Wind Tre has established a Big Data & Analytics unit to drive the company's evolution toward becoming more data-driven by developing advanced data science use cases for revenue increase, cost optimization, and transforming decision making processes. The company is also collaborating with government agencies like ISTAT by providing mobile data to help with projects like population estimates and mobility analysis, and participating in data science challenges to enrich its internal data assets. Wind Tre's approach involves translating business objectives into machine learning and analytics use cases to generate value from big data.

1. July 4th 2018
Big Data as core engine to
support the Wind Tre data-
driven journey
Marcello Savarese
Chief Data Officer – Wind Tre

2. 2
Agenda
Big Data in Wind Tre
Q&A
Value generation through Big Data in Wind Tre
Active collaborations with ISTAT and Italian PA

3. 3
Let me introduce Wind Tre
§ Wind Tre, the first mobile Telecom Operator in Italy
32 million customers (both mobile and fixed lines)
§ Wind Tre plans to achieve significant efficiencies investing in digital
infrastructures
§ Wind Tre aims to become a key player in fixed-mobile convergence
and next-generation network development
§ Wind Tre is engaged in different innovative activities: Big Data

4. 4
Big Data & Analytics in Wind Tre
§ Wind Tre has established a Big Data & Analytics unit with the objective to drive the
evolution toward a data-driven company:
o Define and implement the data & analytics strategy
o Govern the data lifecycle from an end-to-end perspective
o Shape the evolution of the data application landscape
o Develop advanced data science use cases (ML, cognitive, AI) to achieve
revenue increase and cost optimization
§ Wind Tre has started the Data-Driven Company program, with the objective to
transform decision making processes across the entire organization, from an
experience-based approach to decisions supported from data and advanced data
science models.
§ All these activities will be carried out in compliance with current data
protection legislation

5. 5
Agenda
Big Data in Wind Tre
Q&A
Active collaborations with ISTAT and Italian PA
Value generation through Big Data in Wind Tre

6. 6
Collaborations between Wind Tre and ISTAT
• In 2014 WIND has provided mobile data (CDR) of different
provinces in Italy
• The first phase of analysis demonstrates that Mobile Phone
Data can be used successfully to perform researches and to
launch new integration activities of big data and
administrative sources
• Mobile phone data (MPD) can be used to validate population
estimates, as complementary data source, as primary
data source
• Possibility to define the OD Matrix and to assess the
frequencies of the mobility and other aspects as Event
Detection and Tourism Analysis

7. 7
European Projetcs – H2020
OpenReq
Solution for Requirement Engineering
Analyze explicit and implicit feedback (for example Social Network), integrate
stakeholders expertise and preferences, evaluate previous knowledge, project
history and code reusability to identify the requirements candidate for the
optimal release.
CloudDbAppliance
Hardware and software solution for Big Data
Operational Data Lake + Operational DB + Streaming Analytics + Fast Analytics
all in one appliance with the respect of High Availability and performances
Resource Allocation

8. 8
Exploit data by leveraging information with the Italian Government’s
Digital Team
Data & Analytics Framework (DAF) Data Hack Italia 2018
Leveraging on Government and Public
Administration open data to enrich Wind Tre
internal information assets and increase
knowledge on our customers
Online and onsite challenges, 400+ data
scientists participants to develop data science
model on anonymized and open data to address
a Wind Tre case

9. 9
Agenda
Big Data in Wind Tre
Q&A
Value generation through Big Data in Wind Tre
Active collaborations with ISTAT and Italian PA

10. 10
Wind Tre approach: understand strategic objectives and operational
Business pain points
DATA
DRIVEN
COMPANY
Finance &
Accounting
HR &
Organization
…
Channels
Increase
lead
conversion
Im prove
custom er
experience
Reduce
cost-to-
serve
Im prove
brand
reputation
Im prove
cam paigns &
prom otions
effectiveness
Increase
cross-sell
and up-
sell
Increase
share of
wallet
Reduce
churn
Reduce
distribution
costs
Marketing
& Sales
ü How to optim ize m arketing
program s and cam paigns
targeting to m axim ize M RO I?
ü How to target and plan
prom otions based on
custom ers behavior and
com petitors inform ation?
ü How to gain real-tim e insights
on cam paigns perform ance?
ü How to identify the next best
propositions for custom ers?
ü How to target prom otion based
on custom ers behavior?
ü How to apply dynam ic pricing
based on custom ers behavior?
ü How to collect insights on total
asset owned by the custom er
on different banks?
ü How to anticipate churn and
evaluate initiatives to
discourage it?
ü How to analyze custom ers behavior
to optim ize leads generation and
increase conversion rate?
ü How to optim ize and effectively size
the om nichannel distribution m odel?
ü How to analyze and optim ize
custom er service operations?
ü How to optim ize and effectively size
the om nichannel distribution m odel?
ü How to analyze and optim ize
custom er service operations?
ü How to analyze brand reputation
and benchm ark it against
com petitors?
ü How to forecast future talent and skill needs for the
organization – who’s needed, where they are needed,
when they are needed? How to optim ize workforce
perform ance?
ü How to identify and retain high perform ers?
ü How to optim ize the budgeting and forecasting process?
ü How to optim ize the accounts receivable and payable cycle?
ü How to optim ize m anagem ent of short term cash and m axim ize
returns from investm ents? How to identify frauds and com pliance
issues?

11. 11
Wind Tre approach: translate Business pain points in advanced statistic,
machine learning & cognitive computing use cases
Strategic Business imperatives Data science algorithms
Technology enablers
§ Churn
§ Up-sell
§ X-sell
§ Fixed-mobile convergence
§ Inactivity
§ Network quality
§ Gradient boosting
§ NLP § Clustering
§ Network analysis
§ Profiling
§ Correlation matrixes
§ Feature engineering
§ Call deflection
§ Python
§ Scikits § Cassandra
§ Mongo DB§ PySpark
§ Jupyther
§ Apache Beam
§ Tableau
§ Trifacta
§ Multi-variate analysis
§ Pandas

12. 12
Agenda
Big Data in Wind Tre
Value generation through Big Data in Wind Tre
Active collaborations with ISTAT and Italian PA
Q&A