1) The national data strategy aims to realize a citizen-centric and sustainable human society by 2030 through effective data use and digital twins. 2) The strategy's vision is to allow anyone easy access to high-quality data and services to start businesses quickly. It will connect various data sources through a shared platform. 3) The strategy outlines principles of data use, priority actions to improve data management, and a roadmap to increase data maturity through 2030 with interim goals in 2023 and 2025.

1. National Data Strategy
and Data Maturity
2022-02-09

2. • We published the national data strategy on June 18,
2021.We will realize a citizen-centric society by 2030.
• Vision
−We aim to realize a sustainable human society. It is the
human-centric society that creates new value by achieving both
economic development and solving social issues. It is achieved
through a digital twin by using data.
−It is consistent with the vision of Society 5.0 that is the future
vision of Japan.
−Our society values trust and safety. It is essential to realize
high efficiency and hospitality services at the same time.
The National data strategy

3. • Anyone get data easily, start business quickly
and use high-quality services.
Vision for 2030 citizen-centric society
High value
data sets
Statistics
Realtime
sensor data
Livable
Base
registries
Open Data
＋Data
from private sectors
Platform
Start-ups
• Person
• Legal entities
• Land
• Administration
data
• Traffic
• Weather
Digital Twin
Service providers keep
their services, sustainably.
Trust＆ Safety

4. Principles
4
Use anytime,
anywhere
• Availability
• Quickness
• Cross-boarder
Connect
• Interoperability
• Efficiency
Control your
data yourself
• controllability
• Privacy
Safe
• Security
• Trust
• Quality
Create together
• Co-creation
• Creation of new value
Evidence Based administration
•Identify the priority data
•EBPM
•Business transformation
•Data management and open data
•Cultural change
Data ecosystem
•Data engineering for data
ecosystems
•Data standards
•Data quality management
•List of data asset in the
administrations
Maximize the value of data
•Rule management for data access
•Chanel management for the
various data accesses
•Open data
Principle of Data strategy
Principle of ministry’s action

5. Trust
（DFFT）
Architecture of our strategy
Data
Service Platform
Rule
Security
/
Privacy Services for citizens and businesses
Organization and human resource
Infrastructure, Asset
Tools
Maximizing the Value of services
Base registries Internal data
Statistics Open data

6. • Stakeholders
• Transactions
• Original copy
Trust
• DATA-EX
• Catalogue
Platform
• Base registries
• Open data
• Data engineering
Data
• Smart cities
• Disaster risk Management
Showcase
Priority Actions
6
Citizens use and store data with confidence
Citizens efficiently use data
Citizens can use various data
Citizens understand the value of data

7. • To make it a sustainable initiative, it is important to build on
the foundation.
The steps for realizing the data-driven organization
7
Findable
• Data catalogue
• Service
catalogue
• Platform
Usable
• Structured data
model
• Quality
management
Processable
• Machine
readable
Automatable
• Auto-check
• RPA（Robotic
Process
Automation）
Analyzable
• AI
• Big data analysis
We are here.

8. • Data connect to the world, so global interoperability is essential.
Concept of Data maturity
8
Data
Process
Governance
• Is the data of sufficient quality?
• ISO25012
• Are there any issues with the process?
• ISO25024
• Is the data being managed strategically?
• ISO8000-61
Usage
USER SIDE
SUPPLY SIDE
Are users making
use of the data?

9. • We provide some resources for users. And we provide a maturity
model and tools for data owners.
Activities for data maturity
Data design
Data
collection
Data
integration
External data
acquisition
Data
processing
Presentation
Other use
Data store
Delete
ISO25024
Reference data model
Base registries
Converter
Validator
Form DCAT-GOJ
Feedback
Data quality management guidebook
Data management guidebook
Data HR management framework
Open data guidebook
(Harnessing the open data)
Use cases
(Open data 100)
Reference data model
USER SIDE SUPPLY SIDE

10. • The data maturity model consist of Data view(ISO25012),
Process view(ISO25024) and Governance view(ISO8000-61)
−The check items have 4 levels.(Ad hoc, Part of, Basic, Sustainable)
Maturity model(Beta ver.)
10
Service Data Process & Governance
We measured some services.
It combined service maturity
model and data maturity model.
Evaluation report
Data maturity

11. A sustainable society through Trust and Quality
Roadmap
Stage 01 Stage 02 Stage 03 Stage 04
Data-driven
society
2023-03 2025 2030
 Trust
 Platform
 Data
 Trust base system
 Data exchange platform
 Formulate the guideline
 Data marketplace
 Pilot studies
 Identify the priority data  Base Registries
 Prototype project
11
 Data maturity model
Expansion to the
ministries and
local government

12. 12
Appendix

13. Data maturity(Data view)
• To improve data quality, it is necessary to establish a culture of managing data.
Accuracy
Completeness
Consistency
Credibility
Currentness
Accessibility
Compliance
Confidentiality
ISO/IEC 25012:2008
Software engineering
-- SQuaRE
-- Data quality model
Efficiency
Precision
Traceability
Understandabilit
y
Availability
Portability
Recoverability
Sample of data quality model
(Business register)

14. Data maturity (Process view)
• Data quality assurance is important.
Data design
Data
collection
Data
integration
External data
acquisition
Data
processing
Presentation
Other use
Data store
Delete
C
D
E
A
Other
Ministries
B
Sample of data quality model (ISO25000)
(Corporate information portal site)
Not updated
Low
Speed
Insufficient
update
No standard
procedure
Not real
time data
Coverage
Data design

15. Data maturity(Governance view)
• To improve data quality, it is necessary to establish a culture of managing data.
Data-related
support
Resource provision
Implementation
Data quality
planning
Requirements management
Data quality strategy
management
Data quality policy/Standard/
Procedures management
Data quality implementation
planning
Data quality
improvement
Root cause analysis &
solution development
Data cleansing
Process Improvement for data
nonconformity prevention
Data quality
control
Provision of data specification
& work instruction
Data processing
Data quality monitoring &
control
Data quality
assurance
Review of data quality issue
Provision of measurement
criteria
Measurement of data quality
& process performance
Evaluation of measurement
results
Data architecture
management
Data transfer management
Data operation management
Data security management
Data quality organization
management
Human resource management
Detailed structure of
data quality management
(ISO8000-61)
15
Sample of data quality model
(Business register)

16. We are building
the future society.
16