The document outlines the agenda and key discussions from a 2013 big data event held at the Royal Horseguards Hotel, led by Chris Eldridge and featuring guest speaker Mike Fishwick from Telefonica Digital. Key topics included monetizing big data, the transformation of Telefonica into an information company, and challenges related to data quality and privacy. The event emphasized the importance of internal and external monetization strategies and the role of specialized teams in driving data-related initiatives.

1. Big Data Event - 2013

2. breakfast event: Big Data 2013
The Royal Horseguards Hotel
Schedule
8:00am Welcome
8:30am  Introduction – Chris Eldridge MD (PSD)
 Guest Speaker – Mike Fishwick
(Telefonica Digital)
 Summary – Chris Eldridge
9:00am  Questions & Discussion
 Breakfast & Networking
9:45am Close

3. Making money from your data
Mike Fishwick
18th September 2013

4. Broadly covering….
 Broad business context for Telefonica
 Big Data - a few stats from mobile world
 Essential business proposition created for Telefonica
 People stuff
 Products - do they really work & can we make money
 Data Quality – A cautionary note
 Summary and final considerations

5. Going Digital…
 Telefonica Digital formed 18 months ago
 Consolidate and accelerate “non-Core” products and services to
the market
 Core Mobile telephony is commoditising
 Objective to become an information company
 Identified 3 key “information needs”
 BI unit for Tef Digital
 A global BI transformation programme across the OB’s
 Monetise our data assets

6. Food for thought….
“Big data is like teenage sex:
everyone talks about it,
nobody really knows how to do it,
everyone thinks everyone else is doing it,
so everyone claims they are doing it.”
anon

7. What is Big Data?
Volume
Variety
Velocity
From Dawn of Time to 2003 5 Exabytes were created; Now generated every two days

9. 90% of mobile users keep their mobile <1 metre, 24/7

10. Monetising your Data
 Monetisation can take two major routes
 Internal monetisation - Customer and Operational effectiveness
 External monetisation
 Internal Monetisation
 Suggest it becomes the remit of the BI team that you have internally
 They have the technical skills to lead
 They have the science skills to analyse
 They have the commercial skills to interpret
 External monetisation
 Don’t mix it up with the internal activity
 Create it as a separate function to drive commercialisation
 Make it a part of your digital business plans

11. The Business Model....

12. Privacy is a vital to the approach
• Anonymised
• Aggregated
• Extrapolated
&
• Dispersed

13. Organisational Design
 A business unit with full P&L NOT sales
 Recruitment of industry specialists – retail first – to blend in
with the existing product technologists
 Investment in Data engineers and Data scientists – NOT
network engineers
 Building a skunkworks or lab function and NOT being afraid to
seek direct customer feedback as we formulate the product
 Sell with specialists NOT the Telco sales team
 Augment with partners NOT We can do it ourselves mentality

14. 360°Retailer view – Retail Product
vision
Catchment
Location footfalls
In-Store Ffalls
Offline Checkouts
Online Checkouts
Geo-located
Online Visits
of product pages
Geo-graphical mapping
of product
Demand based on
Online Logs
Intra Store Ffalls
What our customers
already know
What we can tell
Our customers

17. JetSetMe – Product Overview
1. Consumer opts-
in (one-time) using
mobile number
Merchant
2. Consumer
makes purchase
at merchant with
card
3.
Authorisation
request
5. Approved
or Declined
4. Real time risk analysis performed with
addition of TEF customer geo-location data
Location updates
sent to bank fraud
system
6. Approved
or Declined

18. Product Roadmap
Aggregated data from
third parties – CRA for unbanked
subprime
TDI Historical data – Credit
Scoring
Personal data – Identifty Provider
(IDP)
Number reputation - Geolocation
ID&F
Datatypes/capability
JSM
Mexico
Germany
Ireland
Spain
Brazil
UK
JetSetMe is the first building block in an ecosystem of solutions that use mobile data
to address identity related opportunities. JSM addresses a global fraud opportunity
estimated to be in excess of € 6 billion.

19. Value proposition – (Card Present abroad)
• Costs of processing
flagged/blocked transactions
• Costs related to call center
(pre travel, to un-block)
• Number of transactions
• Avg. transaction value
• Transaction fee
• Amount of detected fraud
transactions
• Avg. transaction value
• Number of False / + declines
• Avg. declined value
• Transaction fee
Saved operation costs
Revenues from additional
card usage
Increased fraud detection
savings
Revenues from reduced
transaction declines
Value generated KPIs ImpactedFraud Losses
Globally ~€ 6.16 bn
136
DOMESTIC ABROAD
CNPCP
Fraud loss
UK/DE/ES ~ € 760M
1%
6%
81%
12%
Total value to trial client modelled at Multi millions pa

20. Data Quality
 A deep and challenging topic BUT in summary the following are just some of the
issues my team came across and had to address….
 The issues with data quality are NOT TO BE TRIVIALISED if one is going
beyond the network operation to one of using DATA for DATA PRODUCT
development.
 Issues relate to how the network behaves under load
 Issues of down time for maintenance.
 Referential integrity because the network engineers change the topology (and don’t tell us!)
 General failure of network devices like probes
 All this means that understanding the data is critically important
 What event types do we use
 How do we model these network behaviours
 How to deal with data losses – time series/trending requires this
 Locational Precision
 effective aggregation
 Land use and where not to put people

21. How was it all achieved?
 Dealing with the existing business model
 Taking a different view about organisation
 Examining the leadership model
 Throwing away the take to market model
 Leveraging new technologies (All cloud & Open source Based)
 Taking managed risks

22. Summary
 Moving from the unknown unknown’s to the “we now know what we don’t know”
 Investing time in understanding the source data – go beyond just enough
 Product architecture is key to agility
 Data architecture is key to flexibility
 Be clear about the debate between product and platform as the proposition
 Don’t let the Legacy mentality take over
 Get someone in who’s hand-produced the T shirt

24. Big Data 2013
Contact:
Chris Eldridge MD (PSD)
Chris.Eldridge@psdgroup.com
Tel: 0207 970 9700
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