Big Data Monetisation
PSD were pleased to host a breakfast at the Royal Horseguards Hotel discussing the subject of what companies can do with their data to monetise it and bring the debate to the CEO's office.
Leading the discussion, and presenting his portfolio of work in this area was Mike Fishwick.
Mike has recently led the Business Insights programme at Telefonica Digital, and has an almost unique viewpoint on the application of data science in this area.
Attending were technology leaders from a broad range of sectors all of whom are investigating what they do with the ever increasing torrent of data that they are managing.
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Chris Eldridge - MD
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
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
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
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
15.
16.
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
23.
24. Big Data 2013
Contact:
Chris Eldridge MD (PSD)
Chris.Eldridge@psdgroup.com
Tel: 0207 970 9700
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