This document discusses strategies for telcos to leverage big data. It begins by outlining the shift from traditional "small data" paradigms to new "big data" approaches characterized by processing vast amounts of data from both internal and external sources. It then provides examples of the types of internal and external data available to telcos and how this data could be used. The document also outlines some of the main challenges telcos may face in developing big data capabilities, such as competition from internet companies and ensuring privacy and regulatory compliance. It concludes by emphasizing the importance of a gradual, pilot-based approach to building big data strategies.

1. Strategyzing Big Data in Telco
Challenges and Opportunities
Parviz Iskhakov, 2015

2. Variety
Old Paradigm – Small Data New Paradigm – Big Data
- Limited volumes processed
- Terabytes
- Hardware defined processing
- Full available Data Set processed
- Petabytes
- Data can be processed in cloud and
mostly software defined
- Analytics for basic reporting,
segmentation, network planning
- Limited data sources used
- Unstructured data is mostly unused
- Analytics used widely for prediction
and recommendation
- All available sources used
- Unstructured data processing is
hugely utilized for data refinement
- Limited speed of processing
- Hours and Days
- Waterfall PM, slower time2market
- Unlimited speed of data
- Seconds and Hours
- Agile PM, Fail Fast approach
- Poor range of formats being
processed
- Difficult to check the quality
- Poor data protection that can hurt
quality
- Any format of data
- Data quality cross check
- Full-scale depersonalization and
ultimate protection
WHAT IS BIG DATA FOR TELCO? SHIFT FROM OLD PARADIGM TO A NEW ONE
Volume
Velocity
BIG DATA IS A POOL
OF ACTIVITIES
intended at
processing the
data a company
owns (internal and
external)
so that to open new
revenue
opportunities,
minimize costs
and enhance UX.
Veracity

3. Data Source Source Brief
Current Value
Extraction
Difficulty of
Extraction
Difficulty of
Processing
Potential Value
Billing logs
Call details, Traffic, Revenues, Balance, Debt, Services
used, ARPU, MOU, Age, Gender, Roaming 3
1 1 4
Radio Network, Call Tracing
Systems
Point of Interest, Location Analysis, Real Time
Tracking, Frequency of visits 2
2 3 5
SMS data
Sender's numbers (including B2B senders), Semantic
and Sentiment analysis, 1
1 1 3
Device Management Systems History of devices, Functionality, Cost, Brand 2
1 1 3
DPI, Gn/Gi/S1
Type of data traffic, Applications, OTT usage, Pages
visited, Search quiries, Apps installed/used, Page 2
3 4 5
Call Centre Infrastructure
Call Center Logs, Call Center Speech, Complaints,
Requests, Profiles Refinement 1
3 3 3
Network
Network logs, Signalling data, Network faults data /
Incidents 3
3 3 3
ERP
Orders, Procurement, Corporate Documents and
interaction 1
1 1 1
IOT infrastructure NFC data, M2M data, Sensor data 1
2 4 4
CRM
Complaints, Profiling details, Location data,
Requests, Client emails 3
2 4 5
Web Infrastructure IP adresses, Transactions, Basket Analysis 2
2 3 4
Other Internal TV, Media, Fixed lines, Financial Dat (Hyperion) 2
3 3 3
Social networks (FB, VK, Twitter
and alike)
SNA, Alpha leaders, Hubs, Sentiments and tones,
Engagement, Rich Customer Profiling 0
4 5 5
Mobile Applications Usage, Preferences, Profiling 0
4 5 5
CSP Exchange Data exchange with other operators 0
1 1 4
Financial and Insurance
Institutions Score exchanges, fraudulent customers 1
1 1 4
Retail Cheque, Preferences, Location, CRM 0
1 1 3
Web Crawling Sentiment, Interest Profiling 0
4 4 4
Government
Transportation, Weather Forecast, Real Estate,
Urban Statistics 1
2 2 3
Research Companies Behaviour analysis etc. 0
3 3 4
Other Third Party Data Other data 0
3 3 3
WHAT DATA CAN TELCO RELY ON?
Internal Data
The data generated from
all internal sources starting
from traditional billing and
core network and finishing
with logs generated from
web sites and various
applications
External Data
The data generated from
unusual external sources

