Governance Data Strategy Inquiry Management Business Consulting Data Product Management Data ETL Data Engineering IU Dev Data Solutions Cloudera PS/PSE Data Science 1-4 Machine Learning 1-2 DSP ML OCR Voice Speech NLP Speech & Voice Planning SET Delivery Infra Observability Infra Data Management Data Platform Data Labs Engineering Infrastructure
3 axis such as Human Resource, Goods and Capitals Mono(Goods) Promotion Promotion of understanding data Hito(Human Resource) Education Increase people who can utilize data Kane(Capital) Value Contribute to business profit
according to the business situation Offensive Data Utilization Support Data Platform Data Planner Data Scientist / ML Engineer Services LINE Users Data Management Dept. Data Labs
and legal staff Strengthening Defense Making rules Penetration Bottom Up Top Down Inquiry management Authorization Information Security Privacy Compliance Gathering requests Identifying issues Documentation Announcement Seminar Survey Improvement
and Product Management Data from Services Data ETL Cross-group Data Service-specific Data Data Products Platform Service Utilizing based on Data Governance
/ Data Manager Data Planner Data Scientist Data Product Manager ML Engineer Data Platform Engineer ETL Engineer Data Management Department Information Security Privacy Legal Data Governor
Maturity Level Lv0 Lv1 Lv2 Lv3 Strategy / Offense D : Education for new comer A : Data Utilization Defense B : Data Open Orientation System C : IU Boot Camp
Type A1 Sharing session PM, Planner 1.5h Presentation A2 Workshop of data utilization PM, Planner 3h x 2 Workshop B1 Data Open Orientation All 1.5h Presentation C1 IU Boot Camp Planner, Engineer 3h Hands-on AC1 Tableau User Session Tableau User 1.5h Presentation D1 Orientation for new comer New Comer 1h Presentation
Preparation for Using Data Data Owner Data Integration Information Security Compliance Common ID Privacy Policy Legal Confirmation FSA JV Agreement Internal Contracts Tax Requirements and others… Application Approval Data Ingestion Labeling Access Control A kind of wall…
a transaction KPI of Own Business Viewing Information Starting Trading Access Opening Account User Logs User Logs Transaction User Logs Campaign Analysis Report Understand Service Download App Application Upload KYC Check Status Consider Investment Deposits Withdrawals Order Contract
and non-fintech users Fintech Data LINE User Data (ML Completed) lookalike Fintech User Scoring by machine learning Non-fintech User Similarity Score A:76% B:82% C:55% ・・・
data Fintech Data LINE User Data (ML Completed) Algorithm Fintech User Non-Fintech User Estimate the attributes of non-fintech users by machine learning (Predictive Data) …
LINE including fintech data with very strong characteristics Performing various tasks from data analysis to contract conclusion work Building relationships globally