nigeria DOWNLOAD
Nigeria Contextual/Demand Data DOWNLOAD
Nigeria Supply Side Data DOWNLOAD
Peru
About the Data_Peru DOWNLOAD
Acerca de los datos_Perú DOWNLOAD
Peru demand side data DOWNLOAD
Peru supply side data DOWNLOAD
Philippines
About the Data_Philippines DOWNLOAD
Rwanda
About the Data_Rwanda DOWNLOAD
Rwanda Demand Side Data DOWNLOAD
Rwanda Supply Side Data DOWNLOAD
Senegal
About the Data_Senegal DOWNLOAD
South Africa
About the Data_South Africa DOWNLOAD
South Africa Cash Points DOWNLOAD
South Africa Contextual/Demand Data DOWNLOAD
Tanzania
About the Data_Tanzania DOWNLOAD
Tanzania Cash Points DOWNLOAD
Tanzania Contextual/Demand data DOWNLOAD
Uganda
About the Data_Uganda DOWNLOAD
Uganda Cash Points DOWNLOAD
Uganda Contextual/Demand Data DOWNLOAD
Zambia
About the Data_Zambia DOWNLOAD