Mike Sherman Amsterdam, October 22, 2014 Copyright 2014 Mike Sherman, no use allowed without explicit written permission DataMediary (Consumer Data Mall) – A Consumer Driven Big Data Concept
2014 10 22 broadband world forum mike sherman
Jun 14, 2015
My take on the issues facing big data and two solutions to addressing these issues: one required and the other disruptive
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Mike ShermanAmsterdam, October 22, 2014
Copyright 2014 Mike Sherman, no use allowed without explicit written permission
DataMediary (Consumer Data Mall) – A Consumer Driven Big Data Concept
2
KISS always applies
Copyright 2014 Mike Sherman, no use allowed without explicit written permission
3
Big Data Defined
Big data is like High School 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 itCopyright 2014 Mike Sherman, no use allowed without explicit written permission
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More Variety
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More Volume
Billing Data (old) CDR (call data records: old
but can store more now App usage (if on network,
new) Location (new) Sensors, e.g.
accelerometer, gyroscope (new)
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IT is helping
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Big Data: Overhyped!
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Source: DMA, Big Data and one to one marketing, 12 myths busted, August, 2014
Myth: Big Data is a Strategy
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Source: DMA, Big Data and one to one marketing, 12 myths busted, August, 2014
Myth: Big Data Provides A Competitive Advantage
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Source: DMA, Big Data and one to one marketing, 12 myths busted, August, 2014
Myth: More Data = More Insight
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15Copyright 2014 Mike Sherman, no use allowed without explicit written permission
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Sometimes Less Data Is Better
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Big Data isn’t Pacman, you can’t gobble data and spit out the answer
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Lots of Overpromise
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Source: What the Failed $1M Netflix Prize Says About Business Advice – Forbes April 16, 2012
It made for great “the future of business” fodder. A technology company crowdsources its R&D by ponying up $1,000,000 to anyone who can improve their
recommendation engine. Netflix, stickler for user-experience as it is, was willing to pay all this money even if it meant making their algorithm just 10% better. Amazing! If you were looking for an anecdote to cheerlead Web 2.0 to, this was almost too good to be true.
And in fact, it was. Despite all the plaudits and case studies, Netflix announced this
week that despite paying $1 million dollars to a winning team of
multinational researchers in 2009, they never bothered to implement their solution. Why? Because, according to Netflix the “additional accuracy gains that we measured did not seem to justify the engineering effort needed to bring them into a production environment.”
In other words, all the experts telling businesses to follow in Netflix’s crowdsourcing footsteps were shouting bogus advice all over the internet.
More failures than successes
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Failed PersonalizationSelling gas to a bus rider?
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Failed Personalization:Selling flights 6 months too late?
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End Users / Senior Management
Data, IT, Data Scientists
Issue: Communication Gap
αιτήματος Ανάλυση Έξοδος Ανάλυση
정보 요구 정보 가 필요합니다
??? ???
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End Users / Senior Management
Translator/Integrator Role Required
(Analysis Request) (Analysis Output)
Translator/Integrator
(Information needs) (Information required)
αιτήματος Ανάλυση Έξοδος Ανάλυση
정보 요구 정보 가 필요합니다
Data, IT, Data Scientists
24Copyright 2014 Mike Sherman, no use allowed without explicit written permission
The Consumer Data Mall
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Data Mall
Data
Scores
Relevant Offers
Feedback
Dialogue, not Monologue Permission based, not
passive Win-Win
Marketers
Efficiency and Effective-ness)
Consumers Service, not
sales Engagement,
not annoyance
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Consumer Data Mall: Anchor Tenants
Retailer(s)
TelcoTransaction processor
Category exclusivity
Bring extensive data and customers
Benefit by expanding their share of market
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Consumer Data Mall: Other Tenants
Non-exclusive
Bring more data
Use data output (marketing scores)
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Consumer Data Mall: Kiosks
Kiosks
Non-exclusive
Mainly local companies
Use data, contribute little
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Consumer Data Mall: Consumers
Opt-in
Share data
Receive PERSONALIZED messages and offers
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Yes, Big Data exists: there is more data, more types of data, faster access to data and perhaps, more accurate data (the 3-4 V’s)
Yes, there are new hardware and software solutions that allow us to collect, access, manipulate and analyze that data
No, that alone won’t solve our problems What else do we need?
Translators/Integrators? Consumer Data Mall?
Key Points
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Thank You!Questions?
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Consumer Data Mall
Kiosk
Copyright 2014 Mike Sherman, no use allowed without explicit written permission
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