1.
Big Data
Overview of a Big Data eco system and its use in the
telecommunication industry
Bearing Point .

2.
Big Data revolutionizes the way companies do business allowing to develop new
strategies as well as optimize processes
Big Data affects all organizations from different industries
- Over 90% of all data in the world was created in the past 2 years
- The amount of data transferred over mobile networks increased
by 81% to 1.5 exabytes (1.5 billion gigabytes per month between
2012 and 2014)
- 1.9 million IT jobs will be created in the US by 2015 to carry out
big data projects
40
30
20
10
2009
‘Source: HP Vertica
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2011
I Zettabytes
Big Data growthl
2015
2020
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an ergaard, Gartner Research
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BearingPoint.

3.
Exploiting big data for business usage requires companies to define their use
cases and navigate through the value chain to select the right setup and partners
Big Data Value Chain Overview
1. 2. 3. . 5.
Data Data Storing Data Data
Generation and Accessing Preparation Usage
Content IAn enormous variety Retrieved data is ITo make big data ready IAna| ytics allows ITrans| ating conclusions
{of data sources have to I stored and made Ifor analytics, available I examination of raw I into implementable
be made accessible to accessible while data has to be data with the purpose business actions and
Eexploit analytical value Iassuring data integrity consolidated, normed Ito draw conclusions measures is key to
Iof big data I and standardized. This I either by deep dive I materialize big data
‘ I I data preparation is a I analytics or I
I pre-requisite for data Ivisualization of data
_ Data types I I - Transformation I I - Decision support
Data origins ° Access ° Loading 3- Predict 3- Just in time business
Characteristics
5° Data velocity I g° Consolidation - Report modeling
Main players . Teradata MapR Iflypefcubeo NetBeans
(exemplary) Oracle SPSS '“"' “""
3- MySQL 1° APEX - Oracle 3
- Sybase I - Hadoop I - SAP I
i I- Amazon Web I - IBM I
Services I - Autonomy (HP)
lSI2"5‘B"§‘ I I‘ 3". °“l’~“‘ I
- - lnfochimps l - Daataari/ iI: tch I
I- Google BigQuery I- Microsoft
Source: BearingPoint analysis
3 BearingPoint.

4.
MNCs are adopting Big Data generated knowledge along different areas of
intervention both for internal and external purposes
Most of the revenues potential lays within the monetary value of raw data and insights
Areas of Intervention
Customer centricity and customer
experience management
/
“ New Business Model
Risk and Financial services
0 Operational Optimization
Targeting product and
marketing offers
Sentiment Analysis
Churn Prevention
Enhancing the multichannel
consumer experience
Predictive Policing
Fraud Management and
prevention
Revenue assurance
Market fluctuation forecasting
Network Optimization
Proactive issue resolution
Real time FAQ broadcasting
IT operational efficiency
Internal Use
’IDATE A Big Data for Telcos: How Big Data can get new revenues and reduce costs_DecA2D13 4
Customer micro segmentation
Sentiment Analysis
Location based Marketing
Geo targeted advertising
Revenue sharing with digital
retailers
Health care monitoring
Safety tracking of Family
members
Marketing Insights
Extemal use
Focus on Monetization
‘r Mobile revenue from selling mobile-
user behaviour data may reach fl
billion USD in 2016* including both
insights and raw data sales
‘r The competition on this segment is
low:
1) no OTTs offering such concept
2) mobile data: CDRS, location &
demographics are unique to telcos
‘r There are not many initiative around
the sales of data so far
Data Usage
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5.
Big Data : monetization to share the benefits of data
ownership
Use case : Morrissons used Telefonica Dynamic Insights solution to improve its customers loyalty
Morrissons wishes to improve the
loyalty of its customers without any
heavy investment
Dynamic Insight precisely analyze
the footfall
1 Data anonymization
A huge potential : in UK, households
1 go shopping to the supermarkets on 2
an average of around 5 times a week
Aggregation: The hashed values
are aggregated into groups
Extrapolation: The sample is
taken and extrapolated to
population totals
2 But many rotations between 2 or 3 3
supermarket chains
Morrisons stake is to better define customer targeting and
3 households that should be targeted 4 comparisons with the
f0" C°UP0n5 Sending performance of challengers)
The main source of Smart Steps data comes from the information about 02
sims users
The data from 02 network is anonymized,
but the main characteristics of the people
having a phone are kept (gender, age)
. _ __ E
E; ., " This information is enriched with Morrissons
. . . ,
" ' / own data
V t
—_. :
Source : BearingPoint analysis 5
Thanks to 02 monetized data and
solution, Morrissons was able to
Benefit from as much customers data as
with 10 years of conventional loyalty
programs
+
Better define the good areas to targets
and those which should be avoided
+
Optimize the sending of coupons to
households which were not already
regular customers
+
Limit the expenses on costly loyalty
programs
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BearingPoint.

6.
Big Data : improving existing products by understanding
the work environment
Use case : Vestas Wind Systems and wind turbines optimization
Vestas wished to make the most of The method aimed to determine the
its already developed products by weather at any point and time
Pinpointing of the best spots to GI b Ifl d I.
1 deploy wind turbines 1 O a OW mo 9 mg
2 Maximizing the power generated 2 Mesoscaie modeiing
3 Reducing energy costs 3 Wind library generation
. . Determining the wind
4 Reducing the downtime 4 characteristics
The powerful software and hardware used by Vestas allowed it to efficiently
deal with a always growing set of parameters (around 200 today)
Data from 27,000 of the
wind turbines and the
history of the company
35,000 meteorological
stations around the
world
Z
Coming soon : g|oba|
deforestation metrics,
satellite images,
Historical data
Source : BearingPoint analysis 6
Vestas enhanced its wind
turbines abilities
Wind forecasting response time
reduced by approximately 97%
+
Development time for a wind turbine
site reduced by nearly a month
+
Enhanced ability to deal with huge
amounts of data
+
Cost per kilowatt hour lowered for the
customers (ROI increased)
Optimization of the wind
forecasting response time and of
the Clients ROI
What could be reused by telecoms ?
I Optimization of the antennae network
thanks to the use of:
I data from the existing antennae
I users data, including the strength of
the signal at each time and location
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7.
Companies benefit according to the revolutionary development of Big Data
Telecommunication industries can be inspired by other industry sectors already using Big Data
Benefits for telecommunication operators Opportunities for telecommunication industries
already using Big Data based on benefits of other industries
. . A.
' Increasing the companies’ turnover - Big increase of customer data | ‘
' Customer loyalty is forced up ° Optimize offers
° Opportunity for additional value ' Reducing editing time .23
° Reducing communications fraud J ' Drive operational intelligence, agility and
cost effectiveness _'- < I
- Understanding of customer requirements and as ’
adaptation of products (I ' Discovering & removing superfluous 5
functions in products ‘
Telecommunication operators are the natural pioneers and leaders of the Big Data trend
There is still room for improvement
They can greatly benefit from crossing their technology with the more diverse and innovative strategies ofthe newcomers.
7 BearingPoint.

8.
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9.
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