为电信服务供应商的智能大数据信息业务培训

课程编码

bdbitcsp

课程时长

35 小时 通常来说是5天,包括中间休息。

要求

  • Should have basic knowledge of business operation and data systems in Telecom in their domain
  • Must have basic understanding of SQL/Oracle or relational database
  • Basic understanding of Statistics (in Excel levels)

课程概览

概观

Communication服务提供商(CSP)面临着降低成本和最大化每用户平均收入(ARPU)的压力,同时确保出色的客户体验,但数据量不断增长。全球移动数据流量将以2016年的78%的复合年增长率(CAGR)增长,达到每月10.8艾字节。

同时,CSP正在生成大量数据,包括呼叫详细记录(CDR),网络数据和客户数据。充分利用这些数据的公司可以获得竞争优势。根据经济学人智库(Economist Intelligence Unit)最近的一项调查,使用数据导向决策的公司可以将生产率提高5-6%。然而,53%的公司仅利用其有价值数据的一半,而四分之一的受访者表示大量有用数据尚未开发。数据量非常大,无法进行手动分析,大多数传统软件系统无法跟上,导致丢弃或忽略有价值的数据。

借助Big Data &Analytics的高速,可扩展的大数据软件,CSP可以挖掘所有数据,以便在更短的时间内做出更好的决策。不同的Big Data产品和技术提供了一个端到端的软件平台,用于收集,准备,分析和呈现大数据的洞察力。应用领域包括网络性能监控,欺诈检测,客户流失检测和信用风险分析。 Big Data和分析产品可以扩展到处理数TB的数据,但是这些工具的实现需要新的基于云的数据库系统,如Hadoop或大规模并行计算处理器(KPU等)

本期针对Telco的Big Data BI课程涵盖了CSP为提高生产力和开辟新业务收入流而投资的所有新兴领域。该课程将提供完整的360度全方位视图,以便在Telco中查看Big Data BI,以便决策者和管理人员可以非常全面地了解Telco中Big Data BI的可能性,从而提高生产率和收益。

课程目标

该课程的主要目标是在电信BusinessMarketing /销售,网络运营,财务运营和客户关系Management )的4个领域引入新的Big Data商业智能技术。学生将被介绍如下:

  • 引入到Big Data -什么是4Vs(音量,速度,种类和准确性)在Big Data -产生-从电信透视提取和管理
  • Big Data分析与旧数据分析的区别
  • Big Data的内部理由-Telco观点
  • Hadoop生态系统简介 - 熟悉所有Hadoop工具,如Hive ,Pig,SPARC,以及它们如何用于解决Big Data问题
  • 如何提取Big Data来分析分析工具 - Business Analysis如何通过集成的Hadoop仪表板方法减少收集和分析数据的痛点
  • 针对Telco的Insight分析,可视化分析和预测分析的基本介绍
  • 客户流失分析和Big Data Big Data分析可以减少客户流失和客户对电信案例研究的不满
  • 网络元数据和IPDR的网络故障和服务故障分析
  • 销售和运营数据的财务分析 - 欺诈,浪费和ROI估算
  • 客户获取问题 - 目标营销,客户细分和销售数据的交叉销售
  • 所有Big Data分析产品的介绍和摘要以及它们适用于Telco分析空间的位置
  • 结论 - 如何采用分步方法在您的组织中引入Big Data Business Intelligence

目标观众

  • Telco CIO办公室的网络运营,财务经理,CRM经理和顶级IT经理。
  • 电信Business分析师
  • CFO办公室经理/分析师
  • 运营经理
  • 质量保证经理

Machine Translated

课程大纲

Breakdown of topics on daily basis: (Each session is 2 hours)

Day-1: Session -1: Business Overview of Why Big Data Business Intelligence in Telco.

  • Case Studies from T-Mobile, Verizon etc.
  • Big Data adaptation rate in North American Telco & and how they are aligning their future business model and operation around Big Data BI
  • Broad Scale Application Area
  • Network and Service management
  • Customer Churn Management
  • Data Integration & Dashboard visualization
  • Fraud management
  • Business Rule generation
  • Customer profiling
  • Localized Ad pushing

Day-1: Session-2 : Introduction of Big Data-1

  • Main characteristics of Big Data-volume, variety, velocity and veracity. MPP architecture for volume.
  • Data Warehouses – static schema, slowly evolving dataset
  • MPP Databases like Greenplum, Exadata, Teradata, Netezza, Vertica etc.
  • Hadoop Based Solutions – no conditions on structure of dataset.
  • Typical pattern : HDFS, MapReduce (crunch), retrieve from HDFS
  • Batch- suited for analytical/non-interactive
  • Volume : CEP streaming data
  • Typical choices – CEP products (e.g. Infostreams, Apama, MarkLogic etc)
  • Less production ready – Storm/S4
  • NoSQL Databases – (columnar and key-value): Best suited as analytical adjunct to data warehouse/database

