Big Data Business Intelligence for Telecom and Communication Service Providersのトレーニングコース

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)