big data challenge and opportunity michel birau
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Big Data challenge and opportunity – Orange testimony
Michel Birau Orange Global IS&T – Europe IS Transformation leader - michel.birau@orange.com
Agen
da
2 2
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
3
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
6
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
7
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
10
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)
11
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
14
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.
15
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
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