4. WHAT TELCO MIGHT NEED THIS DATA FOR?
Cost
Optimization
New
Revenue
Streams
InternalmonetizationExternalmonetization
Enhancing
UX
- Network planning
- Supply chain
- Channel Performance
- Sales performance
- Revenue assurance
- Churn prevention
- Retail optimization
- Improving cross/up-sale
- Product and service design
- Fraud Prevention (Banking and other)
- Marketing
- Customer complaint prevention
- Smart city services
- Retail planning
- Digital advertising
- Insurance and finance scoring
- Marketing research
- Utilities
- Healthcare
- Data Brokerage
- Data hub
- Recommendation engines support
- Converged B2B services
64%
22%
14%
Share of Opportunity in 2019
Internal
Monetization
Big Data as a
Service
Big Data Driven
Biz Models
Euro 359mn 2015
2019
15-19
Euro 1,526mn
Euro 4,380mn
Detecon estimations for Europe
- Less than 40% of Big Data initiatives expected to result in new revenue streams
- The most promising revenue-generating initiatives are in City Planning,
Healthcare and Advertising
Based on
Gartner
evaluations
Future
Cash
Cows

5. • It is going to be a long way for telcos to reach
maturity in big data processing and value
extraction
• Internet peers however are already at the
top level of maturity which may result in
fierce competition and dramatic devaluation
of data telcos currently dispose
Big Data
metamorphosis
Small Data
Paradigm
• Reformatted project and process management
• Full-scale recommendation and prediction engines
• Fully anonimyzed, inventoried and protected data
• Large number of products including internal fraud
and risk prevention. New digital revenue streams
• Large number of partners from Internet community
• Formulated Big Data strategies and
implementation
• Advanced Cross-sell/Upsell
• Mature churn prediction and
prevention process
• Large range of white labeled digital
and IoT services
• Small Data Paradigm
• Slow decision making process
• Lack of digitalization of the
business
• Small penetration of convergent
products
• New revenue streams reaching 10-
30% of total revenues
• Smart and Soft Pipe
• Significant M&A activity in the
Internet domain
• NewGen services and products
2015 2016 2017 2018 2019
BIG DATA TRANSFORMATION STAGES AND OUTCOMES
Vodafone
Telefonica
Telstra
SKT
SingTel
Orange
DT
DOCOMO
AT&T
Telcos Big Data Activities
Most of the
telcos
are here
Gartner There are a lot of activities in Big
Data domain however revenue
implication of these activities is still
low or unreported

6. • Identification of clear priorities and a development
plan for internal products
• Participation in the formation of a product market
with external monetization
• Development of mechanisms for the purchase and
use of external data
Demand and USP
creation
Building new paradigm
Infrastructures
Competencies
Processes, project
management
• Creation of holistic Big Data IT infrastructure
• Implementation of agile development principles for
Big Data infrastructure, on-demand development
processes
• Spin-off Big Data Initiatives
• Agile project management
Legal Risk
Management
Nurture and cultivate new competencies:
• Data Science
• Data Governance
• Product Management
• System architecture
• DevOps
• Creation of a unified system for managing the
risks associated with Big Data products
BIG DATA TRANSFORMATION PILLARS AND PREREQUISITES
*Source: Gartner, Key Trends in Analytics, Big Data and Data Science, 2014
• The most important issue with big data is whether it can add
significant value to the business of the telco beside its cost-
optimizing effects
• Legal risks are not perceived as the most crucial ones though
might be a showstopper for almost all profitable external
services and products

7. Purpose:
 Increase in awareness regarding benefits of
“Big Data” related approaches throughout
the top management
 Identification of most relevant use cases /
pilot project set-up
 Alignment with IT Roadmap
Activities
 Big Data use case identification
 Big Data use case description, covering data
requirements and expected benefits
 Valuation of use cases (high level) and short
list derivation
 Elaboration of Business Case for shortlisted
use cases
 Prioritization of use cases
 Set-up of big data technology roadmap for BI
Big Data Use Case Evaluation
Purpose:
 Validation of the business value of a
selected use case
Activities
 Identification of business
requirements
 Vendor assessment for technical
solution components
 Proof of concept and trial setup of the
preferred technical solution
 Execution of pilot project and
performance monitoring
 Preparation of Go/No Go decision
based on detailed analysis of pilot
results
Big Data Pilot Project
Purpose
 Development of overall Big Data strategy
and implementation plan to fully
leverage the benefits of Big Data
Activities
 Develop the vision, targets, target
segments, technology architecture and
roadmap
 Optional: Design of a Telco Center of
Excellence for Big Data
 Optional: Design a Business Unit “Big
Data as a Service”
 Adaptation of organization and relevant
processes to Big Data logic
 Run RfP & Vendor selection for Big Data
technical solution
 Plan technical integration
 Launch & operational support
Full Implementation & Launch
STEPS TO BUILD BIG DATA CAPABILITY IN TELCO
Small steps. Pilot projects. Proof of concept and proof of value. Formulation of the strategy
Board approval of the strategy and CAPEX. Execution stage

