1. Driving Business Performance in
Telco with Hadoop
21 Use Cases with Lessons Learned at the
Intersection of People, Processes and Technology
Juergen Urbanski
Former Enterprise CTO, Deutsche Telekom
Board Member for Big Data & Analytics, BITKOM

2. Agenda
• The Journey to a Data Driven Organzation
• 21 Telco Use Cases for Hadoop
• Overall Lessons Learned
– 2 –

3. Business Value from Hadoop
Flight Plan for a Journey in Four Phases
* Timeline varies by company size. Often smaller or focused online businesses achieve milestones at the shorter end of the range.
1
2Evaluation –
Business
Value
Awareness
& Interest
Evaluation –
Technical
Enterprise
Deployment
Enterprise
Production
Industry
Leadership
Point
Deployment
Point
Production
3 4
Operational Value Strategic Value Data-Driven
Organization
Flight plan – typical elapsed time*
from start of phase 1 in months:
2-6 9-15 18-36
Potential Value
– 42 –

4. 1 2 3 4
What Would You Like to Accomplish?
Levels of Success with Hadoop
Potential Value Operational Value Strategic Value Data-Driven Organization
CXO • Recognition of
potential
• Mandate to explore
• Recognition of value realized
• Sponsorship to expand use
• Recognition of material value realized
• Sponsorship to transform organization
• Competitive advantage
• CDO part of Exec Team
Line of
Business
• Basic
understanding of
the value of
Hadoop to the
business
• Value realized in 1 area
‒ Customer intimacy
‒ Operational excellence
‒ Risk, security,
compliance
‒ New business
• Value realized and tracked in many
areas
‒ Customer intimacy
‒ Operational excellence
‒ Risk, security, compliance
‒ New business
• Data managed like capital
• Intelligence at the front
line
• JIT decision making
• Widespread value
creation
Analytics &
Applications
• Basic
understanding how
Hadoop fits into
existing landscape
• BI and EDW access to
Hadoop
• Some new analytic apps,
often batch
• Few use cases and
processing engines
• Many sources and time
periods
• Mostly departmental silos
• 10-50 enterprise users
• Hadoop consumable by any
department, both technically and
process-wise
• New apps natively on Hadoop, often
transactional or real-time
• Many use cases and processing
engines
• Multiple lenses into common data pool
• Emerging data science team
• 50-500 enterprise users
• Data-driven culture
• High-performing data
science team
• Use cases build on each
other
• 500-5000 enterprise
users
Data Mgt. &
Security
• Basic
understanding how
Hadoop fits
• Benefitting from schema on
read
• Professionalizing data
definitions and models
• Collaboration and granular security
controls governing use of shared data
• Incentives and process to
encourage consumption
of shared data
Infra-
structure
• Basic fluency with
core technical
concepts of
Hadoop
• 1 or more production
environments
• Multi-tenant shared service worldwide
• Data Lake
• Service Desk / CoE
• Hadoop community
participation and
contribution
– 43 –

5. Early Stage Center of Excellence
Mastering the Hadoop Journey 2
– 49–
Roles
Analytic
Application
Development
(LOB)
Infrastructure
Operations
Change &
Program
Management
ArchitectureHead of Big
Data
Business
Analysts
(usually in
LOB)

6. Roles
Later Stage Center of Excellence
Mastering the Hadoop Journey
Legend
Red = New at this stage
– 50 –
3
Analytic
Application
Development
(LOB)
Infrastructure
Administration
End User
Service Desk
Change &
Program
Management
Advocacy &
Demand
Management
Data Science &
Machine
Learning
OperationsBusiness
Team
Architecture &
Service
Portfolio
Management
SVP Big Data /
Chief Data
Officer
Group CIO or
EVP
Business
Analysts
(usually in
LOB)

7. A Data Lake Establishes Hadoop as a Shared Service
Mastering the Hadoop Journey
Data Lake Characteristics
• Timely insights for all authorized
users / tenants
• Many use cases, often building
on each other
• The right processing engine for
the right job
• All data, across all time periods
• Multiple lenses on the same data
3
– 45 –
User
Use
case
Processing
engine
Data

