KPI Visualization, Prediction for LINE Fukuoka Service Ops

Kazuhiro Maeda
LINE Fukuoka DataLabs Data Analysis Team Data Scientist
https://linedevday.linecorp.com/2020/ja/sessions/0556
https://linedevday.linecorp.com/2020/en/sessions/0556

2. About me Self introduction › Analysis to drive service/project growth. › Analysis to promote business improvement within LINE Fukuoka Corp. › Manage Analytical Projects / Products. My Roles Skills › Data analysis processing using the R language in general. MAEDA Kazuhiro › Develop center – DataLabs – Data Analysis Team › Team Manager, Data Scientist
3. Agenda › Introduction of LINE Fukuoka Corporation › Issues in Operation Tasks of LINE Fukuoka › VIIM Project › Centralized Prediction Project › In future direction
4. Introduction of LINE Fukuoka Corporation
5. The 4 Roles of LINE Fukuoka Our Business Business Planning Engineering Creative Works Operations
6. Issues in Operation Tasks of LINE Fukuoka for ”always data driven”
7. for “always data driven” Three basic phases the data Collect the data Apply the data Analyse
8. for “always data driven” Three basic phases Collect Consolidate and manage relevant data in one place › Automatically collect diverse data › convert to an easy-to- use format for analysis Analyse Analyze aggregated data › Calculate metrics to deal with › Visualization › Extract issues etc… Apply Implement Analysis Results › Planning of business plans based on time- series forecasts › Apply machine learning etc…
9. The three NOs we had Issues that Prevent Data Driven in LINE Fukuoka NO integrated data collection for operations NO metric measurement and/or visualization framework NO cross-organizational projects in data application
10. From NO to BUILD Three approaches for data driven BUILD integrated data collection for operations BUILD metric measurement and/or visualization framework BUILD cross-organizational projects in data application
11. VIIM Project VIsualization – Improvement - Management
12. Purposes of this project › Consolidate and visualize data that management and field members can use to make decisions › Based on visualized data, the environment/system for improvement measures and future directions is constructed.
13. Cross-Organizational Project Structure › PM/PdM, requirements definition › dashboard construction Digital Planning Team › application development › construction of data acquisition systems Global Operation Team › overall system construction › in-house IT support Work Improvement Team › data pipeline construction › dashboard construction Data Analysis Team
14. Architecture of this system Referred DB other Products Data Lake Data Ware House CMS CMS Log data DB Files Storages LINE Fukuoka DB
15. Dashboard Visualization
16. Achievements Consolidate data from many departments into one place Build a visualization platform for KPI metrics Reduce field operational costs
17. Time series forecast of workload Centralized Prediction Project
18. Purposes of this project › Established a framework for predicting workload that affects the operation › Delivering more accurate predictions using data science techniques Centralized Prediction Project
19. Dashboard Architecture of this system - batch Input data storage manipulating data training, predicting Raw data Events model: DeepAR, prophet, etc… predicted result Dashboard
20. Online time series prediction tool
21. Achievements Provide accurate predictions Flexible design for predictive systems Both online and batch systems are available
22. In future…
23. Toward true data driven Three approaches for data driven Building a more efficient data management and operational structure Building an integrated framework for time series forecasting Streaming other operations using machine learning
24. Thank you