Data-Driven Decision Making (Feb 2023)

This presentation was used in a session for DDDM
- Overview
- 3 Use cases
- Key findings

Transcript

1. Data-Driven Decision Making in Japan 2023-02-21 Kenji Hiramoto Head of Data Strategy, Executive Officer
2. DDDM(Data Driven Decision Making) • Operation division EBPM(Evidence Based Policy Making) • Policy division DDDM & EBPM 2 Real Time Long term
3. DDDM(Data Driven Decision Making) • Operation division EBPM(Evidence Based Policy Making) • Policy division DDDM & EBPM 3 Real Time Long term EBPM with real time feedback
4. • DDDM by using big data －Human density －SNS messages －Disaster information • DDDM in administrative procedures (EBPM with real time feedback) －Service dashboard ∙ My number card ∙ Availability of hospital ∙ Vaccination records DDDM(Data Driven Decision Making) 4 Data Analysis Visualization Decision making or Policy making Action
5. V-RESAS (Regional Economy and Society Analyzing System ) 5 Use Cases 1 (DDDM by using big data)
6. • V-RESAS is a latest version of RESAS(Regional Economy and Society Analyzing System ) . • Government provide data and data analysis tool for municipalities. • Government and municipalities can decide action based on the data. V-RESAS 6 Human density Last Update Consumption Restaurant Hotel Event Employment Business location All Japan Favorite Description Area Category Human density Consumption Restaurant Hotel Event Employment Business Finance Business location Business finance https://v-resas.go.jp/
7. V-RESAS 7 Human density Last Update Consumption Restaurant Hotel Event Employment Business location Tokyo Favorite Description Area Category Human density Consumption Restaurant Hotel Event Employment Business Finance Business location Business finance Human density Consumption(Card) Restaurant Hotel Event Employment Business location Consumption(Store) https://v-resas.go.jp/
8. Human Density 8 Decision maker （Administration） Citizens In municipality In prefecture Cross prefecture Hour New positive patient Human density Last Update Consumption Restaurant Hotel Event Employment Business finance Business location (Same week) All Japan Favorite Description All Smartphone Operator Weekly Decision • New regulation • Event schedule • ・・・ Decision • Travel • Workplace • ・・・ Realtime
9. Human Density 9 Decision maker （Administration） Citizens In municipality In prefecture Cross prefecture Hour New positive patient Human density Last Update Consumption Restaurant Hotel Event Employment Business finance Business location (Same week) All Japan Favorite Description All Smartphone Operator Weekly Decision • New regulation • Event schedule • ・・・ Decision • Travel • Workplace • ・・・ Realtime
10. Hotel 10 Decision maker （Administration） Citizens In municipality In prefecture Cross prefecture Hour New positive patient Human density Last Update Consumption Restaurant Hotel Event Employment Business finance Business location (Same week) All Japan Favorite Description All Hotel Operator Monthly Decision • Campaign • Grants • ・・・ Decision •Travel •・・・ Monthly Weekly Hotel Decision •Business plan • ・・・ All traveler Family Group(Women) Group(Men) Single Group Couple New positive patient Traveler
11. SIP4D(Strategic Innovation Program for Disaster Risk Management) 11 Use case 2 (DDDM by using big data)
12. • SIP4D gather the various real time data from the disaster area and integrate them. SIP4D 12 Decision maker （Administration） Decision • Logistics • Medical service • Construction • Garbage • ・・・ Road information Shelter information Prefecture data DIMAPS Road damage data Private sector Drive history data EMIS Shelter data Prefecture Shelter data Municiparity Shelter data Road availability information Integrated shelter information Gathering Gathering Data fusion Data fusion Integrated disaster risk management map Damaged building Damage to medical facilities Landslide Disaster Information Water supply restoration information
13. My number dashboard 13 Use case 3 (DDDM in administrative procedures)
14. • Data was extracted from the application process and visualized. • Staffs can check the situation and do some actions. My number dashboard 14 Citizens Decision maker （Administration） Decision • Campaign • Grants • ・・・ Apply on website My number card Health insurance card Personal bank account Server Daily Other servers (Health, Bank)
15. 15 Key Findings
16. • A data-driven culture is important. • Leadership and vision is important. • Visualization is a critical feature of the legacy person. • It is important to accumulate basic data and provide a data platform. • APIs are the essential function for gathering data from various resources. IDs are needed for data fusion. • Training is essential for Data-driven decision making. • We should verify the decision-making results and make a feedback loop. • In the future, a combination of DDDM and Rule as Code(RaC) should be considered. Key Findings 16