8. Stakeholder Expectations of a Shared Service
Mastering the Hadoop Journey
• Multi-tenancy, workload fencing, resource isolation
• Data security, governance and workflows
• Data privacy, policy and regulatory compliance
• Consumption models (e.g., self-service, charge-back,
on-boarding)
• Data publication guidelines (availability, stability, quality)
• Operational processes, standards, service tiers with
SLAs
Stage 3 – Enterprise Data LakeDepartmental Project Silos
• Security largely via restricting physical
access to a few friendly users
• Best efforts service for batch use cases
• Data largely owned by each
department
3
– 46 –
User
Use
case
Processing
engine
Data

9. Hadoop Can Create Competitive Advantage
Mastering the Hadoop Journey
2-3
Data Science & Machine Learning*
Wave C
Wave B
Wave A
Use
case
New Front-line Applications, often Real-Time
Integration with Front-line Applications, often Real Time
Data Mining – New Analytic Applications
Data Refinery – Integration with Analytic Applications
Active Archive and Data Offload
Creates Operational Value
Drives Competitive Advantage
* A practice area that is relevant to many Hadoop workloads.
– 44 –

10. Agenda
• The Journey to a Data Driven Organzation
• 21 Telco Use Cases for Hadoop
• Overall Lessons Learned
– 2 –

11.  Network capacity planning
 Network upgrades
 Network maintenance
 Network performance management
 Network traffic shaping
21 Telco Use Cases for Hadoop
– 11 –
Use Case
Network Infrastructure
Function
 Customer experience analytics
 Contact center productivity
 Field service productivity
 Data protection and compliance
 End-user device security
Service and Security
 360-degree view of customer value
 Personalized marketing campaigns
 Upselling and cross-selling
 Next-product-to-buy (NPTB)
 Churn reduction
Sales and Marketing
 New product development
 Actionable intelligence serving:
 Advertisers
 Merchants/retailers
 Payment processors
 Federal governments
 Local governments
New and Adjacent Business
Network
Care
Sales
New Biz

12. Network Infrastructure –
Network Capacity Planning
– 12 –
Business Problem
 The consumption of services and
resulting bandwidth in a particular
neighborhood may be out of sync with a
telco’s plans to build new towers or
transmission lines in that same
neighborhood.
 This leads to a mismatch between
expensive infrastructure investments
and the actual revenue from those
investments.
 Examples:
 4G (LTE)
 FTTC (fiber to the curb)
 FTTH (fiber to the home)
 One European carrier used Hadoop to
optimize the rollout of 4G coverage in
time and space to match the likely pick-
up in service revenue, based on detailed
cell tower traffic data of the last few
years.
 With their prior, less informed approach,
they would have had to spend 10%
more capex for the same outcome.
Value Realized
Network
Care
Sales
New Biz

13. Hadoop in Network Infrastructure –
Network Upgrades Improve the Customer
Experience
– 13 –
• Correlate
network
congestion and
customer
experience
• 11 different data
sources
• Millions of
subscriber
records, work
orders, calls,
IPDRs, Tivoli
NPMs
• Finding: Only a
few nodes
responsible for
most of the
negative
customer
experience
Network
Node
TNMP
CMTS
Performance
Network
Sensors
IPDR
Cable
Modem
Usage
Competitive
Spend
Data
HouseholdHousehold
Master
Subscriber
Record
Marketing
Demo-
graphics
Caller
Experience
Work Orders
Mobile
Devices
Customer
Premise
Equipment
Online
Transactions
Social Media
Interactions
SOURCE DATA
Network
Care
Sales
New Biz

14. Service and Security –
Customer Experience Analytics Based on
Call Detail Records (CDRs)
– 14 –
Business Problem
 A typical mobile service provider
generates >1 billion CDRs per day,
ingesting millions of CDRs per second.
 System holds >100 billion records, half
a petabyte added every month!
 Due to the cost of existing solutions, the
data expires after 60 days
 CDRs need to be analyzed and archived
for compliance, billing and congestion
monitoring.
 Example: forensics on dropped calls
and poor sound quality.
 High volume makes pattern recognition
and root cause analysis difficult.
 Often those need to happen in real-time,
with a customer waiting for answers.
 With Hadoop the carrier can to retain
some data for up to three years
 Hadoop provides both a cost advantage
– Hadoop provides storage 20x cheaper
than enterprise-grade storage – and
better insights.
 Better analysis to continuously improve
call quality, customer satisfaction and
servicing margins.
Value Realized
Network
Care
Sales
New Biz

