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Mac201 big data
The document discusses the rise of big data and its applications and limitations. It notes that while big data allows unprecedented tracking of digital behaviors, incorrect assumptions can lead to big errors. Proper theory and statistical modeling are still important to make sense of large datasets and signals amidst noise. Overall, big data offers new insights but also risks if not approached carefully.
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Mac201 big data
- 1. BIG DATA #mac201
- 4. 100 petabytes of data
- 5. 1 petabyte = 1000 terrabytes
- 6. 1 terrabyte = 1000 gigabytes
- 7. 100 petabytes = 100,000,000 gigabytes
- 12. Theory free? ‘Google’s engineers didn’t bother to develop a hypothesis about what search terms – “flu symptoms” or “pharmacies near me” – might be correlated with the spread of the disease itself. The Google team just took their top 50 million search terms and let the algorithms do the work.’ -‐ HarLord, 2014
- 14. IdenOfied key data points associated with pregnancy Hit enough indicators = receive vouchers
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- 17. As our communicaOon, leisure and commerce have moved to the internet and the internet has moved into our phones, our cars and even our glasses, life can be recorded and quanOfied in a way that would have been hard to imagine just a decade ago (Harford, 2014)
- 18. 4 big claims: 1. That data analysis produces uncannily accurate results; 2. That every single data point can be captured, making old staOsOcal sampling techniques obsolete; 3. That it is unnecessary to concentrate upon what causes what, because staOsOcal correlaOon tells us what we need to know; 4. That scienOfic or staOsOcal models aren’t needed
- 19. This is a world where massive amounts of data and applied mathemaOcs replace every other tool that might be brought to bear. Out with every theory of human behavior, from linguisOcs to sociology. Forget taxonomy, ontology, and psychology. Who knows why people do what they do? The point is they do it, and we can track and measure it with unprecedented fidelity. With enough data, the numbers speak for themselves. -‐ Anderson, 2008
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- 21. Remington Rand UNIVAC Cost $1m in 1952 $8m inflaOon adjusted
- 22.
- 23. Predicted: 438 to 93 Actual: 442 to 89
- 25. Richard Whihaker, Senior Vice President at SAP Labs
- 26. The amount of likes on Facebook can predict a screening's chance of selling out, allowing film marketers to focus their ahenOon on gekng the word out amongst relevant demographics. Eugene Hernandez, Director of Digital Strategy at the Film Society of Lincoln Center
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- 37. "With the purchase of series, we look at what does well on piracy sites … Prison Break is excepOonally popular on piracy sites. Kelly Merryman, content acquisiOon exec at NeLlix, 2013
- 42. “in large data sets, large deviaOons are vastly more ahributable to variance (or noise) than to informaOon (or signal)” Taleb, 2013
- 44. Summary • Big data is being employed to track digital media users and their behaviours • Offers new insights into real world pracOces and preferences • Incorrect or theory free assumpOons can mean that big data = big errors
- 45. Images CC • JD Hancock (2012) Big Data • Michael Donovan (2009) Large Hadron Collider • Torkild Retvedt (2009) Server room at CERN • Carlos Luna (2008) Google • Peter Kirkeskov Rasmussen (2014) Social Media • David Telford (2011) 082/365 man-‐flu! • Mike Mozart (2014) Target • R2hox (2013) data.path Ryoji.Ikeda – 4 • Sean MacEntee (2010) social media • Kevin P Trovini (2014) Google Glass (Red) • Sean MacEntee (2014) data is oil • Charis Tsevis (2012) I Like Facebook • R2hox (2013) data.path Ryoji.Ikeda -‐ 3