HUAWEI TECHNOLOGIES CO., LTD.

Transcription

1 HUAWEI TECHNOLOGIES CO., LTD.

2 Typical Carriers' big data platform architecture Personnel Competence Application management Location Insight Value Credit Operations O&M User experience improvement monetization Industry insight and analysis application planning 1. Have big data awareness to fully understand data, plan big data products and applications. 2. To build a big data internal and external good ecosystem Service capability Location Information Hobby Social relationship Capability openness and sharing Quality of Experience analysis and applications 1. User big data technology to analysis data value in telecom field 2. Understand the idea of carriers' big data application construction, be able to plan big data services Platform Data source 1 Data mining and data modeling Computing and storage Data collection B domain O domain M domain DPI Others Business Support - Competence Improvement Solution platform construction 1. Construct and design enterprise-level big data platform, including data governance of big data platform 2. Multi-domain data collected in a unified manner and modeling and storage

3 Managers Business Strategy and Management Training Technology and Application Professionals Huawei Universe -based Customer Insight Training Huawei Universe Data Governance Training Service Support Analysis Practice(Customer Retention) FusionInsight System Deployment and Management Training Hadoop Open Source Technologies Analysis Practice(Precision Marketing) Network Optimization Analysis Practice (Performance Monitoring) Analysis Practice (Network Optimization) Processing Technologies -related personnel Carriers' Application Best Practice Sharing Everyone Learns IT - Technology (MOOC) 2 Business Support - Competence Improvement Solution

4 Trends of Carriers s Services and Applications Course Outline The development of big data has shifted from technology-driven to business-driven, and players of each domain concern more about the value of big data itself. This course focuses on sharing and discussion of telecom operators' big data application practice. It introduces big data application cases in typical industries and helps trainees develop big data application methods, widen business view, and enhance practice experience to support business success. Location insight Location information Carriers' big data service capability and application building roadmap managem ent Value credit operation Service recommenda tion Value assessment User experienc e O&M Enterprise efficiency Preference Social relationship Experience monetizati on Antispoofing detectio n Association prediction Industry insight and analysis BSS domain OSS domain MSS domain External data Plan major application directions Develop service products Build service capabilities B/O/M/I domain coordination Basics of Data: embrace the era of big data Background of big data Characteristics of big data concept Trends of big data Telecom operators' digital transformation Practice: Enable telecom operators' big data application treasure box service capability development roadmap Telecom big data application management + smart operation + smart O&M + smart monetization Application Best Practice Sharing Illustrative base: Build the telecom operators' enterpriselevel big data platform operation architecture Key platform technologies Internet big data architecture Telecom operators' big data mash-up architecture Success stories from others: Reveal industry big data application Internet ad applications E-business industry Financial industry Business site selection industry News 3 This Course Can Help You Master the end-to-end core technologies, typical process, and key elements for developing big data applications. Understand telecom operators' typical practices and summarize the big data service capability building methodology. Learn other industries' big data application practice cases for approach development. Business Support - Competence Improvement Solution Training Design Target Audience: big data-related personnel, including executives, marketing personnel, group customers, product managers, and departments related to big data application (business support Dept, information Dept, network Dept, and others) Learning Modes: case sharing and discussion Duration: 3 days

5 Online Prelearning(MOOC) Common Technologies Scenario-based Dry Run Everyone Learns IT - Technology (MOOC) (2 weeks) basic and features ecosystem Hadoop core components Typical applications of big data Processing Technologies Discussion on applications of big data on network optimization Data analysis and mining Data mining tools and languages Hadoop technology Analysis and Mining Practice (Network Optimization) Analysis and Mining Practice (Performance Monitoring) Hadoop data processing based on network optimization. -based data analysis and application on network optimization Hadoop data processing based on performance monitoring -based data analysis and application on performance monitoring 4 Business Support - Competence Improvement Solution

6 Online Prelearning(MOOC) Common Technologies Scenario-based Dry Run Everyone Learns IT - Technology (MOOC) (2 weeks) basic and features ecosystem Hadoop core components Typical applications of big data Processing and Mining Technologies Discussion on applications of big data on network optimization Data analysis and mining Data mining tools and languages Hadoop technology Analysis Practice(Customer Retention) Data analysis and mining Data mining tools and languages Training Universe MOOC (1 weeks) Data modeling of customer Data analysis and mining insight based on Universe Huawei Universe Data Governance Training Universe MOOC (1 weeks) Data governance basic Data analysis and application on customer retention Analysis Practice(Precision Marketing) Data analysis and mining Data analysis and application on Data mining tools and languages precise recommendation Huawei Universe -based Customer Insight Data governance based on Universe 5 Business Support - Competence Improvement Solution

7 1 Review the competency domains, covering key roles 2 Cover four key areas, E2E competency improvement 3 practice sharing 4 Scenario-based practices to improve competency service process Key role of the carrier competency model position Learning development concepts Data application competency Data mining competency Data governance competency Platform O&M competency Carrier domain application case Case study Huawei big datarelated application and practice Internet best practice sharing of big data Scenario: Application scenarios in the telecom field Data: Simulates carrier s O/B domain data Faculty: Rich practical experience and expert 6 Business Support - Competence Improvement Solution

8 Customer requirement Solution design Improve key competency Benefit 1. Sort out 4 core positions and established 12 competence items Product design and operational staff: Product planning application Innovation Capability openness Obtain management investment data in real time Supporting 4 data business scenarios Data mining staff: Data analysis & mining Mathematical algorithm application Release 3 industry analysis report 1 data capability openness field Big Data Establish big data operation organizations, undertake business strategy 2. Course design based on scenario-based drills and project practice Data governance staff: Data architecture design Data interface design & clean ETL process development Cultivation big data talent, build big data position system 3. Construct personnel competency improvement plan for 3 years Platform O&M staff: application platform O&M Hadoop platform O&M Cloud platform O&M Business Support - Competence Improvement Solution

9 Copyright 2017 Huawei Technologies Co., Ltd. All Rights Reserved. The information in this document may contain predictive statements including, without limitation, statements regarding the future financial and operating results, future product portfolio, new technology, etc. There are a number of factors that could cause actual results and developments to differ materially from those expressed or implied in the predictive statements. Therefore, such information is provided for reference purpose only and constitutes neither an offer nor an acceptance. Huawei may change the information at any time without notice.