15. Service and Security –
Contact Center Productivity
– 15 –
Business Problem
 A US-based mobile provider struggled
with a combination of high costs but low
customer satisfaction related to
customer care.
 An increasing share of support cases
are related to mobile data usage and
associated charges.
 Traditionally, contact center agents did
not have granular insights into a
particular customer’s data usage, hence
were unable to provide effective call
resolution.
 With Hadoop, one operator detected
that 25% of callers were contacting the
call center merely to have their late fees
on the monthly bill waived.
 The provider was able to off-load these
cases to online self-service and
interactive voice recognition.
 Frees up the agents to focus on more
valuable customer interactions.
 The provider is now extending this
solution to focus on issue resolution.
Value Realized
Network
Care
Sales
New Biz

16. Service and Security –
Field Service Productivity
– 16 –
Business Problem
 A provider’s contact center agents had
insufficient ways of diagnosing what was
wrong with customers, leading to many
unnecessary truck rolls.
 In particular, the agents were not able to
triage network vs. home-based
problems accurately enough.
 Therefore, technicians were dispatched
to the customer premises for problems
that reside within the network.
 The provider was able to avoid a large
number of “false positive” truck rolls.
 With each truck roll costing about $150
fully loaded, the provider was able to
save several million dollars already in
the first year.
Value Realized
Network
Care
Sales
New Biz

17. Sales and Marketing –
360 Degree View of Customer Value
– 17 –
Business Problem
 Telcos and cable companies interact
with customers across many channels
and points in time.
 Data about those interactions is stored
in silos.
 Difficult to correlate data about
customer purchases, marketing
campaign results, and online browsing
behavior.
 Problem is exacerbated by recent
acquisitions and a proliferation in the
volume and type of customer data.
 Merging that data in a relational
database structure is slow, expensive
and technically difficult.
 Enterprise-wide data lake of several
petabytes
 360-degree unified view of the customer
(or household) life time value based on
usages patterns across time, products
and channels.
Value Realized
Network
Care
Sales
New Biz

18. Sales and Marketing –
Personalized Marketing Campaigns
– 18 –
Business Problem
 Mobile phones not only follow their
owners everywhere, but also reveal a lot
about their owners’ interests through
browsing behavior and the applications
present on the phone.
 Telcos are looking for ways to mine that
information.
 Provider risked losing substantial
revenue as prepaid customers were
starting to switch to a competitor as a
result of a particularly effective
marketing campaign.
 Pinpoint those individual customers
most at risk of churning, and then built a
highly targeted campaign to retain the
remaining customers in that segment.
 A churn alarm system was established
and revenue leakage was minimized.
 Telesales revenue increase by 50% by
tracking competitors web-sites visited
and counter offers to products searched
 +20% conversion rate increase by
optimizing and personalizing the path-
to-transaction
 $1.65 ARPU increase for 1 million
customers boosts topline by $20 million
per year.
Value Realized
Network
Care
Sales
New Biz

19. Sales and Marketing –
Up-selling and Cross-selling
– 19 –
Business Problem
 The provider needed to find an
approach to upsell smart phones into a
user base that was still largely on legacy
feature phones.
 The operator converted many hundred
thousand feature phone users to smart
phones with associated data plans.
Value Realized
Network
Care
Sales
New Biz

20. Sales and Marketing –
Next Product to Buy (NPTB)
– 20 –
Business Problem
 As telco product portfolios grow more
complex, there are ever more
opportunities to sell additional services
to the same customer base.
 Many sales reps however are
overwhelmed with that complexity and
struggle to translate the breadth of the
product portfolio into incremental sales.
 Confident NPTB recommendations,
based on data from all its customers,
empower sales associates and improve
their interactions with customers pre-
transaction.
Value Realized
Network
Care
Sales
New Biz

