220209 nds

220209 nds

• 1.
  National Data Strategy andData Maturity 2022-02-09
• 2.
  • We publishedthe 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 getdata 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 ourstrategy 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 makeit 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 connectto 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 providesome 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 datamaturity 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 societythrough 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 (Processview) • 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 thefuture society. 16