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Francesco D’Orazio, @abc3d VP Product, PulsarPlatform.com
Heavy, Messy, Misleading Why Big Data is a human problem, not a technology one
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Every talk about Big Data should start with Twin Peaks
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There’s more to big data than the technology behind it.
And the best way to find out what it is, is to start from the
metaphors of Big Data
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stream of data
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ocean of data
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river of data
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a data leak
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data firehose
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data flood
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data tsunami
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data “is” fluid
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data “is” huge
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data “is” powerful
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data “is” unpredictable
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data “is” uncontrollable
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Data is the new oil(?!)
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We are not going to war for it (yet)
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Data is not a scarce resource
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The abundance of data is the result of the instrumentalisation
of the natural, industrial and social worlds
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The Large Hadron Collider can record up to 40 million particle interactions per second
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The Square Kilometer Array will collect data from the deep space dating back to more than 13 billion years ago
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Wolfram Data Science on Facebook Data: how our topics of discussion change by age and gender
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Carna Botnet: in just 60 seconds nearly 640 Terabytes of IP data is tranferred across the globe via the Internet
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Machine Sensing
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The sensors on the new Airbus 380 generate 10 terabytes of data every 30 minutes. That’s 120T every LDN-NYC flight
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And yet, another reason why data is not the new oil is that we are
not actually doing it much…
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99.5% Percentage of newly created digital data
that’s never analysed
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But that’s not strictly true either…
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0.5% Percentage of newly created digital data
that’s actually being used
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higher % of teenagers having sex vs
% of new data being analysed
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Credit Scores have replaced the handshake with the bank manager
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Fair and Isaac came along in 1956. Today they crunch around 10 billion scores each year
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Buying advertising used to be about smiles and jokes over Martini lunches
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Now it looks more like this…
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11 seconds of trading for the FB shares. Already in 2006 one third of all transactions in EU and US was algorithmic
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Walmart handles more than 1M customer transactions per hour, all affected by price elasticity
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Price Discrimination based on log in info, browser history, device, A/B testing is common practice for most online retailers
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75% of the content Netflix serves is chosen based on a Netflix recommendation
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At Buzzfeed every item of content has its own dashboard showing how it spreads form ‘seed views’ to ‘social views’ and by what ‘viral lift’
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Upworthy
Systematic experimentation: 15% of the top 10.000 websites uses A/B testing
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Crowdpac matches candidates and funders based on analysis of public speeches, contribution and other sources of public data on the candidate
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LAPD run a pilot to predict where a crime is going to happen next (‘crime aftershocks’) based on 13 million crimes over 80 years
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The Dubai police is equipping officers with Google Glass enabled with face recognition to identify potential wanted criminals
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Systematic experimentation: 15% of the top 10.000 websites uses A/B testing
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LinkedIn had a student problem: so they re-arranged the data they already have for a student audience
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99.5% Why then are we throwing away this
much data?
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We are still learning to recognize problems as
data-problems
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Big Data changes the very definition of how we produce knowledge
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Less > More Exact > Messy
Causation > Correlation
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Significant correlation requires scale. And scale is hard to handle.
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DNA research is a case in point: DNA data is hard to manipulate and there’s not enough sequenced DNA available to establish significant patterns
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Big Data comes with Big Errors
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Data is rarely normalised.
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Data is siloed and not verifiable.
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Big does not equal whole.
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Big does not equal representative.
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Data doesn’t speak for itself. We speak for it.
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Big Data is still biased and the result of interpretation.
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Correlation doesn’t imply causality.
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Models are often too simple and not peer-reviewed.
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Context is hard to interpret at scale. Traditional Qual & Quant
have to work with big data.
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3 billion queries/day 50 million top keywords identified 5 years of data on flu spread matched Overestimates by 50% Didn’t predict pandemics
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Big Data also means a big
new digital divide.
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Accessible doesn’t mean ethical.
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The problems slowing down the adoption of Big Data
are human problems
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And that’s because the biggest innovation in Big
Data is a human innovation
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An innovation in decision-making: framing,
solving and actioning a problem
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“Data is just like crude. It’s valuable, but if unrefined it
cannot really be used. It has to be changed into gas, plastic, chemicals, etc., to create a valuable entity that drives
profitable activity; so must data be broken down, analyzed for it
to have value” Michael Palmer
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The opportunity in Big Data is data middleware: turning
crude into gas, plastic, chemicals
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But until we invent the new plastic, the new gas, the new chemicals, we are stuck with the smokescreen. Or even the smoke monster.
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Francesco D’Orazio, @abc3d VP Product, PulsarPlatform.com
Thank You