21. Sales and Marketing –
Churn Reduction
– 21 –
Business Problem
 A North American provider faced the
following challenge: 50% of new
customers churned off within 6 months
of acquisition.
 The average customer life time in this
segment was 13 months, well short of
the 18 months needed to break even.
 The provider increased the “right”
customer acquisitions by 27% and
decreased subsequent churn in this
segment by 50%.
 Price related churn down by 40%
 Reducing cable subscriber churn (“cord
cutting”). Every 100,000 subscribers
equates to customer lifetime value of
$1 billion
Value Realized
Network
Care
Sales
New Biz

22. New and Adjacent Businesses –
Actionable Intelligence Serving Advertisers
– 22 –
Business Problem
 Europe’s leading real estate
marketplace Scout24 – a subsidiary of
Deutsche Telekom – features more than
one million properties for rent or sale at
any given time, and has facilitated more
than 20 million property transactions
over the last few years.
 The company wanted to drive more
market share to Scout24 by offering
advertisers – typically real estate agents
and brokers – an even better service.
 A small team consisting of a product
manager, a data scientist and a few
developers was able to make a
meaningful contribution to revenue
growth.
Value Realized
Network
Care
Sales
New Biz

23. Big Data as a Product:
ImmobilienScout (Deutsche Telekom)
– 23 –
Network
Care
Sales
New Biz

24. New and Adjacent Businesses –
Actionable Intelligence Serving
Payment Processors
– 24 –
Business Problem
 Credit card issuers experience
increasing fraud when their card
members are travelling abroad.
 95% of travelers opted into the SMS
alerting service, resulting in a
substantial decrease in fraud related to
card use in foreign countries.
Value Realized
Network
Care
Sales
New Biz

25. New and Adjacent Businesses –
Actionable Intelligence Serving
Federal Governments
– 25 –
Business Problem
 The Eastward expansion of the
European Union has resulted in a longer
and more porous border to non-EU
member states.
 This has made it more difficult to protect
the EU against a stream of illegal goods
and refugees, which often travel over
land from the EU’s Eastern and South-
Eastern neighbors.
 Law enforcement agencies are able to
target their scarce resources much
more effectively, for instance choosing
to intercept suspicious cars traveling in
certain directions at speeds above
130km/h.
 This radically increases their hit rate per
mission.
Value Realized
Network
Care
Sales
New Biz

26. Agenda
• The Journey to a Data Driven Organzation
• 21 Telco Use Cases for Hadoop
• Overall Lessons Learned
– 2 –

27. Lessons Learned – Technology Disciplines
– 48 –
Security. XA Secure is a big step forward. But internal security sign-offs are
complicated. New possibilities opened up by the data lake imply a steep learning
curve.
Data quality. Early use case was volume of structured data. Gets harder now
with unstructured data use cases.
Metadata management needs to be unified across BI and Hadoop.
Coordination between data publishers and consumers on our internal social
network breaks down barriers.

28. Lessons Learned – Business Disciplines
Sponsorship. Even our CEO is aware that Hadoop has a role to play in the
transformation of our business.
Ownership of projects always sits with business people. Success is measured.
On-boarding 10 new use cases per year.
Governance is difficult in a federated organization. Hence the CEO pushes for
success in a few areas, and the rest of the organization can opt in.
Funding and incentives. We pay the internal data producers, there is no free data. If
you consume our data, you have to share all of your data, no cherry picking.
Application development not that different. Important to apply usual coding
best practices here as well.
Cloud is intriguing for rapid prototyping, side-stepping procurement or where sources
are in the cloud. However, different clouds are not fully interoperable, resulting in
some lock-in.
Power plays. Data has power. A data lake brings lots of power. Beware of
internal politics over who should own Hadoop.
– 47 –

29. Driving Business Performance in
Telco with Hadoop
21 Use Cases with Lessons Learned at the
Intersection of People, Processes and Technology
Questions?
juergen@techalpha.com
@juergenurbanski
LinkedIn: juergenurbanski