8. A ZOO OF SOLUTIONS TO CONSTRUCT THE ARCHITECTURE
Knowledge
Build Up
What is the best way to control the
new technology ?
 Build your own knowledge base and
experience
 Hire external knowledge/consultants
Analytical
Processing
How can analytical tools handle the
necessary amount of big data ?
 Look for solution on the market
 Write your own code
Access to
Source
Data
How can you get access to data
on mission critical systems (e.g.
HLR) ?
 Manage risk
 Convince system owners
Big Data
Platform
Selection
Go with traditional BI tools or with
new open source driven platform ?
 Reliability and maturity of solutions
 Cost of solutions
 New capabilities
• Big Data is a multitude of critical blocks that together
transform into a high-performance monolith.
• Each such block is provided by a host of companies
and solution options you can pick from

9. • Big Data Technology
Source: BITKOM, Big Data
technologies - Knowledge
for decision makers, 2014
Data Management and Storage
Data Integration
Visualization
Analytical Processing
Data Manipulation
Data
Connectivity
Data Ingestion
Dashboards
Advanced
Visualization
Real-time
Intelligence
Video /
Audio
Geospatial Web
Text
Semantics
Predictive
Data
Mining
Machine
Learning
Reporting
Batch
Processing
Streaming
& CEP
Search &
Discovery
Query
Hadoop
Distributed
File System
NoSQL
Databases
In-Memory
Databases
Analytics
Databases
(DW, etc.)
Transactional
Databases
(OLTP)
Data analysis - be it big or
small - needs a set of
functional modules to do
its work.
FUNCTIONAL COMPONENTS OF BIG DATA ARCHITECTURE
Data Governance & Security
Identity & Access
Management
Data Encryption
Multi-client
Ability
Governance

10. SAMPLE MODULAR DESIGN OF BIG DATA ARCHITECTURE AND CAPEX ISSUES
Large portion of CAPEX might be
consumed to get data prepared for
further ingestion and processing,
e.g. preciseness of geospatial data,
web traces inspection, structuring
internal unstructured data.
Security aspects of Big
Data require huge
amount of CAPEX

11. A thorough PEST analysis should precede any Big Data development with an external
component
 Is data privacy a high profile political concern?
 What is the regulatory framework?
 Which data types are protected?
 How long can I store data?
 Who can I share it with?
 How do I need to protect it?
 How will the general public react to our business model?
 What has to be expected in terms of press coverage?
 How will privacy interest groups react?
 Who are my competitors in the Telco industry / in other
industries?
 Which advantages / disadvantages may their business
model have over mine?
 Which competitors have the highest potential to create
synergies through partnering?
 Whom do I need to partner with to gain a competitive
advantage?
 How can I connect to these partners? (APIs)
Political Economic
Social Technological
BIG DATA PEST MATRIX
44%*
30%
70%
33%
Never or very
rarely share
their personal
data
Would be unhappy if their
personal data were shared with
third companies
Would agree to a company
using their personal data for
more relevant marketing
Would agree to a company
using their personal data
for the development of
new products and services
*Source:
Ernst&Young,
Big Data
Backlash, 2013
Since telcos operate under strict regulatory rules any
unauthorized personal data usage might be prohibited.
Moreover, the negative publicity around such Big Data
products may heavily overweigh its positive outcomes
Consumers in many countries are not ready
to embrace processing of their personal data

12. WHAT ARE THE MAIN RISKS TELCOS MIGHT FACE? SAMPLE BIG DATA RISK MAP
Legal
risks/Data
privacy
Fierce
Competition
with OTT
Lack of
scale/Lack of
demand
Competition
with other
telcos
Fail to deliver
products and
services
Small range of
external
products
Fail to
construct an
appropriate
architecture
Fail to gain
public support
Fail to gain
support from
the regulation
Devaluation of
the data and
poor profits
Poor execution
Lack of
scalability
devaluating
future revenues
Early price
erosion of
data
There at least 4 highly probable
showstoppers according to the Big
Data Risk Map
- Competition with other telcos
Since all the telcos have quite similar sample of
data it will be difficult to differentiate services
and products that will result in price damping
- Fierce Competition with OTT
OTT players can compete in many ways with
telcos. Combined they have comparably huge
amount of data at considerably lower prices
(LocateIt)
- Legal risks/Data privacy
Threat of data leakage can have a very dangerous
outcomes since telcos operate under regulatory
set of rules. Moreover, many telcos are not
allowed to process data except for purposes
formulated by law
- Fail to gain public support
The right and beforehand publicity of the
services with positive externalies is absolutely
the must. In Britain, geospatial service
Smartsteps from O2 was boycotted while the T-
Mobile’s MotionLogic gained support and is still
active thanks to its right prepositioning and PR

13. Thank you!
Should you have any questions please reach me out @
Parviz.Iskhakov@gmail.com