Day-1 : Session -3 : Introduction to Big Data-2

NoSQL solutions

  • KV Store - Keyspace, Flare, SchemaFree, RAMCloud, Oracle NoSQL Database (OnDB)
  • KV Store - Dynamo, Voldemort, Dynomite, SubRecord, Mo8onDb, DovetailDB
  • KV Store (Hierarchical) - GT.m, Cache
  • KV Store (Ordered) - TokyoTyrant, Lightcloud, NMDB, Luxio, MemcacheDB, Actord
  • KV Cache - Memcached, Repcached, Coherence, Infinispan, EXtremeScale, JBossCache, Velocity, Terracoqua
  • Tuple Store - Gigaspaces, Coord, Apache River
  • Object Database - ZopeDB, DB40, Shoal
  • Document Store - CouchDB, Cloudant, Couchbase, MongoDB, Jackrabbit, XML-Databases, ThruDB, CloudKit, Prsevere, Riak-Basho, Scalaris
  • Wide Columnar Store - BigTable, HBase, Apache Cassandra, Hypertable, KAI, OpenNeptune, Qbase, KDI

Varieties of Data: Introduction to Data Cleaning issue in Big Data

  • RDBMS – static structure/schema, doesn’t promote agile, exploratory environment.
  • NoSQL – semi structured, enough structure to store data without exact schema before storing data
  • Data cleaning issues

Day-1 : Session-4 : Big Data Introduction-3 : Hadoop

  • When to select Hadoop?
  • STRUCTURED - Enterprise data warehouses/databases can store massive data (at a cost) but impose structure (not good for active exploration)
  • SEMI STRUCTURED data – tough to do with traditional solutions (DW/DB)
  • Warehousing data = HUGE effort and static even after implementation
  • For variety & volume of data, crunched on commodity hardware – HADOOP
  • Commodity H/W needed to create a Hadoop Cluster

Introduction to Map Reduce /HDFS

  • MapReduce – distribute computing over multiple servers
  • HDFS – make data available locally for the computing process (with redundancy)
  • Data – can be unstructured/schema-less (unlike RDBMS)
  • Developer responsibility to make sense of data
  • Programming MapReduce = working with Java (pros/cons), manually loading data into HDFS

Day-2: Session-1.1: Spark : In Memory distributed database

  • What is “In memory” processing?
  • Spark SQL
  • Spark SDK
  • Spark API
  • RDD
  • Spark Lib
  • Hanna
  • How to migrate an existing Hadoop system to Spark

Day-2 Session -1.2: Storm -Real time processing in Big Data

  • Streams
  • Sprouts
  • Bolts
  • Topologies

Day-2: Session-2: Big Data Management System

  • Moving parts, compute nodes start/fail :ZooKeeper - For configuration/coordination/naming services
  • Complex pipeline/workflow: Oozie – manage workflow, dependencies, daisy chain
  • Deploy, configure, cluster management, upgrade etc (sys admin) :Ambari
  • In Cloud : Whirr
  • Evolving Big Data platform tools for tracking
  • ETL layer application issues

Day-2: Session-3: Predictive analytics in Business Intelligence -1: Fundamental Techniques & Machine learning based BI :

  • Introduction to Machine learning
  • Learning classification techniques
  • Bayesian Prediction-preparing training file
  • Markov random field
  • Supervised and unsupervised learning
  • Feature extraction
  • Support Vector Machine
  • Neural Network
  • Reinforcement learning
  • Big Data large variable problem -Random forest (RF)
  • Representation learning
  • Deep learning
  • Big Data Automation problem – Multi-model ensemble RF
  • Automation through Soft10-M
  • LDA and topic modeling
  • Agile learning
  • Agent based learning- Example from Telco operation
  • Distributed learning –Example from Telco operation
  • Introduction to Open source Tools for predictive analytics : R, Rapidminer, Mahut
  • More scalable Analytic-Apache Hama, Spark and CMU Graph lab

Day-2: Session-4 Predictive analytics eco-system-2: Common predictive analytic problems in Telecom

  • Insight analytic
  • Visualization analytic
  • Structured predictive analytic
  • Unstructured predictive analytic
  • Customer profiling
  • Recommendation Engine
  • Pattern detection
  • Rule/Scenario discovery –failure, fraud, optimization
  • Root cause discovery
  • Sentiment analysis
  • CRM analytic
  • Network analytic
  • Text Analytics
  • Technology assisted review
  • Fraud analytic
  • Real Time Analytic

Day-3 : Sesion-1 : Network Operation analytic- root cause analysis of network failures, service interruption from meta data, IPDR and CRM:

  • CPU Usage
  • Memory Usage
  • QoS Queue Usage
  • Device Temperature
  • Interface Error
  • IoS versions
  • Routing Events
  • Latency variations
  • Syslog analytics
  • Packet Loss
  • Load simulation
  • Topology inference
  • Performance Threshold
  • Device Traps
  • IPDR ( IP detailed record) collection and processing
  • Use of IPDR data for Subscriber Bandwidth consumption, Network interface utilization, modem status and diagnostic
  • HFC information

