Big Data – From Hype to Reality
Dr. Richard Benjamins
Group Director BI & Big Data
Telefonica
Telefonica
© 2014
Overview
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•
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Telefonica
What & Why of Big Data
Opportunities of Big Data
Privacy challenge
Example application: Smart Steps
2
What’s the big deal with Big Data?
Big
Deals
Big
Data
McKinsey
McKinsey
Telefonica
3
Big Data is a hype
Telefonica
4
But what is Big Data?
Dave Feinleib, Forbes blog
1.
2.
3.
4.
5.
Big Data is Only About Massive Data Volume
Big Data Means Hadoop
Big Data Means Unstructured Data
Big Data is for Social Media Feeds and Sentiment
Analysis
NoSQL means No SQL
Telefonica
5
Where does all the hype come from?
Today, huge marketing budgets are
being thrown at those two words,
driven by new business… no wonder
all the noise!
2004: Google
publishes Map
Reduce paper
(link: here)
Telefonica
2006: Yahoo’s
Doug Cutting
open sources
Hadoop out of
his older search
engine project
Nutch.
(Link: here)
2011: McKinsey
Global Institute
publishes report
on Big Data’s
market potential
for business,
reaching out of
the tech. world
(link: here)
6
Where is Big Data coming from?
Type of Big
Data
OTT/Telco
Cost of data
collection
By product/
seeking
Batch/realtime
Differential?
Social media
OTT
Low
Active
Both
No
Web logs
Both
Low
Passive
Both
No
Network data
(telco)
Telco
High
Passive
Both
Yes
M2M (sensor)
data
Both
High
Active
Both
Might
Open data
OTT
Low
Both
Batch
No
Transact. data
Both
Medium
Passive
Both
No
Telefonica
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Several business opportunities with (big) data
Different “business” models with different maturities and different risks
Internal use
Improve
services
Examples of external use
Access to
insights
Advertising
PI Economy
Become a
gatekeeper of
personal data
Data = improved
business
Data = better
advertising
Data = business
Data = risk =
business
Leverage data to
understand and
improve business
(x/up sell, churn)
and products
Leverage data for
targeting users
with relevant ads
and higher CTR
and conversion
Insights that help
improve
businesses and
governments
Recognize that
digital data is
delicate (privacy)
Turn that into an
opportunity
M2M
Smart cities
Telefonica
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But Big Data is also good for society and
environment
H1N1 virus pandemic
2012 Earthquake in Mexico
Telefonica used mobile data to measure the spread
of a global epidemic (“swine flu”) in Mexico DF
Dimensioning emergency services in advance for an optimal
response to natural disaster situations
After the magnitude 7.4 earthquake in Mexico DF, Telefonica
researchers captured modile data records that once anonymized and
aggregated allowed building visualizations of the density of calls in
the differents part of the city, immediately depicting the areas most
affected by the earthquake.
With Big Data tools like this, it would be possible for authorities to
better anticipate contingency plans, dimensioning emergency
services and placing them in those points where there is evidence
that will be mostly needed in case of catastrophic events.
To understand more about human mobility and the spread of
epidemics through society, Telefónica Digital’s research team
used anonymised and aggregated mobile phone call records to
measure numbers of people visiting locations such as airports
or universities.
The study found successful Mexican Government’s decision
to shut down key infrastructures, reducing virus
propagation by 10%.
(Click images for more)
Telefonica
(Click images for more)
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Privacy remains an issue
Telefonica
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There is increasing awareness of what customer data
companies store
Telefonica
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The industry is learning by doing
Telefonica
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Are you aware where your data is going?
Telefonica
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To the US …
Europe’s leading analytics companies call upon European
Institutions to replace Google Analytics
Telefonica
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Smart steps, for retailers
Big decisions…..
....made better
1st product – “Smart Steps” for Retailers:
• Decide on store location
• Understanding store performance vs footfall
• Plan local marketing campaigns and track their impact
• Optimise resource planning – staffing/open hours
Telefonica
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Retailers worry about …
Retailers have questions...
Where should I target loyalty
or acquisition marketing
campaigns? Where are my
customers coming from?
Where should I locate
my new store?
I need to manage my resources.
When are my peak times? Could I be
operationally more effective if I
changed my opening times?
I am a large supermarket
owner and one of my
competitors has opened up
down the road. I need to
identify our battleground.
Where is my competitor
strongest and weakest?
Strategic
Decisions
Telefonica
I know the activity that
goes on inside my stores.
But what % of my target
market is walking past
outside? What is the
opportunity that I am
missing?
Performance
Management
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Case study with 4th largest UK
food retailer
400 stores nation wide
Crawford Davidson: Customer Director at Morrisons
Supermarkets:
“Unlike some of our competitors, we don’t have a store
card to tell us who our customers are, and how they shop
our stores, which means we’re at a disadvantage in
targeted marketing. Over-rewarding one loyal customer
disadvantages us in investing in the next”
“This increase in customers was achieved
without any reduction in customer spend,
“Smart Steps identified many more
and with an improved new customer
suitable target post code sectors,
activation rate. Overall there was a 150%
enabling us to send promotional
increase in the amount of new or
coupons to double the number of
reactivated customers who visited
households”
Morrisons stores. This is a fantastic
result.”
Telefonica
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NETWORK DATA
The o2 mobile network has hundreds of cells to measure the trends in
footfall across the country
2G Network
3G Network
900 MHz
1800 MHz
39
%
2100 MHz
2013 4G Network
Telefonica
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PRIVACY
A 3 step process
ANONYMISATION
Before Telefonica Dynamic Insights (TDI) receives the data, all
personal information is removed. The data TDI receives are
cryptographically hashed values
AGGREGATION
The hashed values are aggregated into groups, i.e. gender & age
band. At this stage there are only crowds of o2 customers
EXTRAPOLATION
We take our sample and extrapolate to population totals, using
mathematical algorithms. This gives us the grouped values Smart
Steps uses.
Telefonica
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200 x 200 GRID
Footfall is rendered into 200 x 200 metre grid squares across the country
Easier to use
Further protecting
anonymity
Extrapolated to
represent local
population
39
%
Telefonica
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Example question of a marketer
COUNT
How does the footfall in our
Whatchange
are thethroughout
profiles of the
area
the
people in theday?
area of my store?
Telefonica
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Differential aspect
Vast sample base
based on observed
crowd behaviour
Intuitive web tool
covering the whole of
the UK to draw insights
from
Today’s data tomorrow.
Fastest data delivery in
the market
Eliminates retailers’
blind spots. The profile
of the footfall in their
area
Insights 24/7/365. Data
every hour, day, week
and month. You choose.
Export data and
combine with other
sources
Telefonica
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And what about the Semantic Web and Data?
Telefonica
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Semantic web and data trends
Telefonica
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Semantic Web and Gartner’s Hype Cycles
Telefonica
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2006 – 5 to 10 years for reaching mainstream
Telefonica
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2009 – more than 10 years to go
Telefonica
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2012 – more than 10 years to go
Telefonica
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