heavy, messy, misleading: why big data is a human problem, not a tech one
DESCRIPTION
"Big data" has been around for a few years now but for every hundred people talking about it there’s probably only one actually doing it. As a result Big Data has become the preferred vehicle for inflated expectations and misguided strategy. As always, the seed of the issue is in the expression itself. Big Data is not so much about a quality of the data or the tools to mine it, it’s about a new approach to product, policy or business strategy design. And that’s way harder and trickier to implement than any new technology stack. In this talk we look at where Big Data is going, what are the real opportunities, limitations and dangers and what can we do to stop talking about it and start doing it today.TRANSCRIPT
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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