Cette présentation détaille un POC dans l'industrie des télécommunications qui met en lumière ce que l'analyse des big data peut apporter

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POC in COMMS IndustryPOC in COMMS Industry
Michel Bruley
May 2013

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Background and purpose of the missionBackground and purpose of the mission
WAN LAN
TR-104
TR-69
TR-135
Access
Network
(POP,
DSLAM)
Access
Network
(POP,
DSLAM)
Customer
TR-98
Customer Data WarehouseNetwork Data
Technical Data (TR files)
• Statistics box
• Internet + Wifi + VoIP + IPTV
Technical Data (TR files)
• Statistics box
• Internet + Wifi + VoIP + IPTV
Customer data warehouse
•Customer details (mobile / ISP)
•Mobile usage
•Marketing campaigns
•Contact Customer Service and
•Service
•Churn
•...
Customer data warehouse
•Customer details (mobile / ISP)
•Mobile usage
•Marketing campaigns
•Contact Customer Service and
•Service
•Churn
•...
ACS
• Improve the quality of service provided to the customer
• Link technical data network/box with those of the customer data warehouse

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3 types of teams are involved3 types of teams are involved
1 - Crisis cell for direct interventions to be performed on the ground following the detection of serious problems
Process/Marketing
Teams
Costs
Customer demands
Churn
Process/Marketing
Teams
Costs
Customer demands
Churn
Networks
Team
Monitoring equipment /
incidents
Networks
Team
Monitoring equipment /
incidents
Products
Team
Box creation
Products
Team
Box creation
Technical owner support /
« War Room1
» industrialization
Business owner support / Customer impacts
evaluation & prioritization
Technical
Owner
Business
Owner

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ApproachApproach
Log
BOX
Logs BOX
TR files
> 20 000 files
> 500 Go
> 5 months data
=> > 1 Billion lines
Teradata Aster
Platform
Data Integration
Data mining
Preparation
Exploratory analysis
Tableau Software
Visualization tool

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Four major stagesFour major stages
Data
TR File + CS tickets + DSLAM topology + Customers
Data
TR File + CS tickets + DSLAM topology + Customers
>1 billion >700 K > 10 K > 1.5 million
• The first two steps (80% of the time) were an investment that is now roughly an asset
• The following two steps (20%) have produced the first examples of TR data analysis

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Sources identification and acquisition
Loading data
• 20K files loaded
• Billion records
Data understanding
• Broadband Forum /
Specifications TR-069 and TR-
135)
• Products team
• Networks team
Audit data
• Data quality
• Data reliability
TR files understanding & explorationTR files understanding & exploration
Essential for the following step:
•Need to have access to transverse
experts
•Systematic approach
Essential for the following step:
•Need to have access to transverse
experts
•Systematic approach
Exploration
30%

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In theory
– Hundreds of indicators
– Cover three services: ADSL, IPTV
and VOIP
In practice
– Insufficient granularity (day)
– Partial, unreliable or missing information
We selected four concepts
Desynchronization: loss of internet connection (IAD) results for the client in a loss of access to all
services
Reboot: reboot the box, at the initiative of the client or on the initiative of the operator
Lost packets (IPTV): the loss of a single packet can result in damage to the image and sound
Rate: number of packets transmitted per second. We look in particular for the down flow under QoS
fixed ISP
We selected four concepts
Desynchronization: loss of internet connection (IAD) results for the client in a loss of access to all
services
Reboot: reboot the box, at the initiative of the client or on the initiative of the operator
Lost packets (IPTV): the loss of a single packet can result in damage to the image and sound
Rate: number of packets transmitted per second. We look in particular for the down flow under QoS
fixed ISP
Customer service qualityCustomer service quality
measuremeasure

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Vision QoS/QoE ISP land line and customerVision QoS/QoE ISP land line and customer
experience from logs BOX (TR)experience from logs BOX (TR)
Objective: To improve the service provided to customers

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DSL VoIP IPTV
Indicators Error rate
(FEC + CRC + HEC +
Erroredsec)
Cut calls rate IPLR = lost packets ratio
Variations in flow
Synchronisation loss
Land line consumption TV consumption TVReboot
Consumption
Axes Physical network x Product x DSLAM x Location x Temporal x Customer
Quality Good  Medium  Bad 
Calculated metrics summaryCalculated metrics summary

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No IPTVNo IPTV
IPTV SDIPTV SD
IPTV HDIPTV HD
Falling rates and customer impactFalling rates and customer impact
August 2012 Septembrer 2012 October 2012 November 2012
Analysis = monitoring current down flow
•Red curve = 1 customer suffers a sudden drop in the flow passing HD to SD, followed by stabilization in SD. Impact: TV
quality degradation
•Blue curve = 1 customer suffers a sudden drop in flow, passing from an excellent full HD quality to a total degradation.
Impact: out of service.
Learning's
•Easy and fast identification of customers with problems
•Ability to rapidly decline this type of analysis on other monitored data

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Box Reboot and Resynchro by DSLAMBox Reboot and Resynchro by DSLAM
Customers attached to a DSLAM
Huawei suffer more disconnections and loss of access to services
Internet, VoIP & IPTV
Question: connection to a DSLAM Alcatel-Lucent or Huawei has an impact on customer
perception of quality ISP?

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Errors CRC
ErroredSecs
Errors FEC
Desynchronization
Customer health state historyCustomer health state history
Analysis
•Various technical indicators reflecting the health and performance of services, followed during a
period, for a customer
Learning's
•Synthetic vision facilitates diagnosis and visualization of technical impact on a customer

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Others analysisOthers analysis
• ISP closed barometer: The proportion of closing for technical problems
undergoing resynchronization / reboots, increases significantly with the merger of
the closing date.
• Assess the stability of products: product tracking throughout the park,
any extension. Calculations acceleration and therefore the lessons which can be
exploited.
• Correlation analysis and QoS customers: Possibility to analyze
technical data and customer relationship simultaneously. Qualification impact on
customers. With Aster the axes of analysis are very easy to cross: products,
technical equipment, location, ...
• ...

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Learning’s
•TR data alone in the current state, contribute little to the predictive power of the
models
•The improvement of the churn existing model is not significant
•We must not abandon the TR data but "stabilize" and cross them with other data,
with a deeper history (> 1 year)
Predictive CS IPTV model
Predictive churn ISP model
Predictive AnalysisPredictive Analysis

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The POC give us a better understanding of TR data, they offer the possibility to:
Provide an additional analysis axis of the customer
Highlight the quality of service experienced by the customer
Offer the potential for improved models for CS and Marketing (churn, ...)
Facilitate discussions / negotiations between services (networks, products,
process & marketing) around common observations and data
Conclusions regarding TR dataConclusions regarding TR data