Day-3: Session-2: Tools for Network service failure analysis:

  • Network Summary Dashboard: monitor overall network deployments and track your organization's key performance indicators
  • Peak Period Analysis Dashboard: understand the application and subscriber trends driving peak utilization, with location-specific granularity
  • Routing Efficiency Dashboard: control network costs and build business cases for capital projects with a complete understanding of interconnect and transit relationships
  • Real-Time Entertainment Dashboard: access metrics that matter, including video views, duration, and video quality of experience (QoE)
  • IPv6 Transition Dashboard: investigate the ongoing adoption of IPv6 on your network and gain insight into the applications and devices driving trends
  • Case-Study-1: The Alcatel-Lucent Big Network Analytics (BNA) Data Miner
  • Multi-dimensional mobile intelligence (m.IQ6)

Day-3 : Session 3: Big Data BI for Marketing/Sales –Understanding sales/marketing from Sales data: ( All of them will be shown with a live predictive analytic demo )

  • To identify highest velocity clients
  • To identify clients for a given products
  • To identify right set of products for a client ( Recommendation Engine)
  • Market segmentation technique
  • Cross-Sale and upsale technique
  • Client segmentation technique
  • Sales revenue forecasting technique

Day-3: Session 4: BI needed for Telco CFO office:

  • Overview of Business Analytics works needed in a CFO office
  • Risk analysis on new investment
  • Revenue, profit forecasting
  • New client acquisition forecasting
  • Loss forecasting
  • Fraud analytic on finances ( details next session )

Day-4 : Session-1: Fraud prevention BI from Big Data in Telco-Fraud analytic:

  • Bandwidth leakage / Bandwidth fraud
  • Vendor fraud/over charging for projects
  • Customer refund/claims frauds
  • Travel reimbursement frauds

Day-4 : Session-2: From Churning Prediction to Churn Prevention:

  • 3 Types of Churn : Active/Deliberate , Rotational/Incidental, Passive Involuntary
  • 3 classification of churned customers: Total, Hidden, Partial
  • Understanding CRM variables for churn
  • Customer behavior data collection
  • Customer perception data collection
  • Customer demographics data collection
  • Cleaning CRM Data
  • Unstructured CRM data ( customer call, tickets, emails) and their conversion to structured data for Churn analysis
  • Social Media CRM-new way to extract customer satisfaction index
  • Case Study-1 : T-Mobile USA: Churn Reduction by 50%

Day-4 : Session-3: How to use predictive analysis for root cause analysis of customer dis-satisfaction :

  • Case Study -1 : Linking dissatisfaction to issues – Accounting, Engineering failures like service interruption, poor bandwidth service
  • Case Study-2: Big Data QA dashboard to track customer satisfaction index from various parameters such as call escalations, criticality of issues, pending service interruption events etc.

Day-4: Session-4: Big Data Dashboard for quick accessibility of diverse data and display :

  • Integration of existing application platform with Big Data Dashboard
  • Big Data management
  • Case Study of Big Data Dashboard: Tableau and Pentaho
  • Use Big Data app to push location based Advertisement
  • Tracking system and management

Day-5 : Session-1: How to justify Big Data BI implementation within an organization:

  • Defining ROI for Big Data implementation
  • Case studies for saving Analyst Time for collection and preparation of Data –increase in productivity gain
  • Case studies of revenue gain from customer churn
  • Revenue gain from location based and other targeted Ad
  • An integrated spreadsheet approach to calculate approx. expense vs. Revenue gain/savings from Big Data implementation.

Day-5 : Session-2: Step by Step procedure to replace legacy data system to Big Data System:

  • Understanding practical Big Data Migration Roadmap
  • What are the important information needed before architecting a Big Data implementation
  • What are the different ways of calculating volume, velocity, variety and veracity of data
  • How to estimate data growth
  • Case studies in 2 Telco

Day-5: Session 3 & 4: Review of Big Data Vendors and review of their products. Q/A session:

  • AccentureAlcatel-Lucent
  • Amazon –A9
  • APTEAN (Formerly CDC Software)
  • Cisco Systems
  • Cloudera
  • Dell
  • EMC
  • GoodData Corporation
  • Guavus
  • Hitachi Data Systems
  • Hortonworks
  • Huawei
  • HP
  • IBM
  • Informatica
  • Intel
  • Jaspersoft
  • Microsoft
  • MongoDB (Formerly 10Gen)
  • MU Sigma
  • Netapp
  • Opera Solutions
  • Oracle
  • Pentaho
  • Platfora
  • Qliktech
  • Quantum
  • Rackspace
  • Revolution Analytics
  • Salesforce
  • SAP
  • SAS Institute
  • Sisense
  • Software AG/Terracotta
  • Soft10 Automation
  • Splunk
  • Sqrrl
  • Supermicro
  • Tableau Software
  • Teradata
  • Think Big Analytics
  • Tidemark Systems
  • VMware (Part of EMC)

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