Big Data challenge and opportunity – Orange testimony

Michel Birau Orange Global IS&T – Europe IS Transformation leader - michel.birau@orange.com

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Agenda

1. Big Data in the context of Orange

2. Potential, benefits and opportunities

3. IT Challenge: sources, architecture & tools

4. Points of vigilance

5. Summary – key takeaways

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Orange at a glance

Big Data

challenge

B2B

B2C

In 35 countries for the B2C market

170 000 employees

227 M customers

169 M mobile customers

15 M DSL or FTTx customers

Forecasted

Traffic Growth

4

Big data: volume, variety, velocity in the Telco context

volume explosion of data volumes

to process

with internet, the social

networks and the M2M, the

increase becomes exponential

variety

Heterogeneous structures

Our analysis deal more and

more with non or semi

structured data. Classical

architectures not adapted

anymore.

The data must be

collected, stored

AND consumed/treated

through real time

processes

Ex: details of communications,

volume of events pushed by the

network, web analysis

velocity Real Time needs:

the Fast Data

Ex : web servers logs, character

service platform logs, device

agents

Ex: Detection: Customer

Churn, QoS drop in real time

5

Big Data in its lifecycle

Entering the trough of disillusionment ?

the Big Data adoption Hype curve A growing torrent…

Source: Mc Kinsey (2011)

Source: Gartner (2012)

Real term work ahead of us will require cautious investments

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Potential, benefits and opportunities

Big Data: for which Telco objectives?

Source: Yankee Group report on BI

(2010)

Network

Management

Sales &

Marketing

BI – Big Data is used for various transverse business purposes,

including Network Management and Sales & Mkg

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Source: big data Analytics by

Philip RUSSOM (2011)

The Data value: from Small data ‘Mass Marketing’ to Big Data

‘Responsible Marketing’ thanks to analytics

Potential, benefits and opportunities

Taking advantage of the data value

Better market knowledge Better user experience

Network optimization E2E QoS / QoE analysis

Real time action New offerings Smarter interactions

8

Potential, benefits and opportunities - Analytics

Taking advantage of the data value requires

improving maturity of analytics

9

Big Data as a technological opportunity – targeting hybrid technology for DWH

Offloading DWH for existing services based on data usage

focus on current ETL process and storage for structured

(CDRs, XDRs) and semi-structured data (network probes

data)

Main objective: reduce architecture spending

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IT challenge

The growing big data torrent creates limitations

In spite of the fantastic

improvement of the Hardware…

Big data torrent puts a high

pressure on Design to cost

Current systems are Purpose

built: they store their own data.. Larger analytic data sets are

required

Volume,

Transactions /s,

Performance

€

“Companies in all sectors have at least 100 terabytes of stored data ; many have

more than 1 petabyte” Mc Kinsey (2011)

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IT challenge: Telco usage of the “dark data”

CDRs reliable and detailed: Caller & callee numbers, call duration,

geo-location, data volume, option details, VoD consumption,

Platform logs rich lower reliability: both service and network PFs

keep traces that are partially exploited today: Portal, mail, IM,

VOIP, Video, Social Network, Geolocalisation

Device agents as a complementary source of data, focused on

user service QoS: Liveboxes, STBs, Mobile devices Dark

and u

nstruc

tured

Data

Data & events from the

network, service

platforms, probes, www,

document and external

world

12

IT challenge: crossing these data sources with back-end

systems to improve analytics

Data

Sources

Enterprise data

Third Party

Data

Social Network

Data

Unstructured

External data

Structured Unstructured

Customer

Platforms data

Offer &

products

data

xDR &

IP data

Network

Probes

data

Semi-structured

Device &

Logs

data

Documents,

Cookies Website

data

Growth of semi-structured and unstructured data is rising faster than structured.

New architecture and tools framework is required

13

IT challenge - Architecture & tools A framework combining OSS and BI: the Decision Support System

Data sources

repositories Operational IT Data

Billing / ERP / CRM / Order Mgt

Application

layer Reporting & Dashboard

Real-time

analysis

Aggregation Layer

Data

Storage

layer DataWareHouse

DataMarts DataMarts

DataMarts

ODS

Data

Integration

layer Continuous Flow

/ CDC

Decis

ion

Su

pp

ort S

yste

m (D

SS

)

Very Large

Data Store

Predictive

analysis

CEP ETL ETLT

Operational Network Data SCA / Service Platform / Sessions / Probes logs

Events Web Portal, Transactions, Visits

Aggregation Layer

Ad-Hoc

analysis

Descriptive

analysis

DataMart

s DataMarts

(Cube)

marketing service

management technical

management

customer

service

Financial

analyst

sales

management

API API API

API API API

Adaptor Adaptor Adaptor

Staging

Area File

System

Customer,

Supplier &

Partner

Log collector

DataLab

Advanced

Datalab NoSQL

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Points of vigilance

Federation of the initiatives around the Big Data phenomenon

which has rapidly grown

infrastructures not adapted: the machines are not ready and

works are on-going to catch-up

The changes driven by the Big Data will require the right skills on

these technologies

Finding out the adapted business use cases: business processes

appear not mature enough to consume these new sources of

information

Data have different legal definition across geography. Exploitation of network data requires a strong attention from legal department. Risks to be assessed on privacy (CNIL in France) and personal data: browsing data, private correspondence (call, mails, sms), storage duration.

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Summary – key takeaways

Cross-analysis data from the network and

back-end systems (BSS, OSS) to understand

and predict customer demand.

it includes predictive modeling, real time

network analysis

An understanding of the customer is

paramount to lower churn and improve

customer experience.

it includes e2e QoS, QoE, 360 degree

view of the customer, next best actions.

IT & Network infrastructure deliver vast

amounts of diverse data, but it is not fully

exploited

It includes managing logs coming from

Service Platforms, IT servers to improve

operational efficiency and security (SIEM)

Big data and Fast data technologies are

driving IT transformation to reduce and

optimize IT costs providing additional

features to improve analytics

it includes optimizing TCO, costs

reduction, BI tools selection and

adapting operation

1. Analytics improvement 2. Customer Experience Management

3. Logs management 4. IT Transformation

4 Main challenges