This document discusses big data and its hype and realities. It begins by looking at where the hype around big data originated from, including Google and Yahoo's early work and a 2011 McKinsey report. It then defines big data and common misconceptions. The document explores the sources of big data, such as social media, web logs, network data, M2M sensor data, and transaction data. It outlines examples of internal and external business uses of big data and different business models. However, it also notes privacy remains an issue and that the industry is still learning how to appropriately use big data.

1. Dr. Richard Benjamins
Group Director BI & Big Data
Telefonica
Big Data – From Hype to Reality
Telefonica
© 2014

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3. Xavi: movements faster than 5 m/sec
Time at each position sec.
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Messi movements faster than 5 m/sec

4. What’s the big deal with Big Data?
Big Big
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McKinsey
Big
Data
McKinsey
Big
Deals

5. Big Data is a hype
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6. 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!
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2004: Google
publishes Map
Reduce paper
(link: here)
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)

7. But what is Big Data?
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Dave Feinleib, Forbes blog
1. Big Data is Only About Massive Data Volume
2. Big Data Means Hadoop
3. Big Data Means Unstructured Data
4. Big Data is for Social Media Feeds and Sentiment
Analysis
5. NoSQL means No SQL

8. Where is Big Data coming from?
Type of Big
Data
OTT/Telco Cost of data
collection
By product/
seeking
Batch/real-
time
Differential?
Social media OTT Low Active Both No
Web logs Both Low Passive Both No
Network data Telco High Passive Both Yes
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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

9. PI EconomyExamples of external useInternal use
Several business opportunities with (big) data
Different “business” models with different maturities and different risks
Data = improved
business
Data = risk =
business
Data = business
Data = better
advertising
Improve
services
Advertising Access to
insights
Become a
gatekeeper of
personal data
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Leverage data to
understand and
improve business
(x/up sell, churn)
and products
Recognize that
digital data is
delicate (privacy)
Turn that into an
opportunity
Insights that help
improve
businesses and
governments
Leverage data for
targeting users
with relevant ads
and higher CTR
and conversion
M2M
Smart cities

10. Privacy remains an issue
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11. Are you aware where your data is going?
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12. The industry is learning by doing
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13. External Monetisation - Smart steps for retailers
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What are the profiles of the
people in the area of my store?
How does the footfall in our
area change throughout the
day?

15. Organizadores Sponsor platinum
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Sponsor Gold Con el apoyo de Socio tecnológico
Nicolas Moulard, Director de Actuonda
moulard@actuonda.com
Tel : +34 699 248 200
@Radio_20 www.bigmediaconnect.es